There’s a major shift happening in the world of data analysis. IBM’s 2022 Global AI Adoption Index found that 35% of companies worldwide are currently using AI, and an additional 42% reported they’re exploring it. Data analysis is a prime target for this AI infiltration, and it carries profound implications for the present and the future of the industry.

Artificial intelligence is shifting the data analysis paradigm not by removing the human involvement, but by amplifying human potential. It’s carving out a niche where mundane tasks get automated and the intricate, creative problem solving becomes the sole domain of human analysts. It’s not a tale of man versus machine but rather, a promising partnership where each player plays to their strengths. 

This is the new frontier, and this article offers a map. Here, we’ll explore the implications of AI on data analytics, how it’s reshaping job roles, and the exciting future it holds. It will also provide a guide for data analysts to navigate these transformative times and emerge equipped for the challenges and opportunities of tomorrow.

The Impact of AI on Data Analysis

Artificial Intelligence and machine learning have become synonymous with innovation in data analysis. Their potential to streamline processes and unearth hidden patterns in data sets is transforming the way analysts work.

One of the primary areas where AI is making a significant impact is in data preparation. Data analysis typically begins with collecting, cleaning, and categorizing data — tasks that can be painstakingly slow and tedious. AI, however, is capable of automating much of this process. Machine learning algorithms can handle vast amounts of data and clean it at a pace that would be impossible for a human analyst. This level of automation removes a substantial burden from data analysts, allowing them to concentrate more on extracting valuable insights from the data.

AI also enables enhanced decision-making by providing AI-powered insights. Traditionally, data analysts would generate reports and make predictions based on historical data. While this approach has its merits, it’s often time-consuming and requires a high degree of expertise. AI simplifies this process by employing advanced algorithms and predictive models to deliver insights quickly and accurately. This capability of AI to process data in real time and predict trends makes it an indispensable tool in the decision-making process.

AI is also transforming the way forecasting is done. Traditional statistical methods of forecasting can often be complex and fall short when dealing with volatile markets or unpredictable scenarios. AI, with its ability to adapt and learn from new data, can deliver more accurate forecasts. Machine learning models can analyze and learn from past data patterns to make predictions about future trends, making them increasingly reliable as they consume more data.

In essence, the impact of AI on data analysis is a shift in focus. The role of data analysts is moving away from mundane, time-consuming tasks and toward more strategic, insightful work. The advent of AI is freeing data analysts from the shackles of tedious data preparation and arduous trend analysis, enabling them to do what they do best: deliver insights that drive strategic decision making.

How AI is Changing the Job of Data Analysts

AI’s transformation of data analysis isn’t just about efficiency — it’s also shifting the nature of the data analyst role. While automation handles the grunt work of data management and basic processing, data analysts find their responsibilities pivoting toward more complex tasks that computers can’t handle — at least, not yet. 

Take the example of a data analyst in a retail company. Traditionally, they would spend substantial time collecting and cleaning sales data, followed by time-intensive trend analysis to forecast future sales or understand past performance. The introduction of AI into this process, however, changes the game. AI can automate data collection and cleaning, rapidly process vast amounts of sales data, and even provide preliminary analysis and forecasting. 

So, what does the data analyst do in this AI-enhanced scenario? They evolve into a more strategic role. Rather than getting buried in the numbers, the analyst can now focus on understanding the “why” behind the data. They can investigate why certain trends are emerging, delve deeper into anomalies, and make strategic recommendations based on their findings. Their role becomes less about producing data and more about interpreting and applying it in a meaningful way. They can also spend more time communicating their insights, influencing decisions, and driving the company’s strategy.

It’s a shift from a purely technical role to a hybrid one, combining technical expertise with strategic thinking and communication skills. This evolution doesn’t lessen the importance of data analysts — in fact, it increases it. They become the bridge between the raw data that AI can process and the strategic insights that businesses need to thrive. They are the ones who can ask the right questions, interpret AI’s outputs, and turn data into actionable strategies. 

The Future of Data Analysis with AI

Peering into the future of data analysis, the role of AI becomes ever more significant. This doesn’t mean that data analysts will become obsolete. Rather, their role will continue to evolve, and they’ll work in tandem with AI to drive better decision making and generate deeper insights. 

AI and machine learning are projected to get more sophisticated with time, becoming capable of handling even more complex tasks. With advancements in technologies like natural language processing and deep learning, AI will be able to understand and analyze unstructured data such as images, text, and even human emotions more effectively. 

This could lead to a future where data analysts don’t just analyze numerical data but also explore non-traditional data sources. For example, analyzing social media sentiment or customer reviews could become as routine as studying sales data. Data analysts may find themselves not only interpreting AI-generated insights from these diverse sources but also guiding the AI’s learning process by asking the right questions.

Moreover, as AI models become more robust and sophisticated, they’ll be able to make more accurate predictions. Machine learning models that can predict market trends or customer behaviors will become more reliable. Data analysts in this future scenario will play a key role in verifying these predictions, understanding their implications, and turning them into actionable strategies.

The picture that emerges, therefore, is not one of AI replacing data analysts but rather, a world where data analysts leverage AI to do their jobs better. In this future, the role of a data analyst will be to harness the power of AI while also understanding its limitations.

Preparing for the AI Revolution

With the undeniable influence of AI on the horizon, data analysts should gear up to navigate this evolving landscape. Adapting to this change doesn’t just mean learning to work with AI; it’s about adopting a new mindset and acquiring new skills.

The need for a strong foundation in data analysis — understanding data structures, statistical methods, and analysis tools — remains essential. However, with AI handling much of the routine data processing, analysts must also focus on developing skills that AI can’t replicate.

Strategic thinking and problem-solving skills are set to be more important than ever. As the role of a data analyst evolves towards interpreting AI’s outputs and applying them in a meaningful way, the ability to think critically and solve complex problems will become vital.

Communication skills, too, will be increasingly important. As data analysts shift towards a more strategic role, they’ll need to effectively communicate their insights to decision makers. The ability to tell a story with data, to make it compelling and actionable, will be a key skill in the AI-enhanced landscape of data analysis.

Furthermore, it’s essential for data analysts to have a basic understanding of AI and machine learning. They don’t necessarily need to be AI experts, but understanding how AI works, its potential, and its limitations, can enable them to better integrate it into their work. Knowing how to work with AI tools, guide their learning process, and interpret their outputs can be beneficial.

Finally, adaptability and continuous learning will be crucial. The landscape of AI and data analysis is constantly evolving, and analysts must be willing to learn and adapt. Whether it’s staying updated on the latest AI tools, learning new data analysis techniques, or improving their soft skills, a commitment to lifelong learning will be key.

Key Takeaways

As we take a step back and view this sweeping transformation, it’s clear that the integration of AI into data analysis is an exciting development. It not only automates and streamlines processes but also elevates the role of data analysts, freeing them to focus on strategic tasks that add greater value to their organizations. 

Yet, the AI revolution is not a one-time event — it’s a continuous journey of learning and adapting. And for data analysts ready to embrace this journey, the path ahead is filled with opportunities to grow professionally and make a significant impact.

And so, the call to data analysts is clear: Embrace the AI revolution, harness its potential, and continue to be the strategic anchor that turns data into actionable insights. The future of data analysis is brighter than ever, and it’s waiting to be shaped by those willing to venture into this new frontier.

This article was written with the help of AI. Can you tell which parts?

The post How Will AI Impact Data Analysis? appeared first on HackerRank Blog.

In the world of tech, the only constant is change, and this is especially true within the realm of data science. This discipline evolves at such a lightning pace that what was cutting-edge a few years ago is considered commonplace — or even antiquated — today. In fact, according to the World Economic Forum, 50% of all employees will need reskilling by 2025 as the adoption of technology increases.

As a tech leader, hiring manager, or recruiter, it’s important to not just hire for the right skills — particularly at a time when 60% of hiring managers say data science and analytics roles are the toughest to hire for. It’s also critical to continuously invest in your team’s development. It’s not about playing catch-up with the latest tech trend but about staying on the wave of evolution, ready to ride its crest. 

In 2023, upskilling your data science team isn’t just a nice-to-have but a need-to-have strategy. The benefits of this upskilling strategy are multifold: not only does it future-proof your organization but it also increases your team’s productivity, lowers turnover, and helps maintain a competitive edge in the market.

So, whether you’re hoping to dive deeper into machine learning, harness the latest in artificial intelligence, or make the most of data visualization tools, this blog post is your guide to upskilling your data science team effectively and efficiently. With a strong upskilling strategy, your data science team will be prepared to navigate the future of this exciting, fast-paced industry for years to come.

Why You Should Upskill Your Data Science Team

According to the U.S. Bureau of Labor Statistics, data science jobs are expected to grow at a rate of 36% between now and 2031 — far faster than the 5% average growth rate for all occupations. This rapid rise in demand is also creating a shortage of data science talent, making upskilling an increasingly appealing — and necessary — strategy. But its benefits extend beyond simply filling in the skills gap. 

Firstly, upskilling increases productivity. An up-to-date, well-equipped data scientist will be more efficient, better able to troubleshoot issues, and more likely to find innovative solutions. It’s simple – if your team has a better understanding of the tools at their disposal, they will be more effective at their jobs. 

Secondly, investing in your team’s growth can also have a positive impact on employee satisfaction and retention. A LinkedIn report shows that 94% of employees would stay at a company longer if it invested in their learning and development. Upskilling gives your data scientists a sense of professional progression and satisfaction, which translates to a more committed and stable team.

Lastly, but importantly, upskilling keeps you competitive. The field of data science is racing ahead, with advancements in AI, machine learning, and big data analytics becoming commonplace. Businesses not only need to keep up, but they also need to be ready to leverage these advancements. A data science team that is proficient in the latest technologies and methodologies is a huge competitive advantage.

In essence, upskilling your data science team is about more than just learning new skills. It’s about fostering a culture of continuous growth and learning, which enhances your team’s capabilities, morale, and ultimately, your organization’s bottom line.

Determining the Skills Gap

Before you can effectively upskill your data science team, you need to identify your skills gaps. This involves both a high-level overview of your team’s capabilities and a deep dive into individual competencies.

Start by reviewing your current projects and pipelines. What are the common bottlenecks? Where do the most challenges or errors occur? Answers to these questions can shed light on areas that need improvement. For instance, if your team frequently encounters difficulties with data cleaning and preprocessing, it may be beneficial to focus on upskilling in this area.

Next, look at the individual members of your team. Everyone has their own unique set of strengths and weaknesses. Some may be fantastic with algorithms but could improve their communication skills. Others might be proficient in Python but not as adept with R. You can identify these individual skill gaps through regular performance reviews, one-on-one check-ins, or even anonymous surveys. 

Remember, the goal here is not to criticize or find fault but to identify opportunities for growth. The process of determining the skills gap should be collaborative and constructive and should empower team members to take ownership of their professional development.

Once you have a clear picture of the skills gaps in your team, you can start to strategize about the most effective ways to bridge these gaps. Whether it’s through online courses, in-house training, or peer-to-peer learning, the key is to create a supportive environment that encourages continuous learning and improvement.

Key Skills to Invest in 2023

With a clear understanding of where your team stands, let’s now focus on the pivotal data science skills that your team should be honing in 2023.

• Advanced Machine Learning and AI: Machine learning and AI continue to dominate the data science field, with technologies becoming more advanced and integrated into a myriad of applications. Upskilling in areas like deep learning, reinforcement learning, neural netorks, and natural language processing can give your team a significant advantage.
• Cloud Computing: With the increasing amount of data being generated, cloud platforms like AWS, Azure, and Google Cloud are becoming increasingly essential. Cloud computing skills can enable your team to handle large datasets more efficiently and perform complex computations without heavy investment in infrastructure.
• Data Visualization: The ability to communicate complex results through intuitive visuals is crucial. Tools like Tableau, PowerBI, and Python libraries such as Matplotlib and Seaborn are continually evolving. Therefore, keeping up to date with these tools can help your team better communicate their findings and make data-driven decisions more accessible to stakeholders.
• Ethics in AI and Data Science: As AI and data science technologies become more advanced and pervasive, ethical considerations become even more critical. Understanding bias in datasets, privacy issues, and the ethical implications of AI decisions will be an important skill for the foreseeable future.
• Communication and Storytelling: A great data scientist isn’t just someone who can crunch numbers but someone who can explain what those numbers mean. Good storytelling helps translate the complex into the understandable, turning raw data into actionable insights. In 2023, soft skills like communication and storytelling continue to be in high demand alongside technical expertise.

While the technical skills needed can vary depending on your industry and specific company needs, these are areas that are becoming universally important in data science. Providing opportunities to upskill in these areas can ensure your team remains adaptable and ready to tackle the future of data science.

Upskilling Strategies

Now that we’ve highlighted the importance of upskilling and outlined the key skills to invest in for 2023, let’s discuss some effective strategies to upskill your data science team.

• Online Courses and Certifications: The internet is a treasure trove of learning resources, with platforms like Coursera, edX, and Udacity offering specialized courses in data science. These platforms offer up-to-date courses in partnership with leading universities and tech companies, ensuring your team gets quality and relevant learning materials. Encouraging your team to pursue relevant certifications can be a great way to upskill.
• Mentoring and Peer Learning: Internal mentoring programs where less experienced team members learn from their more experienced colleagues can be an effective way to transfer knowledge and skills. Similarly, encouraging peer learning — perhaps through coding challenges or pair programming sessions — can foster a healthy learning culture within your team.
• In-house Workshops and Seminars: Organizing in-house workshops on critical topics can be another excellent way to upskill your team. These can be led by team members who have a strong grasp of a particular area or by external experts. Regular seminars keep the team updated about the latest trends and advancements in data science.
• Participation in Data Science Communities and Forums: Online communities like Kaggle, GitHub, or Stack Overflow are places where data scientists from all over the world share their knowledge and learn from each other. Encouraging your team to participate in these communities can expose them to a diverse range of problems, solutions, and innovative ideas.

Remember, the goal of these strategies is not just to teach your team new skills but also to cultivate a culture of continuous learning. When your team sees upskilling as a valuable, ongoing process rather than a one-time task, they’ll be better equipped to keep up with the rapidly changing field of data science.

Measuring Success and Tracking Progress

With the strategies in place and the team ready to plunge into upskilling, the next important step is to track the progress of these initiatives. How do you know if your upskilling efforts are effective? Here are some ways to measure success:

• Improvement in Project Outcomes: As your team members start applying their newly acquired skills, you should observe noticeable improvements in the quality of work and efficiency. It could be faster processing times, more accurate models, or clearer data visualizations.
• Increased Efficiency: Upskilling should make your team more autonomous and efficient. This can look like bringing tasks in-house that were previously outsourced or realizing efficiency gains in tasks that once took a long time. 
• Feedback from Team Members: Regularly check in with your team. Are they finding the upskilling initiatives useful? How do they feel about their progress? Their feedback can provide valuable insights into what’s working and what needs improvement. 
• Skill Assessments: Regular skill assessments can help measure the level of improvement in the specific skills targeted by the upskilling initiative. This can be done through quizzes, presentations, or project-based assessments.
• Retention Rates: As mentioned earlier, employees are likely to stick around longer if they feel the company is investing in their growth. So, consider monitoring turnover rates before and after implementing the upskilling initiatives. A decrease in turnover can be a good indication that your upskilling efforts are successful.

Remember, the goal of tracking progress is not to introduce a punitive or high-pressure environment but to better understand how the team is evolving. Celebrate the wins, and take the challenges as opportunities to refine your upskilling strategy. The journey to upskilling your data science team is iterative and adaptive, just like the data science discipline itself.

Preparing for the Future With Upskilling

Navigating the ever-changing landscape of data science might seem daunting, but with a systematic approach to upskilling, your team will be ready to not only weather the storm but also ride the waves of change.

Upskilling your data science team isn’t just about staying current — it’s about looking ahead and being prepared for what’s coming. It’s about creating a team that’s resilient, adaptable, and always ready to learn. It’s about setting the pace, not just keeping up with it. 

So, as a tech leader, recruiter, or hiring manager, remember that the key to a successful data science team lies not just in hiring the right people but also in continuously investing in their growth. Provide them with the tools, resources, and opportunities to learn and improve, and you’ll have a team that’s not just prepared for the year ahead, but also for the many exciting developments that lie beyond.

This article was written with the help of AI. Can you tell which parts?

The post How to Upskill Your Data Science Team in 2023 appeared first on HackerRank Blog.

With 328.77 million terabytes of data being created each day, harnessing the power of data has become more crucial than ever. Once a distinct competitive advantage, unlocking the secrets hidden within this data is now a business imperative. The fingerprints of data science are everywhere in the tech we see today, from online ads to the navigation apps we rely on to show us the best route to our destination. But what exactly is the magic behind data science? And what makes it so indispensable? 

Simply put, data science is the process of extracting actionable insights from raw data. It’s a discipline that uses a variety tools, algorithms, and principles aimed at finding hidden patterns within the troves of data we produce daily. And it’s the driving force behind technologies like artificial intelligence and machine learning.

Whether you’re an experienced hiring manager or a budding data enthusiast, this article will give you a glimpse into the real-life applications of data science. Instead of an abstract, hard-to-grasp concept, we’ll see data science in action, breathing life into various industries, shaping our world, and quietly revolutionizing the way we do business. 

Banking and Finance

Data science has become an invaluable asset in the banking and finance sector, allowing companies to refine risk models, improve decision-making, and prevent fraudulent activities. With the increasing complexity and volume of financial data, data science tools help these companies dig deep to unearth actionable insights and predict trends. Let’s take a look at how it’s done in practice.

Fraud Prevention

American Express (Amex) has been making effective use of AI and machine learning (ML) to tackle an increasingly sophisticated form of credit card fraud: account login fraud. Amex developed an end-to-end ML modeling solution that assesses risk at the point of account login, predicting whether the login is genuine or fraudulent. High-risk logins are subjected to further authentication, while low-risk logins enjoy a smooth online experience. This real-time fraud detection model leverages vast amounts of customer data, assessing the most recent information, and continually calibrating itself. The success of this predictive model has been marked by a significant decrease in fraud rates over time, making it more effective than most other third-party models in the marketplace.

Automated Trading 

High-frequency trading firms, like Renaissance Technologies and Citadel, utilize data science to automate trading decisions. They process large volumes of real-time trading data, applying complex algorithms to execute trades at high speeds. This allows them to capitalize on minor price differences that may only exist for a fraction of a second, creating an advantage that wasn’t possible before the advent of data science.

Gaming

The gaming industry, one of the most data-intensive sectors, is reaping the benefits of data science in an array of applications. From understanding player behavior to enhancing game development, data science has emerged as a key player. With its predictive analytics and machine learning capabilities, data science has paved the way for customized gaming experiences and effective fraud detection systems. Let’s examine how the gaming giants are leveraging this technology.

Player Behavior Analysis

Electronic Arts (EA), the company behind popular games like FIFA and Battlefield, uses data science to comprehend and predict player behavior. They collect and analyze in-game data to understand player engagement, identify elements that players find most compelling, and tailor their games accordingly. This data-driven approach not only improves player satisfaction but also boosts player retention rates.

Game Recommendations 

Steam, the largest digital distribution platform for PC gaming, utilizes data science to power its recommendation engine. The platform analyzes players’ past behavior and preferences to suggest games they might enjoy. This personalized approach enhances the user experience, increases engagement, and drives sales on the platform.

Cheating Prevention

Riot Games, the creator of the widely popular game League of Legends, deploys data science to detect and prevent cheating. Their machine learning models analyze player behavior to identify anomalous patterns that could indicate cheating or exploitation. This not only maintains a fair gaming environment but also preserves the integrity of the game.

Retail

The retail sector is another industry where data science has made significant strides. It has transformed the way businesses manage their supply chains, predict trends, and understand their customers. From optimizing product placement to forecasting sales, data science is giving retailers the insights they need to stay competitive. Here are a few examples of how data science is reshaping the retail industry.

Real-Time Pricing

OTTO, a leading online retailer in Germany, has effectively implemented dynamic pricing to manage and optimize the prices of its vast array of products on a daily basis. Leveraging machine learning models, including OLS Regression, XGBoost, and LightGBM, OTTO predicts sales volume at different price points to ensure efficient stock clearance and maintain profitability. Their cloud-based infrastructure, developed to handle the computational load, allows for the price optimization of roughly one million articles daily. This innovative application of data science has enabled OTTO to significantly increase its pricing capacity, delivering up to 4.7 million prices per week.

In-Store Analytics

Amazon’s physical retail and technology team recently introduced Store Analytics, a service providing brands with anonymized, aggregated insights about the performance of their products, promotions, and ad campaigns in Amazon Go and Amazon Fresh stores in the U.S. enabled with Just Walk Out technology and Amazon Dash Cart. These insights aim to improve the shopper experience by refining store layout, product selection, availability, and the relevance of promotions and advertising. Brands gain access to data about how their products are discovered, considered, and purchased, which can inform their decisions about product assortment, merchandising, and advertising strategies. 

Healthcare

Harnessing the power of data science, the healthcare industry is taking bold strides into previously uncharted territory. From rapid disease detection to meticulously tailored treatment plans, the profound impact of data science in reshaping healthcare is becoming increasingly apparent.

Disease Detection

Google’s DeepMind, a remarkable testament to the capabilities of AI, has made significant inroads in disease detection. This system, honed by thousands of anonymized eye scans, identifies over 50 different eye diseases with 94% accuracy. More than just a detection tool, DeepMind also suggests treatment referrals, prioritizing cases based on their urgency.

Personalized Medicine

Roche’s Apollo platform, built on Amazon Web Services (AWS), revolutionizes personalized healthcare by aggregating diverse health datasets to create comprehensive patient profiles. The platform has three modules: Data, Analytics, and Collaborations. With it, processing and analysis times for data sets have been dramatically reduced, facilitating scientific collaboration and expanding the use of AI in Roche’s R&D efforts. In the future, Roche plans to add new machine learning capabilities and initiate crowdsourcing for image data annotation.

Social Media

In the hyper-connected landscape of social media, data science is the force behind the scenes, driving everything from trend prediction to targeted advertising. The explosion of user-generated data provides an opportunity for deep insights into user behavior, preferences, and engagement patterns. Data science is key to deciphering these massive data sets and propelling the strategic decisions that make social media platforms tick.

Trend Identification

Twitter uses data science, specifically sentiment analysis, to uncover trending topics and gauge public sentiment. By analyzing billions of tweets, Twitter can identify patterns, topics, and attitudes, giving a real-time pulse of public opinion. This data is valuable not only for users but also for businesses, governments, and researchers who can use it to understand public sentiment toward a product, policy, or event. However, it’s worth noting that earlier this year, Twitter shut down access to its free API, which gives people access to its platform data, causing panic among both researchers and businesses that rely on Twitter data for their work.

Ad Targeting

Facebook leverages the power of data science for personalized ad targeting, making advertising more relevant and effective for its users and advertisers alike. By using machine learning algorithms to analyze user data — likes, shares, search history, and more — Facebook predicts user preferences and interests, allowing advertisers to tailor their ads to target specific demographics. The result is a more personalized, engaging experience for users and a more successful, profitable platform for advertisers.

Transport and Logistics

As we zoom into the bustling world of transportation and logistics, we find data science playing a crucial role in streamlining operations, reducing costs, and enhancing customer experiences. From predicting demand to optimizing routes, data science tools and techniques allow for smarter decision making and improved efficiencies.

Route Optimization

Uber’s groundbreaking business model would not have been possible without the powerful capabilities of data science. For instance, Uber uses predictive analytics to anticipate demand surges and dynamically adjust prices accordingly. Additionally, data science helps in optimizing routes for drivers, ensuring quicker pickups and drop-offs, and an overall smoother ride for the customer.

Supply Chain Optimization

Global logistics leader DHL uses data science for efficient logistics planning. By analyzing a vast array of data points such as transport times, traffic data, and weather patterns, DHL can optimize supply chain processes and reduce delivery times. This data-driven approach allows DHL to manage its resources more efficiently, saving costs, and improving customer satisfaction.

Energy

The energy sector stands to gain immensely from the incorporation of data science. From optimizing power generation and consumption to enabling predictive maintenance of equipment, data science is transforming how we produce and consume energy. The intelligence gleaned from data is helping companies reduce their carbon footprint, boost operational efficiency, and generate innovative solutions.

Optimizing Power Distribution

Siemens, a global leader in energy technology, is leveraging data science to optimize power distribution through their Smart Grid solutions. By collecting and analyzing data from various sources, including sensors, smart meters, and weather forecasts, Siemens can predict and manage energy demand more effectively. This enables utilities to balance supply and demand, optimize grid operations, and reduce wastage. The integration of data science into the energy grid allows for greater reliability, efficiency, and sustainability in power distribution systems.

Predictive Maintenance

General Electric (GE) is another prime example of a company harnessing the power of data science in the energy sector. Their wind turbines are embedded with sensors that collect data to be analyzed for predictive maintenance. Through advanced algorithms, GE can predict potential failures and schedule maintenance in advance. This proactive approach not only prevents expensive repairs and downtime, but it also extends the life expectancy of their equipment, providing a significant boost to efficiency and profitability.

The Transformative Power of Data Science

As you can see, data science has become an indispensable tool across various industries, revolutionizing the way businesses operate and making significant advancements possible. The application of data science techniques, such as predictive analytics, personalization, and recommendation systems, has enabled organizations to make data-driven decisions, improve operational efficiency, enhance customer experiences, and drive innovation. As we look to the future, the potential for data science applications continues to expand, promising even more transformative outcomes in the industries we discussed here — and beyond. 

This article was written with the help of AI. Can you tell which parts?

The post Data Science in Action: Real-World Applications and Case Studies appeared first on HackerRank Blog.

The year 2023 stands at the cutting edge of data analytics, where raw numbers transform into compelling narratives and businesses are redefining their DNA. What once began as a stream of basic insights has turned into a deluge of intelligence that’s continually changing our world.

Data analytics is no longer an auxiliary process; it’s the heartbeat of modern organizations. Its influence reaches into every corner of business, driving decisions and shaping strategies in real time. The marriage of powerful computing capabilities with an ever-growing ocean of data has given birth to novel trends that are redefining the landscape of data analytics.

As we look to the future, the power and potential of data analytics are more apparent than ever — yet constantly evolving. The question that looms large for tech professionals and hiring managers alike: What does 2023 hold for the realm of data analytics? 

As we peel back the layers of this intricate field, we uncover a landscape humming with innovation. Here’s a glimpse into a world where data is not just numbers but a dynamic entity shaping our tomorrow. 

1. AI & ML Become Inseparable Allies

The fusion of artificial intelligence (AI) and machine learning (ML) with data analytics isn’t new. What is remarkable, however, is the depth to which these technologies are becoming intertwined with analytics. In its most recent Global AI Adoption Index, IBM found that 35 percent of companies reported using AI in their business, and an additional 42 percent reported they are exploring AI.

Why this seamless integration, you ask? It’s simple. The raw volume of data we generate today is staggeringly large. Without the cognitive capabilities of AI and the automated learning offered by ML, this data would remain an undecipherable jumble of ones and zeroes.

AI is pushing the boundaries of data analytics by making sense of unstructured data. Think about social media chatter, customer reviews, or natural language queries — areas notoriously difficult for traditional analytics to handle. AI swoops in with its ability to process and make sense of such data, extracting valuable insights that would otherwise remain buried.

Meanwhile, machine learning is giving data analytics a predictive edge. With its ability to learn from past data and infer future trends, ML takes analytics from reactive to proactive. It’s no longer just about understanding what happened, but also predicting what will happen next. 

Take the financial sector, for instance, where ML is being leveraged to predict stock market trends. Businesses are using ML algorithms to analyze vast amounts of data — from financial reports to market indices and news feeds — to predict stock movements. This capability is transforming investment strategies, allowing traders to make more informed and timely decisions.

However, as AI and ML technologies become further embedded in data analytics, they bring along their share of regulatory and ethical concerns. Concerns around data privacy, algorithmic bias, and transparency loom large. As AI and ML continue to shape data analytics in 2023, a close watch on these concerns will be paramount to ensure ethical and responsible use.

2. Edge Computing Continues Accelerating Data Analysis

As we delve deeper into the bustling world of data analytics in 2023, we bump into a trend that’s hard to ignore: the shift of analytics toward the edge. The traditional model of data analytics, where data is transported to a central location for processing, is gradually giving way to a more decentralized approach. Enter edge computing — a market that’s expected to reach $74.8 billion by 2028.

In simple terms, edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. It’s like moving the brain closer to the senses, allowing for quicker response times and less data congestion. This decentralization helps solve latency issues and reduces the bandwidth required to send data to a central location for processing, making data analysis faster and more efficient.

The Internet of Things (IoT) has played a massive role in propelling edge computing forward. With billions of devices continuously generating data, the need for real-time data analysis is more acute than ever. Edge computing allows for on-the-spot processing of this data, enabling quicker decision making. 

Consider a smart city scenario, where an array of IoT sensors continuously monitors traffic conditions. With edge computing, data from these sensors can be analyzed locally and instantaneously, allowing for real-time traffic management and swift responses to changes. This capability would transform urban living, promising less congestion, improved safety, and more efficient use of resources.

In 2023, as the edge computing trend continues to gain momentum, it’s reshaping the landscape of data analytics. We’re moving away from the days of heavyweight, centralized processing centers to a more nimble and efficient model, where analytics happens right where the data is. It’s an exciting shift, promising to make our world more responsive, secure, and intelligent.

3. More Businesses Embrace Synthetic Data

And now we encounter a relatively new entrant to the scene: synthetic data. As the name implies, synthetic data isn’t naturally occurring or collected from real-world events. Instead, it’s artificially generated, often using algorithms or machine learning techniques. Gartner predicts that by 2030, synthetic data will overtake real data in AI models.

But why bother creating data when we have real data in abundance? The answer lies in the unique advantages synthetic data offers, especially when real data falls short.

One of the major benefits of synthetic data is its role in training machine learning models. In many situations, real-world data is either scarce, imbalanced, or too sensitive to use. Synthetic data, carefully crafted to mimic real data, can fill these gaps. It’s like having a practice ground for AI, where the scenarios are as close to real-world situations as possible without infringing on privacy or risking data leaks.

Let’s consider autonomous vehicles, which rely heavily on AI and ML algorithms for their operation. These algorithms need vast amounts of training data — everything from images of pedestrians and cyclists to various weather conditions. However, collecting such a diverse and exhaustive range of real-world data is not just challenging but also time and resource-intensive. Synthetic data comes to the rescue, allowing researchers to generate as many training scenarios as needed, accelerating development and reducing costs.

Another advantage of synthetic data lies in its potential to eliminate biases. Because it’s artificially generated, we have control over its attributes and distributions, which is not the case with real-world data. Thus, synthetic data provides an avenue for creating fairer and more balanced AI systems.

In 2023, synthetic data has emerged as a powerful tool in the data analyst’s arsenal. By addressing some of the challenges associated with real-world data, synthetic data is pushing the boundaries of what’s possible in data analytics. However, it’s essential to note that synthetic data isn’t a replacement for real data; rather, it’s a valuable supplement, offering unique advantages in the right contexts. 

4. Data Fabric Gets Woven Into Analytics

In 2023, the data landscape is complex. We are dealing with not just massive volumes of data, but data that is diverse, distributed, and dynamic. Navigating this landscape can be a daunting task, but there’s an emerging trend that’s changing the game: data fabric. By 2030, the data fabric market is predicted to reach $10.72 billion, up from $1.69 billion in 2022. 

In simple terms, data fabric is a unified architecture that allows data to be seamlessly accessed, integrated, and analyzed regardless of its location, format, or semantics. Imagine it as an intricate tapestry woven with different threads of data, providing a holistic, interconnected view of all available data.

But what’s driving the adoption of data fabric? The answer lies in the increasing complexity and scale of today’s data ecosystems. Traditional data integration methods are struggling to keep up, leading to siloed data and limited insights. Data fabric emerges as the solution to this problem, enabling a more agile and comprehensive approach to data management.

The significance of API-driven and metadata-supported data fabrics has become more apparent in 2023. APIs, or application programming interfaces, provide a means for different software applications to communicate with each other. They act as bridges, enabling seamless data flow across different systems. Metadata, on the other hand, provides context to the data, helping to understand its origins, relationships, and usefulness. Together, APIs and metadata form the backbone of an effective data fabric, enabling efficient data discovery, integration, and analysis.

Let’s consider an example in the healthcare sector, where data fabric is making a real difference. Health organizations often deal with diverse data sets from various sources — patient records, medical research data, real-time health monitoring data, and more. A data fabric approach can bring together these disparate data sources into a unified architecture. This means quicker and more comprehensive insights, improving patient care and medical research.

The increasing adoption of data fabric is not just streamlining data management but also transforming the potential of data analytics. It allows organizations to navigate the data landscape more effectively, unlocking insights that would have remained hidden in a more fragmented data approach.

5. Sustainability Garners More Attention

As we continue exploring the 2023 data analytics trends, there’s one that goes beyond the numbers and tech: sustainability. We’re living in an age of acute awareness, where the carbon footprint of every activity is under scrutiny, including data analytics.

You might wonder how data analytics can contribute to the global carbon footprint. The answer lies in the tremendous energy consumption of data centers that power our digital world. As our reliance on data grows, so does the need for more storage and processing power, leading to more energy consumption and increased carbon emissions. It’s an issue that the tech industry can no longer afford to ignore.

In 2023, we’re seeing a stronger focus on “green” data analytics. Companies are exploring ways to decrease the energy footprint of data analysis without compromising on the insights they deliver.

One of the ways organizations are achieving this is through more efficient algorithms that require less computational power, and therefore, less energy. Another strategy is leveraging cloud-based analytics, which often provides a more energy-efficient alternative to traditional data centers. Companies like Amazon and Microsoft are investing heavily in renewable energy sources for their cloud data centers, offering a greener solution for data storage and processing.

At the hardware level, innovative designs are emerging that consume less energy. For instance, new chip designs aim to perform more computations per unit of energy, reducing the power requirements of the servers that store and process data.

Data analytics has always been about finding efficiencies and optimizations in the data. Now, it’s also about finding efficiencies in how we manage and process that data. As we move further into 2023, the focus on sustainable data analytics will continue to grow, contributing to the broader global effort to combat climate change. It’s an exciting and necessary evolution in the data analytics world, intertwining the pursuit of insights with a commitment to sustainability.

6. Data Becomes More Democratized

While calls for the democratization of data have been growing for years, it will become imperative for businesses in 2023. The days when data was the exclusive domain of IT departments are fading. Now, everyone in an organization is encouraged to engage with data, fueling a culture of informed decision-making.

But why is this happening? Because data literacy is no longer a luxury; it’s a necessity. In an age where data drives decisions, the ability to understand and interpret data is critical. It’s not just about accessing data; it’s about making sense of it, understanding its implications, and making informed decisions based on it.

Recognizing this, organizations are investing in improving data literacy across all levels. In fact, a recent Salesforce survey found that 73 percent of companies plan to continue or increase spending on data skills development and training for their employees. By providing additional training and resources, businesses can enable non-technical team members to understand and use data more effectively. It’s about creating a data-fluent workforce, where everyone is equipped to use data in their respective roles.

Another key aspect of data democratization is the growing reliance on self-service tools. These are platforms that simplify data analysis, making it accessible to non-technical users. Think of them as “data analysis for everyone” — tools that distill complex data into understandable and actionable insights.

A marketing team, for instance, might use these tools to analyze customer behavior data, identify trends, and develop more targeted marketing strategies. They no longer have to rely on IT or data specialists for every query or report, speeding up the decision-making process and empowering them to act quickly based on their findings.

However, data democratization also brings challenges, especially around data governance and security. Ensuring data is used responsibly and doesn’t fall into the wrong hands is a critical concern. As a result, strong data governance strategies and robust security measures are becoming increasingly important.

The Future Is Bright — and Data-Driven 

The landscape of data analytics in 2023 is a testament to the incredible pace of change and innovation in this domain. We’re witnessing an exciting fusion of technology, strategy, and ethical considerations that promise to redefine the way we collect, interpret, and apply data.

Each of the trends we’ve explored today, from the deepening integration of AI and ML and the shift to edge computing to the rise of synthetic data and the much-needed focus on sustainability, all point to a future where data is not just a silent bystander but a dynamic participant influencing decisions and actions.

In essence, we’re moving toward a future where data analytics will be even more embedded in our day-to-day lives, driving improvements in sectors as diverse as healthcare, transportation, marketing, and urban planning. It’s an era where we’re not just analyzing data but understanding and leveraging it in ways that were unimaginable just a decade ago.

Moreover, the focus on democratization and ethical considerations promises a more inclusive and responsible future for data analytics, one where the benefits of data insights are not restricted to a few but are available to many. This future also ensures that as we unlock new possibilities with data, we do so in a manner that respects user privacy and contributes positively to environmental sustainability.

In 2023, data analytics continues to break new ground and redefine its boundaries. But one thing remains certain: these trends signify the start of an exciting journey, not the destination. As we continue to push the envelope, who knows what new possibilities we’ll uncover. As data enthusiasts, professionals, and connoisseurs, the future indeed looks bright, challenging, and full of opportunities.

This article was written with the help of AI. Can you tell which parts?

The post Top 6 Data Analytics Trends in 2023 appeared first on HackerRank Blog.

Data is growing at an unprecedented rate, with 328.77 million terabytes of data being created every single day. Businesses have more data at their fingertips than ever before, but harnessing that data is rarely a simple task. The challenge that arises for businesses and organizations is not just to gather this data, but to make sense of it, to unravel the stories hidden beneath the numbers. That’s where data visualization comes into play.

Leaders need to decipher complex data and act quickly, and that’s where the power of data visualization shines. Data visualization serves as our map in the vast landscape of data, guiding us to insights that could easily get lost in the rows and columns of raw data sets. It helps businesses understand their performance, customers, and market, and aids in predicting future trends. 

As data visualization continues to play a key role in business decision-making, the demand for professionals such as data scientists is only expected to grow. By understanding how data visualization is changing the face of data analysis and how it can be used to create compelling data narratives, businesses and data professionals alike can stay ahead of the curve and build the skills and teams needed to thrive in a data-driven future. 

What is Data Visualization?

Data visualization is the graphical representation of data and information. Using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. It’s a bit like translating numbers into a language we can all intuitively understand — the language of visuals.

For instance, imagine you’ve got a spreadsheet in front of you with thousands of rows of data on global climate change. You could spend hours poring over it, or you could plot it onto a world map that color codes each region based on the increase in temperature. Which do you think would help you — and others — understand the data better and faster? 

Why is Data Visualization Important?

In our data-saturated world, the ability to translate complex datasets into digestible, understandable, and actionable visuals is critical. It enhances the comprehensibility of data and enables decision makers to see analytics visually, understand complicated concepts, and identify new patterns that might go unnoticed in text-based data.

Data visualization isn’t just a pretty way to see data; it’s a way to bring data to life and tell its story. It can help show how things have changed over time, how variables interact with each other, and how certain factors could potentially affect future outcomes. 

Who Uses Data Visualization?

Almost everyone in an organization can benefit from data visualization. From top-level executives looking for industry trends to make strategic decisions, to marketing teams analyzing campaign results, to IT departments tracking software performance, data visualization can deliver insights for all. 

In particular, data scientists, data analysts, and statisticians often use data visualization to check the quality of their data and to explore it for patterns, trends, relationships, and anomalies. It’s an integral part of their workflow — a means to “speak” data more eloquently.

Use Cases for Data Visualization

Data visualization can transform raw data into a form that’s easier to understand and interpret, making it a powerful tool for anyone looking to extract insights from their data. Let’s look at a few of the ways in which data visualization is commonly used.

Tracking Changes Over Time

One of the most common uses of data visualization is to track changes over time. Line graphs are particularly effective for this purpose. For instance, if a business wants to monitor their sales performance, a line graph showing monthly sales over several years can help identify patterns, trends, and potential anomalies.

Comparing Categories of Data

Comparing different categories or groups of data is another common use of data visualization. Bar charts and pie charts are often used for this purpose. Suppose a company wants to understand its market share. A pie chart can illustrate the company’s share of the market compared to its competitors, providing a clear picture of its competitive landscape.

Identifying Relationships Between Variables

Data visualization can also be used to identify relationships or correlations between different variables. Scatter plots are typically used for this purpose. For example, a marketing team may want to understand if there’s a relationship between advertising spend and website traffic. A scatter plot could help visualize any correlation.

Highlighting Patterns and Trends

Data visualization can help highlight patterns, trends, or anomalies that may not be immediately apparent in raw, tabulated data. Heat maps are a great way to visualize complex datasets, and can be particularly useful when trying to identify patterns or correlations.

Communicating Insights

Finally, one of the most important uses of data visualization is to communicate findings and insights to others, especially those who may not be data experts. A well-designed, clear visualization can tell a story about the data, making the insights it contains accessible to a wide audience.

Remember, the key to effective data visualization is to choose the right kind of visualization for your data and what you want to communicate. Not every visualization works for every type of data, so it’s important to understand your data and your goals before deciding how to visualize it.

Data Visualization Techniques

When it comes to data visualization, there isn’t a one-size-fits-all approach. The technique you choose depends on what you want to communicate. Here are a few common techniques:

• Charts and Graphs: These are the most common techniques used. Line charts are perfect for showing changes over time. Bar graphs compare different groups, while pie charts show parts of a whole.
• Heat Maps: Heat maps use color intensity to represent different values. These are particularly useful when you want to show patterns or correlations within large data sets, like user behavior on a website or geographical data.
• Scatter Plots: Scatter plots show the relationship between two numerical variables and are often used to identify trends, correlations, and outliers within a data set.
• Box Plots: Box plots are great for statistical studies, especially when you want to compare data across categories and identify outliers or patterns.
• Geospatial Visualization: Geospatial visualization, or map-based visualization, is used when geographic data is crucial to the story the data tells. For example, tracking disease outbreaks or visualizing demographic data.
• Interactive Dashboards: Interactive dashboards compile multiple visualizations onto a single screen, allowing users to interact with the data, change variables, and see the impact in real time.

Data Visualization Tools and Technologies

The tools you choose for data visualization can significantly affect your ability to interpret the data and extract insights. Some technologies offer a robust set of out-of-the-box tools for data visualization, while others require experience coding in languages like Python or JavaScript. Here are some of the most popular data visualization tools:

Tableau

Tableau is widely recognized for its intuitive drag-and-drop interface and its ability to create interactive dashboards quickly. It allows you to work with data from numerous sources, from Excel spreadsheets to cloud-based databases. You can then turn this data into comprehensive visualizations and even combine them into interactive dashboards.

PowerBI

Microsoft’s PowerBI is a tool that integrates seamlessly with other Microsoft products, making it an excellent choice for businesses already operating in a Microsoft environment. Like Tableau, PowerBI supports a wide variety of data sources and offers robust features for creating interactive reports and dashboards.

Matplotlib & Seaborn

For those comfortable with coding, Matplotlib is a versatile Python library for creating static, animated, and interactive visualizations. Seaborn is another Python library built on top of Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.

D3.js

For web-based visualizations, D3.js is hard to beat. This JavaScript library gives you the tools to create sophisticated, custom visualizations that can interact with web users. However, D3.js is not for beginners — it requires a solid knowledge of JavaScript, which can make the learning curve steeper than for other visualization tools.

Qlik Sense

Qlik Sense is known for its responsive design and touch interaction, making it an excellent choice for organizations that want to create visualizations accessible on various devices. It’s also praised for its “associative model” that helps users find unexpected insights.

Wrapping Up and Looking Ahead

As we move into an era where data is increasingly voluminous and complex, the role of data visualization is set to grow in scope and significance. It’s not just about providing clarity to the here and now; it’s also about pioneering the exploration of the unseen and the uncharted territories of data.

The future of data visualization is as dynamic as the data it seeks to represent. Technological advancements and innovations will continue to shape its course. As machine learning and artificial intelligence technologies evolve, we can expect to see more advanced, automated, and insightful visualization techniques. These innovations will enable us to not only visualize and understand data at unprecedented scales but also uncover patterns and insights that would be impossible to detect otherwise.

Emerging fields such as augmented and virtual reality also offer intriguing possibilities for data visualization, potentially allowing us to explore data in a fully immersive 3D environment, where complex data sets can be investigated from every angle, literally bringing the data to life.

But as much as data visualization is about technology, it’s equally about people. As the volume and complexity of data grow, so does the need for skills to interpret and communicate that data effectively. The data scientists, analysts, and storytellers of the future will need to master the art and science of data visualization to ensure data can be understood, decisions can be made, and innovation can be driven.

This article was written with the help of AI. Can you tell which parts?

The post What Is Data Visualization? The Art and Science of Seeing Data appeared first on HackerRank Blog.

From large corporations to small startups, businesses rely on data to make informed decisions, gain critical insights, and drive innovation. To effectively manage and analyze data, specialized tools and languages are required. One such language that has become a foundation of data management and analysis is SQL.

Since its inception in the 1970s, SQL has revolutionized the way businesses handle and process data. It has become the lingua franca of databases, enabling seamless communication between applications and database systems. SQL’s simplicity and versatility have made it the go-to language for managing and manipulating data, driving innovation across industries. From e-commerce platforms utilizing SQL for personalized recommendations to healthcare organizations leveraging it for analyzing patient data, SQL has transformed how we interact with information and become a key element of modern technology.

In this blog post, we’ll explore the world of SQL and its significance in the tech industry. Whether you are a hiring manager looking to understand the value of SQL skills or a tech professional interested in expanding your knowledge, this comprehensive guide will provide valuable insights into the power and versatility of this relational database language. 

What is SQL?

SQL, short for Structured Query Language, is a programming language designed for managing and manipulating relational databases. It serves as a standard interface for interacting with databases and performing operations such as data retrieval, insertion, modification, and deletion. SQL provides a structured approach to organizing and accessing data, making it an essential tool for data engineers, data scientists, data analysts, and software developers.

At its core, SQL operates on the concept of a relational database, which consists of tables that store data in rows and columns. These tables are interconnected through relationships, allowing for efficient and organized data storage. SQL provides a rich set of commands, known as queries, to interact with these databases and perform various tasks.

Let’s explore some fundamental aspects of SQL.

Data Definition Language (DDL)

SQL includes a set of commands for defining and modifying the structure of a database. With DDL statements such as CREATE, ALTER, and DROP, you can create new tables, modify existing ones, and remove unnecessary tables. DDL statements enable you to define the data types, constraints, and relationships within the database schema.

Data Manipulation Language (DML)

DML statements in SQL allow you to manipulate the data stored in the database. Commands such as SELECT, INSERT, UPDATE, and DELETE enable you to retrieve specific data, insert new records, update existing records, and delete unwanted data. DML provides the flexibility to perform complex operations on the database tables.

Querying and Retrieving Data

One of the primary strengths of SQL is its ability to query and retrieve data from databases. The SELECT statement is used to specify the columns to retrieve and the conditions to filter the data. SQL provides various clauses like WHERE, ORDER BY, and GROUP BY to refine the query results and sort the data based on specific criteria. This querying capability allows for efficient data retrieval and analysis.

Data Integrity and Constraints

SQL supports data integrity through constraints. Constraints ensure the accuracy and consistency of data stored in the database. SQL provides different types of constraints, such as primary key, foreign key, unique, and check constraints, to enforce rules and relationships within the data. These constraints help maintain data integrity and prevent inconsistencies.

Joins and Relationships

SQL allows you to establish relationships between tables using joins. Joins combine data from multiple tables based on related columns, enabling you to retrieve data that spans across tables. SQL supports different types of joins, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN, providing flexibility in querying related data.

SQL’s versatility extends beyond relational databases. It also offers extensions and features for handling large datasets, working with non-relational databases, and performing advanced analytics. These extensions, such as window functions, common table expressions (CTEs), and aggregate functions, enhance SQL’s capabilities and make it suitable for complex data analysis tasks.

Advantages of SQL

SQL has long been considered the industry-standard language in relational database communication. With a battle-tested track record and a robust ecosystem of resources, SQL remains a popular choice for database projects. Here, we’ll dive into some of the key advantages that have contributed to SQL’s widespread adoption.

Standardization

SQL is a standardized language that follows a set of rules and syntax defined by the International Organization for Standardization (ISO) and the American National Standards Institute (ANSI). This standardization ensures that SQL is consistent across different database management systems (DBMS). Developers and data professionals can leverage their SQL skills across various platforms without the need to learn different languages or techniques for each specific DBMS.

Ease of Use 

SQL offers a user-friendly and intuitive syntax that makes it relatively easy to learn and use. Its declarative nature allows users to focus on specifying what data they want to retrieve or manipulate rather than worrying about how to achieve it. The SQL queries resemble natural language, making them more accessible to individuals with minimal programming experience.

Data Integrity and Security

SQL provides robust mechanisms for maintaining data integrity and enforcing security measures. Through constraints, SQL ensures that data stored in databases adheres to predefined rules, preventing data inconsistencies. Additionally, SQL offers features such as user authentication, role-based access control, and encryption, which enhance the security of sensitive data.

Flexibility and Extensibility

SQL’s flexibility allows users to perform a wide range of operations on data. It supports complex queries, aggregations, sorting, and filtering, enabling users to extract valuable insights from datasets. Moreover, SQL has evolved beyond its traditional relational roots and now offers extensions for handling non-relational data, performing advanced analytics, and integrating with other programming languages.

Industry Support and Community

SQL has a vast and active community of developers, data professionals, and database vendors who contribute to its growth and development. This community-driven ecosystem provides access to a wealth of resources, including online forums, tutorials, documentation, and libraries, making it easier for users to find help, share knowledge, and stay up to date with the latest SQL advancements.

Integration with Other Tools and Technologies

SQL seamlessly integrates with a wide range of tools and technologies commonly used in the data ecosystem. It can be integrated with programming languages like Python, Java, or C#, enabling developers to incorporate SQL queries into their applications. SQL also integrates with popular data analysis and visualization tools, making it easier to extract insights and present data in a meaningful way.

Industries Using SQL

SQL’s versatility and power make it an indispensable tool for various data-related tasks. Let’s delve into some practical use cases where SQL shines and demonstrates its effectiveness in solving real-world data challenges.

• E-commerce and Retail: SQL is used extensively for managing product catalogs, tracking customer behavior, and analyzing sales data. SQL queries can help identify popular products, calculate revenue by category or region, monitor inventory levels, and generate personalized recommendations based on customer preferences. 
• Financial Services: SQL plays a critical role in the financial services sector for tasks such as risk analysis, fraud detection, and regulatory compliance. Financial institutions utilize SQL to query and analyze vast amounts of transactional data, identify patterns of suspicious activity, and generate reports for auditors and regulators. 
• Healthcare and Medical Research: SQL is employed in healthcare organizations and medical research facilities to manage patient records, track medical procedures, and analyze clinical data. SQL queries can help identify disease patterns, track treatment outcomes, and conduct population health studies. 
• Marketing and Advertising: Marketers use SQL to analyze campaign performance, customer segmentation, and advertising effectiveness. Marketers can use SQL to query customer databases and extract valuable insights for targeted marketing campaigns. SQL is also used for analyzing web analytics data, tracking website conversions, and measuring the success of online advertising campaigns. 
• Data Analysis and Business Intelligence: SQL is a fundamental tool for data analysts and business intelligence professionals across industries. These roles involve querying and manipulating data, generating reports and dashboards, and conducting data-driven analyses. 
• Human Resources: SQL is utilized in human resources for managing employee data, generating reports, and conducting workforce analytics. SQL queries can help HR professionals track employee performance, analyze training and development programs, and generate reports on employee demographics and diversity. 
• Logistics and Supply Chain: SQL is applied in logistics and supply chain management to track inventory levels, manage warehouse operations, and optimize logistics networks. SQL queries can help monitor stock levels, identify demand patterns, and streamline supply chain processes. 

SQL Hiring Trends

The increasing reliance on data-driven decision-making has fueled the demand for professionals who can effectively manage and analyze data. SQL, being a powerful language for data manipulation and retrieval, has become one of the most sought-after skills in tech. In our latest Developer Skills Report, we found that demand for SQL skills grew in 2022, putting it in third place on our list of the most in-demand programming languages — and even surpassing C++. 

This growth in demand is largely driven by the fact that SQL proficiency is a fundamental requirement for many data-related roles. Data analysts, data scientists, database administrators, and business intelligence specialists all use SQL to perform various data-related tasks. As the need for all types of data professionals has grown — fueled by advancements in artificial intelligence, machine learning, and Big Data— so too has demand for professionals who can leverage SQL effectively. 

Proficiency in SQL not only expands career opportunities but also positions individuals for career growth. It serves as a solid foundation for learning other data-related technologies and languages, allowing professionals to adapt to evolving industry trends and stay ahead in the competitive job market.

To learn more about the types of roles that require SQL skills and stay up to date on the latest trends, check out our roles directory.

This article was written with the help of AI. Can you tell which parts?

The post What Is SQL? A Guide to the Relational Database Language appeared first on HackerRank Blog.

Human beings have been analyzing data since the dawn of civilization. The drive to measure and study the world around us has proven pivotal for history’s greatest innovations. Now, as artificial intelligence accelerates the creation of data to unfathomable heights, the ability to analyze data will become increasingly vital.

Enter data analysts, the professionals responsible for collecting, processing, and analyzing data to provide insights that drive business decisions. 

Overview of Data Analytics

A data analyst’s primary role is to analyze data to uncover patterns, trends, and insights that can help businesses make informed decisions. They collect, clean, and organize data from various sources and use statistical analysis tools to create reports and visualizations that highlight key findings. Data analysts work closely with business stakeholders, such as marketing teams, product managers, and executives, to understand their requirements and provide them with data-driven recommendations.

On a more technical level, the core job responsibilities of data analysts include:

• Writing high-quality code
• Collecting, processing, cleaning, and organizing data
• Analyzing data to identify patterns and trends
• Creating data visualizations and dashboards 
• Presenting findings to stakeholders
• Conducting experiments and A/B tests
• Collaborating with cross-functional teams
• Keeping up-to-date with advancements in technology

What Kinds of Companies Hire Data Analysts?

Employers across every industry employ data analysts to unlock insights in their data. The top industries hiring data analysts include tech, finance, healthcare, ecommerce, and consulting firms.

Tech Companies

Companies such as Google, Microsoft, Amazon, and Facebook rely heavily on data analysis to improve their products and services.

Finance and Banking

Banks, investment firms, and insurance companies use data analysts to monitor and analyze financial data, make predictions and manage risk.

Healthcare

Hospitals, medical research institutions, and pharmaceutical companies hire data analysts to analyze patient data, clinical trial results, and research outcomes.

Retail and E-commerce

Retail and e-commerce companies hire data analysts to analyze customer behavior, sales data, and marketing trends to improve their products and services.

Government and Non-profit Organizations

Government agencies and non-profit organizations use data analysts to analyze large data sets and make data-driven decisions.

Manufacturing and Logistics

Manufacturing and logistics companies hire data analysts to optimize production processes, analyze supply chain data, and identify areas for cost reduction.

Types of Data Analyst Positions

Data analyst job titles vary widely, depending on experience, specialization, and industry. 

Early career-level professionals will typically start their career with an entry-level title like junior data analyst or data analyst I. They’ll typically work in that role for one to three years, gaining experience and domain expertise. 

A data analyst’s title may also vary depending on the industry they work in. Industry-specific job titles include:

• Business Intelligence Analyst
• Marketing Analyst
• Financial Analyst
• Healthcare Analyst
• Operations Analyst
• Data Science Analyst

From there, they may have the opportunity to move into more senior-level roles with more hands-on experience, such as senior data analyst or lead analyst. While they spend several years honing their skills, their responsibilities expand to include taking more ownership of projects, working independently in a team environment, and mentoring project team members. 

With some experience under their belt, a data analyst often faces a crossroads in their career. The first path is to pivot into people and team management functions, where hiring, mentoring, resource planning and allocation, strategy, and operations become a larger component of their role. The other possible career path is to continue as an individual contributor, where they can develop deeper technical expertise in various technology languages and frameworks.

Requirements to Become a Data Analyst

Programming Skills

Data analysts use several programming languages and frameworks to collect, process, analyze, and visualize data. The choice of programming language depends on the type of analysis required, the size and complexity of the data, and the individual preferences of the analyst.

Python

Python is one of the most popular programming languages for data analysis. It has a large and active user community and is widely used in scientific computing and data analysis. Python has several libraries and frameworks useful for data analysis, including Pandas, NumPy, Matplotlib, and Scikit-learn.

R

R is another popular programming language for data analysis. It has a comprehensive set of libraries and packages that make it ideal for statistical analysis and data visualization. R is particularly useful for working with large datasets and conducting advanced statistical analysis.

SQL

SQL (structured query language) is a programming language used to manage and manipulate relational databases. It is commonly used for data analysis, particularly in industries such as finance and healthcare, where data is stored in databases. SQL is useful for querying, manipulating, and aggregating data, and for creating complex reports and data visualizations.

MATLAB

MATLAB is a programming language commonly used for numerical computing, data analysis, and data visualization. It has a wide range of toolboxes and functions for signal processing, statistics, and machine learning. MATLAB is particularly useful for scientific computing and data analysis in fields such as engineering and finance.

Julia

Julia is a high-performance programming language designed for numerical and scientific computing. It has a simple syntax and is easy to use for data analysis, machine learning, and other scientific applications. Julia is particularly useful for working with large datasets and conducting complex statistical analysis.

D3.js

D3.js is a JavaScript library for creating interactive visualizations. It provides a powerful set of tools for creating complex and dynamic visualizations that can be integrated with web applications. D3.js is particularly useful for creating custom visualizations that are not easily achievable with other frameworks.

Technical Tools

Tableau

Tableau is a popular data visualization tool that allows users to create interactive dashboards and reports. It provides a wide range of built-in visualization options and a drag-and-drop interface for creating custom visualizations.

Excel

Microsoft Excel is a powerful tool that data analysts use for a variety of tasks. Some of the ways data analysts use Excel include:

• Data cleaning
• Data visualization
• Data analysis
• Pivot tables
• Macros

Power BI

Microsoft’s Power BI is a powerful data visualization and business intelligence tool that’s tightly integrated with Excel. Data analysts use Power BI to analyze data, create interactive dashboards, and share insights with others. 

SAS

SAS (Statistical Analysis System) is a software suite that data analysts use to manage, analyze, and report on data. Key functionalities in SAS include data management, statistical analysis, data visualization, machine learning, and reporting.

Mathematics & Statistics

Beyond programming, data analysts also need to be skilled in mathematics and statistics. Competency in the following subjects is key:

• Linear Algebra
• Calculus
• Probability
• Classification 
• Regression
• Clustering

Soft Skills

Technical competency alone isn’t enough to succeed in a data analyst role. Soft skills are a must in any data analysis role, Soft skills that are important for data analysts include:

• Time management
• Communication
• Presentation
• Project management
• Creativity
• Problem solving

Experience & Education

After competency, the most important qualification for data analysts is experience. For most employers, on-the-job experience and training is a critical requirement.

Then, there’s the question of education. 65% of data analysts have a bachelor’s degree, and 15% have a master’s degree. If you’re hiring data analysts, there’s a high likelihood that many of them will have a degree. And many companies still require data analysts to hold four-year degrees. However, many employers are broadening their candidate searches by prioritizing real-world skills.

The post What Does a Data Analyst Do? Role Overview & Skill Expectations appeared first on HackerRank Blog.

From large language models to AI-power climate science, the data science field is producing more diverse and exciting innovations than ever before. In the coming decade, data science will transform entire societies, governments, and global economies. Even the next evolution of humanity is in the works.

But as the possibilities of data science have expanded, so have the skills necessary to succeed as a data scientist. Top data scientists will need to balance their skill set between exciting new disciplines like natural language processing while maintaining traditional skills like statistics and database management. 

Artificial Intelligence

Artificial intelligence (AI) is the ability of a digital computer to perform tasks associated with intelligent beings. 

First theorized by Alan Turing in 1950, artificial intelligence (AI) has become a fast evolving discipline behind the world’s most innovative technologies. While AI has been around for decades, we’ve only begun to unlock its potential. Some experts believe AI is poised to usher in the next era of human civilization, with Google CEO Sundar Pichai comparing the advancement of AI to the discovery of fire and electricity. Given the nearly endless number of potential applications—including cancer treatment, space exploration, and self-driving cars—the tech industry’s need for data scientists with AI skills is vast.

AI is a complex and evolving field with numerous branches and specializations.

Natural Language Processing

Natural language processing (NLP) is the branch of AI focused on training computers to understand language the way human beings do. Because NLP requires massive quantities of data, data scientists play a significant role in this advancing field. With applications including fake news detection and cyberbullying prevention, NLP is among the most promising trends in data science.

Machine Learning

Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions. Machine learning algorithms are dependent on human intervention and structure data to learn and improve their accuracy. Data scientists build machine learning algorithms using programming frameworks such as TensorFlow and PyTorch. 

Deep Learning

Deep learning is a sub-field of machine learning characterized by scalability, consumption of larger data sets, and a reduced need for human intervention.

The launch of ChatGPT in late 2022 was a pivotal moment for deep learning, giving consumers their first hands-on exposure to the potential of the discipline. With applications including autonomous vehicles, investment modeling, and vocal AI, deep learning is an exciting field of artificial intelligence.

Deep learning frameworks that data scientists use include TensorFlow, PyTorch, and Keras.

Database Management

Database management is the process of organizing, storing, and retrieving data on a computer system. Data scientists use database management to cultivate and interpret data.

Database skills can be divided into two different categories. The type of databases data scientists work with will vary depending on their specialization or the needs of a given project.

Relational databases use structured relationships to store information. Data scientists use the programming language SQL to create, access, and maintain relational databases. Relational database tools include SQL Server Management Studio, dbForge SQL Tools, Visual Studio Editor, ApexSQL.

Non-relational databases store data using a flexible, non-tabular format. Also known as NoSQL databases, non-relational databases can use other query languages and constructs to query data. Non-relational database tools include mongoDB, Cassandra, ElasticSearch, Amazon DynamoDB.

Data Wrangling

Before business can make data-driven decisions, data scientists have to detect, correct, and remove flawed data. Data wrangling is the process of transforming raw data into a format more valuable or useful for downstream applications. Also referred to as cleansing or remediation, data wrangling is an essential of data science and analysis. However, this process is both time and labor intensive. Some sources estimate that data scientists spend most of their time on this mundane but vital task. Automated data cleansing using AI-based platforms is emerging as an efficient and scalable way for data scientists to work.

Because the creation of raw data is accelerating, it should come as no surprise that employer demand for the ability to transform data is accelerating. In 2022, demand for data wrangling grew by 405%, tying for first in our list of fastest-growing technical skills.

Data Modeling

Data modeling is the process of creating and analyzing a visual model that represents the production, collection, and organization of data. Data models are vital for understanding the nature, relationships, usage, protection, and governance of a company’s data. Tools that data scientists use for data modeling include ER/Studio, Erwin Data Modeler, SQL Database modeler, DBSchema Pro, and IBM InfoSphere Data Architect.

In 2022, demand for data modeling grew by 308%, ranking third in our list of fastest-growing technical skills.

Data Visualization

After unlocking valuable insights from raw data, data scientists need to communicate their findings in a clear and visual format. Data visualization is the process of creating graphical representations of data for presenting insights to technical and non-technical stakeholders. Data scientists create visuals like graphs, charts, and maps using data visualization tools such as Tableau or front-end languages such as JavaScript.

Like data wrangling, employer demand for this skill is accelerating. In 2022, demand for data visualization also grew by 405%, tying for first with data wrangling in our list of fastest-growing technical skills.

Programming

Data scientists use a range of programming languages to work with data. While there are a number of languages used in the field of data science, an individual data scientist might only learn a few languages that align with their specialization, interests, and career path.

Languages used for data science include:

• Python (used for math, statistics, and general programming)
• Java (used for data analysis, data mining, machine learning)
• Julia (used for numerical analysis and computer science)
• MATLAB (used for deep learning and numerical analysis
• R (used for statistical computing and machine learning
• Scala (used for big data and scalability)
• C/C++ (used for scalability and performance)
• JavaScript (used for data visualization)

Math and Statistics

One skill emphasis that makes data scientists unique is mathematics. While a strong background in math is important to any programmer, it’s essential to data scientists. Data science is equal parts statistics and computer engineering, so while a job description may or may not mention it, competency in the following subjects is vital:

• Statistics
  • Probability theory
  • Classification 
  • Regression
  • Clustering
• Linear Algebra
• Calculus

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Called the sexiest job of the twenty-first century, data science is one of today’s most promising technical disciplines. So promising, in fact, that Google CEO Sundar Pichai compared data science’s ongoing development of artificial intelligence (AI) to the discovery of fire and electricity.

In the coming decade, data science will transform entire societies, governments, and global economies. Even the next evolution of humanity is in the works. Here are the eight data science trends that will drive that transformation in 2023.

What is Data Science?

Companies of every size and industry need data to make informed business decisions. Doing so requires technical professionals who use statistics and data modeling to unlock the value in unprecedented amounts of raw data. Data scientists use statistical analysis, data analysis, and computer science to transform this unprocessed data into actionable insights.

On a more technical level, the core job responsibilities of data scientists include:

• Writing code to obtain, manipulate, and analyze data
• Building natural language processing applications
• Creating machine learning algorithms across traditional and deep learning
• Analyzing historical data to identify trends and support decision-making

2023 Data Science Trends

Automated Data Cleansing

Before business can make data-driven decisions, data scientists have to detect, correct, and remove flawed data. This process is both time and labor intensive, which drives up costs and delays decision making. Automated data cleansing is emerging as an efficient and scalable way for data scientists to outsource labor-intensive work to AI-based platforms. This will give data scientists more time and resources to focus on higher-impact actions, like interpreting data and building machine learning (ML) models.

AutoML

Automated machine learning (AutoML) is the process of “automating the time-consuming, iterative tasks of machine learning.” With AutoML, data scientists are able to build machine learning models in a development process that’s less labor- and resource-intensive. Efficient, sustainable, and scalable, AutoML also has the potential to increase the production of data science teams and make machine learning more cost-effective for businesses. Tools like Azure Machine Learning and DataRobot are even making it possible for users with limited coding experience to work with ML.

Customer Personalization

Have you ever received an ad for a product right after you thought about it? It wasn’t a coincidence. And brands aren’t able to read a consumer’s mind (yet). It turns out that data science is to blame. 

Data scientists are using artificial intelligence and machine learning to make recommendation systems so effective that they can accurately predict consumer behaviors. And it turns out that consumers are surprisingly excited about this new approach. 52% of consumers expect offers from brands to always be personalized. And 76% get frustrated when it doesn’t happen. To deliver on these expectations, companies need to collect, store, secure, and interpret huge quantities of product and consumer data. With the skills to analyze customer behavior, data scientists will be at the forefront of this effort.

Data Science in the Blockchain

By 2024, corporations are projected to spend $20 billion per year on blockchain technical services. So, it shouldn’t come as a surprise that data science is poised to help companies make sense of the blockchain. Data scientists will soon be able to generate insights from the massive quantities of data on the blockchain.

Machine Learning as a Service

Machine Learning as a Service (MLaaS) is a cloud-based model where technical teams outsource machine learning work to an external service provider. Using MLaaS, companies are able to implement ML without a significant upfront investment of budget and labor. With such a low cost of entry, machine learning will spread to industries and companies that would otherwise not be able to implement it. 

Use cases for MLaaS include: 

• Analyzing product reviews
• Powering self-driving cars
• Designing chatbots or virtual assistants
• Performing predictive analytics
• Improving manufacturing quality
• Automating natural language processing
• Building recommendation engines

Leading MLaaS providers include AWS Machine Learning, Google Cloud Machine Learning, Microsoft Azure Machine Learning, and IBM Watson Machine Learning Studio.

Natural Language Processing

Natural language processing (NLP) is the branch of AI focused on training computers to understand language the way human beings do. Because NLP requires massive quantities of data, data scientists play a significant role in this advancing field. 

There are a variety of use cases for natural language processing, including: 

• Credit score analysis
• Customer service chatbots
• Cyberbullying prevention
• Fake news detection
• Language translation
• Speech and voice recognition
• Stock market prediction

With so many potential applications, NLP is among the most promising trends in data science.

TinyML

TinyML is the implementation of machine learning on small, low-powered devices.  Instead of running on consumer CPUs or GPUs, TinyML devices can run microcontrollers which consume 1,000x less power. With such a high cost-efficiency, TinyML provides the benefits of machine learning while avoiding.

Synthetic Data

In 2021, GPU Manufacturer Nvidia predicted that data would be the “oil” that drives the age of artificial intelligence. And with 912.5 quintillion bytes of data generated each year, it might seem like the supply to drive this revolution is endless. But what if you could make your own oil? 

Much like natural resources, access to data isn’t distributed evenly. Many companies don’t have access to the huge quantities of data they need to drive AI, machine learning, and deep learning initiatives. 

That’s where synthetic data can help. Data scientists use algorithms to generate synthetic data that mirrors the statistical properties of the dataset it’s based on. 

Unsurprisingly, the potential use cases for synthetic data are as limitless as the data it creates: 

• Autonomous robots
• DevOps
• Fraud detection
• Medical training resources
• Privacy protection
• Product development and testing
• Self-driving cars
• Video surveillance

But there’s one effect of synthetic data that’s a guarantee: more data. With synthetic data, the world’s data generation will accelerate to a rate the human mind can’t begin to fathom.

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SQL interview questions have been a critical component of technical hiring for decades. If you’re a data scientist or software engineer on the job market, the ability to demonstrate your database skills in an SQL interview is critical to landing your next role. 

Despite being over four decades old, SQL is still evolving at a rapid pace. To succeed in an SQL challenge, you’ll need to stay up to date on the latest advancements and prepare for the styles of problems you might encounter. Instead of reviewing basic database definitions and concepts, this article will challenge you with seven advanced SQL interview question examples you need to be familiar with to land your next dream job.

Overview of SQL Interview Questions

SQL (structured query language) is the industry-standard language for working with relational databases. Used for creating, defining, and maintaining databases, SQL is a vital skill for data scientists and software engineers.

During an SQL interview problem, candidates receive sets of data tables, input formats, and output formats and are challenged to perform a series of queries or functions with that data. 

SQL interview questions can cover a wide range of database concepts, including normalization, transactions, subqueries, joining, and ordering.

While some interview questions cover basic-level questions – what is normalization? – experienced engineers and data scientists will encounter problems that test their SQL skills through hands-on coding.

Depending on the employer’s technical interviewing tool, candidates can choose from a range of relational database tools, including DB2, MySQL, Oracle, and MS SQL Server.

7 Advanced SQL Interview Questions

Below are seven examples of the kinds of problems a data scientist or software engineer might face during a technical interview. These questions all test SQL and relational database skills, and are meant to be solved in a collaborative integrated development environment (IDE).

To view the the data tables that accompany each question, click the solve problem link.

Print Prime Numbers

Solve Problem

Write a query to print all prime numbers less than or equal to 1000. Print your result on a single line, and use the ampersand (&) character as your separator (instead of a space).

For example, the output for all prime numbers <= 10 would be:

2&3&5&7

New Companies

Solve Problem

Amber’s conglomerate corporation just acquired some new companies. Each of the companies follows this hierarchy: Founder → Lead Manager → Senior Manager → Manager → Employee

Given the table schemas below, write a query to print the company_code, founder name, total number of lead managers, total number of senior managers, total number of managers, and total number of employees. Order your output by ascending company_code.

The tables may contain duplicate records. The company_code is string, so the sorting should not be numeric. For example, if the company_codes are C_1, C_2, and C_10, then the ascending company_codes will be C_1, C_10, and C_2.

Weather Observation Station

Solve Problem

Consider P1(a,b) and P2(c,d)  to be two points on a 2D plane.

• a happens to equal the minimum value in Northern Latitude (LAT_N in STATION).
• b happens to equal the minimum value in Western Longitude (LONG_W in STATION).
• c happens to equal the maximum value in Northern Latitude (LAT_N in STATION).
• d happens to equal the maximum value in Western Longitude (LONG_W in STATION).

Query the Manhattan Distance between points P1 and P2 and round it to a scale of 4 decimal places.

The STATION table is described as follows:

where LAT_N is the northern latitude and LONG_W is the western longitude.

Binary Tree Nodes

Solve Problem

You are given a table, BST, containing two columns: N and P, where N represents the value of a node in Binary Tree, and P is the parent of N.

Write a query to find the node type of Binary Tree ordered by the value of the node. Output one of the following for each node:

• Root: If node is root node.
• Leaf: If node is leaf node.
• Inner: If node is neither root nor leaf node.

Question: Tenured Employees

Concepts Covered: SQL (Basic), JOIN, ORDER BY

There are two data tables with employee information: EMPLOYEE and EMPLOYEE_UIN. Query the tables to generate a list of all employees who have been employed fewer than three years in order of NAME, then of ID, both ascending. The result should include the UIN followed by the NAME. While the secondary sort is by ID, the result includes UIN but not ID.

Interview Guidelines

Join the tables to get UIN. Filter results to TIME < 3 and sort ascending by name, id.

Schema

Sample Input

Challenge Question: 15 Days of Learning SQL

Solve Problem

Difficult Level: Hard

Julia conducted a 15 days of learning SQL contest. The start date of the contest was March 01, 2016 and the end date was March 15, 2016.

Write a query to print the total number of unique hackers who made at least 1 submission each day (starting on the first day of the contest), and find the hacker_id and name of the hacker who made maximum number of submissions each day (without considering if they made submissions the days before or after). If more than one such hacker has the maximum number of submissions, print the lowest hacker_id. The query should print this information for each day of the contest, sorted by the date.

Input Format

The following tables hold contest data:

• Hackers: The hacker_id is the id of the hacker, and name is the name of the hacker.
• Submissions: The submission_date is the date of the submission, submission_id is the id of the submission, hacker_id is the id of the hacker who made the submission, and score is the score of the submission.

Challenge Question: Interviews

Solve Problem

Difficulty Level: Hard

Samantha interviews many candidates from different colleges using coding challenges and contests. Write a query to print the contest_id, hacker_id, name, and the sums of total_submissions, total_accepted_submissions, total_views, and total_unique_views for each contest sorted by contest_id. Exclude the contest from the result if all four sums are 0.

Note: A specific contest can be used to screen candidates at more than one college, but each college only holds 1 screening contest.

Input Format

The tables hold interview data:

• Contests: The contest_id is the id of the contest, hacker_id is the id of the hacker who created the contest, and name is the name of the hacker.

• Colleges: The college_id is the id of the college, and contest_id is the id of the contest that Samantha used to screen the candidates.

• Challenges: The challenge_id is the id of the challenge that belongs to one of the contests whose contest_id Samantha forgot, and college_id is the id of the college where the challenge was given to candidates.

• View_Stats: The challenge_id is the id of the challenge, total_views is the number of times the challenge was viewed by candidates, and total_unique_views is the number of times the challenge was viewed by unique candidates.

• Submission_Stats: The challenge_id is the id of the challenge, total_submissions is the number of submissions for the challenge, and total_accepted_submission is the number of submissions that achieved full scores.

Resources for SQL Interviews

HackerRank SQL Questions

HackerRank SQL Certification (Advanced)

HackerRank Interview

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