A Day in a Work-Life a Data Science at Apple

Apple's data scientists can collaborate on important projects across the company's software, technology, and services divisions, influencing the lives of millions of individuals using Apple's products every day.

Sneha Runwal, a recent Springboard mentor who began her career at Apple as a data scientist and subsequently moved to the position of machine learning leader, discussed her experience breaking into data science or the skills she uses in her profession. 

Apple is well-known to be among the world's most data-driven companies. But what does this mean for the company's data scientists? Before we move on to the roles and responsibilities of Apple data scientists, feel free to check out the top data science course in Mumbai. 

What is a typical everyday life of an Apple data scientist like?

A data scientist's particular roles and responsibilities at Apple will vary based on the team they join. For example, a data scientist on a marketing or business analytics team will analyze product performance to find significant insights using complex analytical, machine learning, and other analytical tools. To boost Siri's accuracy, a data scientist in the Siri Search team may devote more time to machine learning techniques and artificial intelligence. A data scientist specializing in security may concentrate on predictive modeling for fraud prevention. 

Apple is notorious for having a challenging workplace where employees frequently work long hours, especially in the run-up to one of the company's high-profile product launches. Yet, many current and previous data scientists have praised the experience as rewarding since they work on important products and services with dedicated coworkers and get well compensated with perks that rival Google, Facebook, and Amazon.

Roles and Responsibilities of Apple data scientists' 

Everyone who wants to work in data science at Apple must have fundamental data science skills, including understanding the entire data science pipeline, fluency with computing programming languages like Python, SQL, and C++, and familiarity with data analysis, statistics, and machine learning methods. Yet, Sneha Runwal, former Apple data scientist to machine learning manager, claims to apply a few more talents in her work.

Data Visualization 

"Normally, we devote tremendous energy to learning how a particular algorithm works," Runwal observed. Yet, she believes that because communicating findings and recommendations to stakeholders is such an essential part of the job of a data scientist, data scientists should devote more time and attention to articulating their thoughts in a plain, convincing, or accessible manner. "Data visualization allows us to identify and analyze the consequences," she stated. "Hence, if I could go back in time and change one thing, it would be to devote more time to data visualization and investigate the ways in which it may communicate different concepts to a range of audiences.."

Bridging the gaps

Data scientists may face shortages and constraints also at Apple, a leading content company, and that is why Runwal feels it is essential to have the ability to cover a certain amount of poor data with your abilities and logic. "When people try to adjust to the additional information of scientific culture, they will constantly ask themselves, "Well, what might I have done differently if I hadn't ever received this data?" That's the only exercise I perform regularly. Runwal stated that addressing questions about what she may do otherwise frequently helps her develop new solutions for data science problems.

Patience and thoroughness are required.

As an Apple scientist, Runwal believes people like your position must find hours understanding the data she is working with. The better basic facts are understood, the quicker it will be to identify the problem and find a solution. "Should you dabble without knowing what you are doing, you can end yourself going in unexpected areas," she said. "As rapidly as possible, try to absorb all your information."

Strategies for Succeeding as an Apple Data Scientist

Runwal, who began her career in computer programming and business, considered her former skills valuable to the data science work at Apple. The following are a few of her success suggestions for individuals just beginning out in data science, whether they are newly graduated from college or changing fields.

• Find Connections

"Think about how your present field ties to data science," Runwal suggested. "Once you begin constructing that bridge, it will be simpler to adjust." In contrast, if one remains in the HR department, it is useful to inquire about how statistics might be used to increase the productivity and efficacy of their work. Or, whether you work in sales or marketing, may data help your team accomplish their business goals? Runwal urges people to consider how they could approach the challenges they hope to address as data scientists. "Whenever you interview for a data science position, it benefits the agreed to give why you chose data science," Runwal noted.

• Complete a job from start to finish.

"All candidates should try to complete that many assignments on their own." Runwal proposed. "When you lack relevant experience, many projects enable you to build a solid argument demonstrating your commitment." You might establish a business on Github or your own or publish some data blogs. They demonstrate your keen fascination with information technology." When hiring additional data scientists for Apple, Runwal explains she seeks end-to-end solutions, even though this indicates a person has only one job on their résumé. If you want to work as a data scientist at MAANG companies, register in the top data science course in Pune available online. 

Data Science and Astronomy – Let’s Get to Know

Imagine what data science can accomplish for highly advanced subjects like astrophysics if it can help corporations in traditional industries like technology, manufacturing, etc. retail enhance their businesses. Space is limitless, and countless incredible celestial objects are all waiting to be studied and discovered. Astronomers are provided with the proper technical instruments and shock data science, AI and ML capabilities to fully perfect their capacity to make sense of tremendously complicated celestial occurrences, both close and far away.

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Improvements in Astronomy Data Science

Data-driven Astronomy (DDA), as the name suggests, creates astronomical information from archival data sets that could or could not be directly related to the subject in issue. A fantastic example is the Galaxy Zoo project, which began in 2007 and tasked astrophysicists with identifying 900,000 photos from the Marshall Digital Space Survey over seven years to determine whether constellations were ellipses or spirals whether they were rotating or not.

Human analysis was nearly impossible due to the large volume of data involved. To complete it, one person would have had to work 24 hours a day, seven days a week, for three to five years. The solution is to develop current data science models for measuring big empirical mixed simulation data sets. Data from solar missions, planetary surveys, sky surveys at various wavelengths, Higgs boson devices, and sizable astronomical simulations are all included in these data sets. And they work together to help the astronomers achieve their essential research goals.

Astronomical Data Science: Learning to See Our Sun Better

The sun is possibly our planet's most promising source of energy. Not only for solar power but also as the natural form of fusion energy, solar power is a crucial component of sustainability for clean energy efforts. Yet, our comprehension is restricted to the facts that scientists can acquire. The temperature of the sun and the motion of solar radiation, for example, are relatively straightforward to observe, whereas horizontal motion is far more difficult to observe and contains the key to numerous of the sun's secrets.

To solve that issue, scientists from the United States and Japan created a model of neural networks that analyzed data from various plasma turbulence simulations. Following neural network training, it was possible to predict horizontal motion using only vertical motion + temperature as references. This technique has broad implications for solar astronomy, physics research, fluid dynamics, and fusion research projects. Other studies that will use this data include solar irradiation observations with the upcoming SUNRISE-3 balloon telescope.

Science of Astronomical Data Crowdsourcing

Another popular application of data science in astronomy is crowdsourcing or combining the efforts of thousands of "citizen scientists" to map the heavens and analyze data at scale. Exoplanet Explorers discovered at least six exoplanets using data from NASA's Kepler satellite observatory \. It's the inaugural multi-planet system discovered entirely through crowd data analysis efforts. The investigation first suggested a five system, but further data analysis revealed the presence of a fifth planet. Nearly 14,000 people participated in the crowdsourcing effort, and they are still seeing and analyzing data as it comes in.

Mars Exploration Using Data Science

Scientists have been looking for signs of life on Mars for many years, and new crewed spacecraft will soon send samples out from the planet's surface. The missions will rely heavily on tandem mass analysis to investigate sand samples on Mars for indications of past life. The volume of information that must be analyzed will be massive, so NASA will require new methods for analyzing samples quickly. NASA has created the Mars Spectrometry: Identify Evidence for Previous Incarnation challenge (with a $30,000 award for the most innovative analytical approach) in collaboration with global crowdsourcing startup HeroX / data science vendor DrivenData.

Summing Up!

What data science may accomplish is amazing when applied to advanced fields like astronomy. There's a little mistake that your data-driven world is growing increasingly exciting, whether it's developing unique statistical models to evaluate data at breakneck speed, collecting input from millions of astrophysicists, or data analysis science methodologies used by corporations like NASA. Launch your career through Learnbay's best Data science course in Pune, and get hired by top MNCs.

How Data Science Is Utilized Throughout The Automotive Lifecycle

The Ford Model T was introduced in 1908 and quickly became popular because of its low prices, durability, adaptability, and simplicity of upkeep. It is credited with "setting the globe on wheels" by increasing global mobility through operational efficiencies at a cost the typical consumer could afford.

Today, the automotive sector is still on the cutting edge of technology, changing how people get from where they need to go. Michael Crabtree, Data Scientist at Ford Corporation stated in a recent webinar that the fundamental change is that its growth is now pushed by data science rather than manufacturing. 

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In the automotive industry, smart cities actually require Data Science.

Data science is expanding mobility across low-income regions like the Model T's industrial scalability did over a century ago. It eases the transition for all, regardless of socioeconomic standing, gender, or abilities, by making transportation widely accessible without the high cost of ownership.

For example, optimization algorithms can supply entrepreneurs with energy-efficient cars to service regions for projects ranging from Amazon delivery times to plumbing, but mostly food delivery. Data scientists are working with vehicle and car engineers to create automobiles that benefit differently-abled individuals. These are just a few examples, but Michael claims there are almost limitless applications for machine learning, with so many more yet to be discovered.

Working with Data

Because of the automobile industry's maturity and breadth, organizations have several opportunities to rebuild around data.

One application interacts with data from many data systems and types. Several data scientists are used to working with tabular data, which is data in a pivot table, similar to Excel. However, passenger vehicle data scientists can access a much broader range of data. For example, direct instrumentation data is often recorded as a stream of binary numbers in the automotive industry. They may also come across data from intelligence systems, such as pictures and sensor point clouds. A vehicle data scientist may be needed to understand why autonomous vehicles perform in a given way and how this differs among vehicle models.

Every stage of the automobile product lifecycle involves data science.

Product development is fueled by data science.

Several stages must be completed before a vehicle can be leased to a consumer. Product development is where data science in autos begins. Data science is used to analyze new model settings and model component part reliability, among other things. Data science improves the technique through experimentation and evaluation at scale rather than developing components and testing at each level as an isolated system.

Manufacturing excellence is driven by data science.

In addition, automotive data scientists verify that only high-quality autos are marketed. While engineers seem to be able to test the functionality of each car, each vehicle must be tested individually. Data scientists can analyze a large population containing parts, sellers, and test data. They extensively examine suppliers' financial performance, forecast their ability to provide goods on time based on previous performance, and employ econometrics and regressions to assess the economical situation of supplier locations.

Data science propels connected and self-driving vehicles.

Connected and autonomous automobiles, which rely upon models of deep learning and sensor fusion techniques, are one of the trendiest subjects in futurology today. Data science is critical to developing these vehicles: It converts IoT indicators such as oil life screens, charger monitors, and comprehensive diagnostics instruments into actionable data. It isn't enough to simply discover a person; sensors must also be able to determine where they're walking. Safety systems, such as driver security and ecological safety, are also essential.

Sustainability projects are driven by data science.

All automobile manufacturers place a high value on sustainability. Governments set requirements for fuel usage, but each automaker has its own set of objectives. Yet because each motor has a distinct fuel economy, data science is required to optimize the fuel savings of a company's whole vehicle line. Hence, if a corporation wants to sell a giant natural truck and an electric car, mobile data scientists can optimize to reduce the whole fleet's fuel consumption while meeting the company's global sales targets. Automobile manufacturers may be able to claim regulatory credits for energy efficiency due to optimization work. This has three advantages: it is healthy for the environment, provides more value to customers, and opens up a new market.

Last Lines 

Aside from what we've already said, data science impacts many other stages of the automotive lifecycle. Data science predicts consumer mobility and churn in marketing and sales. Data science helps the consumer post-purchase experience and product quality in customer service and analytics. To go deeper into how data science techniques are impacting the future of automotive,  visit the data science course in Pune, which comes with multiple domains. 

What Can Computer Games Teach Us About Data Science?

The number of online gamers globally has expanded dramatically, with figures indicating that more than 2 billion gamers generate more than 50 GB of data daily. While AI and data science methods are frequently used to improve games and create a better game interface, this gaming can also teach players about data science.

According to studies, video games and the gaming business are among the most engaging ways for an individual to build perceptual, cognitive, behavioral, affective, and motivational impacts and outcomes. Yet, this should not become an addiction. I’ve compiled a list of 6 skills in video games that can educate gamers about data science.

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• Programming Languages 

Several video games instill the concept of increasing coding skills and assist users with computing techniques like Python, R, Perl, JavaScript, etc. Video games like Code Conflict and Empire of Code allow data science enthusiasts to improve existing coding skills if they aren't already familiar with them. Some games, such as Codinggame, can introduce players to programming languages, including Scala and Python, in order for them to win the games. These games assist players in improving their skill set while enhancing their chances of advancing in the profession. These games are available to both novice and advanced coders.

• AI & ML Concepts

Some games even assist players in honing their AI and machine learning skills by exposing them to bot creation. In games like Dynasty of Code, members can employ algorithms to create a bot that they may employ to influence other players. It is also one of the greatest methods for acquiring how to build and run working bots, a skill that may subsequently be applied in a career. Coding abilities are helpful here, which can be learned while playing video games, as previously noted.

• Data Analysis

Enhancing programming languages may help players improve in data analysis, one of the most important prerequisites for working in the data science business. Data Games, for example, is a collection of mini-games that assist players in comprehending the data behind each game. It allows the user to forecast matches with respect to the sample, which is helpful in honing data analysis skills. Anyone interested in entering the data science business can benefit from playing this exciting game to learn a bit or two concerning data analysis.

• Analytical Thinking

Analytical thinking ranks as one of the most important talents that data science practitioners must have. Games like Brainzilla provide a variety of mathematical tasks, aptitude tests, parables, brainstorming questions, and other activities that might assist the player in developing analytical thinking skills. It also includes answering math in a set amount of time, similar to a rapid-fire round. The puzzles span from easy to challenging levels and can be used by people of all ages to help them develop logical thinking.

• Math skills

Games can be a pleasant and exciting technique to instill mathematical abilities and concepts in children and adults. Games like Monkey Labs require solving math puzzles, where tough puzzles are presented to the players and must be answered in a time-constrained manner. It not only introduces you to new math skills, but it also enhances your speed and agility when solving problems.

• Statistical and probability 

Games like Rabbits and Wolves provide an attractive environment for developing statistics and probability skills. It exposes your players to circumstances, including assigning the correct values and evaluating the probability of a certain situation, assisting in developing these skills.

So these were the top skills to know as a data scientist working in gaming and other other fields. If you are planning to start from the ground up, refer to the data science course in Pune right away!

9 Reasons Why You Should Study Data Science

According to Harvard Business Review, data science is the "best-looking job of twenty-first centuries." So, what exactly is the importance of Data Science? How do Data Scientists rank among the highest-paid professionals? Most importantly, then why would you pursue a career in data science? In this piece, we'll look at some of the chiefs causes Data Science has become the most in-demand job in the industry. We'll learn about business needs and why companies need Data Scientists to boost their performance.For you, Learnbay has the top data science course in Mumbai. For more information, please see the website.

Reasons to Study Data Science

There is a lack of data scientists in the industry, but the need for them is growing. This is why mastering data science is so important right now. Those are the best 10 reasons to learn data science.

1. The job market's demand for data scientists

Businesses have learned that they must hire people who can gather, analyze, and apply data in a broad context to benefit the organization. Because there are so few people in this industry, the necessity for a software engineer is growing by the day. Studying data science provides you with the capacity to get very good work in a market that's desperately needed right now.

2. Data science workers get a high wage.

According to surveys, the income range of such a data scientist as in United States is $ 104,000 and $ 153,750 every year. This compensation range variance is determined by the type of contribution that they bring to the firm. A level yet another data science contributor's base compensation is $ 97,000, while a level three data science contributor's base income is $ 152,000. They also get another bonus that ranges from $ 10,000 for level 1 contributors to a considerably larger range for level 3 contributors.

Disclaimer: The above-mentioned income figures are not universal and vary by area.

3. Data Science has the potential to improve the world.

Data Science serves as far more than an instrument for predictive analytics. Numerous charitable and nonprofit organizations are using data to create products for the greater good. Several healthcare organizations use data to help clinicians obtain a better knowledge of our patient's health.

In this part, we will look at some examples of firms leveraging data to social good. This can help you generate drive to investigate Data Science as little more than a way to enhance people's lives.

4. There are numerous career opportunities available if you are skilled in data science.

If you study data science, you will have a variety of work options. Big dataset analytics is just a subgroup of data scientists that has a thriving business. If you are competent in this sector, there are numerous job titles available from large corporations such as IBM, Oracle, and Opera, among others. Learning data science expands your work opportunities. Below are a few job openings for data science students.

• Data scientist

• Data analyst

• Engineer in machine learning

• Data scientists in general

5. The opportunity to work as a data science executive

Being a data psychology major can eliminate the possibility of becoming a data science management. It has been observed that the wages of data science executives are nearly equivalent to or higher than those of doctors. Level 1 Data Science managers can make up to $140,000 per year. Level 2 professionals make $ 190,000 per year, while level 3 professionals make $ 250,00 per year. This is more than a psychiatrist, internal medicine doctor, or paediatrician makes.

6. You will work from anywhere on the planet.

If you grow into a data science professional, you will be able to work anywhere in the globe. In the United States, about 43% of professionals work upon that West Coast, while nearly 28% work in the Northeast. These specialists work in every part of the US as well as abroad. Yet, the highest pay in this sector is now found on the east coast of the United States.

Apart from engaging in the technology industry, data scientists are also employed in other major areas like as healthcare, finance, and marketing. They also share their knowledge with other consulting businesses, CPG industries, and retails.

7. The opportunity to work with big data analytics

Data analytics are becoming increasingly important in every career field and company. The use of big data analysts in businesses is just as crucial as the use of computers in the workplace. Marketing communication, customer management, or big data analytics are the three most crucial things for businesses. Every data science kid has a bright future in the sector because demand will continue to rise, resulting in more work prospects.

8. A wide range of undergraduate disciplines and educational opportunities

Data science is still a relatively new discipline. This seems to be one subject that evolved from others such as statistics as well as mathematics, engineering and computers, and natural science. Many data scientists have degrees in social sciences, economics, medical sciences, and even business.

However, understanding big data does not include sitting in a room all day. This can be learned both online and at your leisure.

9. Enhance Your Problem-Solving Capabilities

To be a good data scientist, you must have a strong drive to investigate an issue, identify the concerns at its heart, and build viable hypotheses to address it.

If you've got a strong sense of curiosity, you are likely to succeed in the data science profession. That being said, should you wish you start from the bottom up, Learnbay provides a virtual data science course in Pune. For further information, go to the website.

6 Popular Data Science Books for Beginners in 2023

The field of data science is quickly growing, and it has vast potential to change how individuals use their time. With the increased data generation, understanding the tools and methodologies for data interruption is becoming increasingly crucial for data science professionals. If you're a newbie or a qualified data scientist, reading the most recent textbooks on the subject will help you stay ahead of your colleagues and up to the minute with the newest and greatest advances in the business. However, if you are looking for some certification courses to upgrade your knowledge, Learnbay has got the best data science course in Mumbai, for you. Visit the site for further information. 

This blog will feature the top data science books to read in 2023. The book covers many themes, including deep learning, big data visualization, and many others. As a result, if you read the books, you will gain significant insight and information.

Best Data Science Books for Beginners and Experts

The classification in the notification is data science books. They are frequently educational and provide readers with a task to help them develop their data science skills. The books might help hopefuls improve their data science understanding for educational and job advancement.

1. The Art statistics: How to learn Data by David spiegel hater

Statistics are required because any data science procedure is to be successful. Yet, powerful machine learning methods use statistics to transform data into usable insights, allowing for error-free conclusions and ensuring success. The book perfectly presents the field of statistics without getting bogged down in mathematical calculations. It contains real-world examples, and the author demonstrates how professionals utilize information to resolve queries and how readers can do the same to grasp numbers, ask the proper questions, and regulate assumptions.

2. Storytelling with Data by cole Nussbaumer knaflic

In data research and information visualization, correct and effective insight is optional. Yet, the book will emphasize the most significant point. The book uses a storytelling structure and detailed images to teach many concepts. The author strives to maintain the book as complete as possible, allowing customers to obtain important points rather than wasting time. Yet, reading books enables people to quickly grasp crucial principles such as necessary information processing, visualization tools, and observation.

3. Advanced R by Hadley Wickham

Those who are just getting started with 'R,' those who want to improve their programming and method are able, and those who want to grasp the intricacies of just this data-driven language. If you want to master the 'R language,' Advanced R will give you a thorough overview and sensible instruction.

Hadley Wickham wrote chapters that were clear and logical. The jargon was minimally utilized, and the book's pragmatism was a winning combination. This book is just a must-have for any data engineer's library.

4. Machine Learning Yearning by Andrew Ng

This is one of the greatest data science books for someone familiar with artificial intelligence and machine learning but wants to learn more about the subject and get started with autonomous technologies.

In recent years, machine learning, based on analyzing and accumulating complicated information, has emerged as a key area of data science. Machine learning is already being used by 20 percent of C-level executives throughout the world as a component of their leading companies.

The face and essence of our work and personal lives are changing due to artificial intelligence. Studying machine learning and the repositories of big data that can be utilized to generate self-evolving, autonomous machine-learning systems is critical to understanding the significance of information and how it is used in modern culture. Andrew Ng, a well-known computer scientist, wrote this engaging book. It provides a simple approach to big data & machine learning.

5. Obvesily Awesome: How to nail product positioning by April Dunford

As a software engineer, you may not consider your work a brand, but it is. You should, however, be able to illustrate what you can provide for your consumers in a way that captures their interest; however, if you know the product you sell is wonderful, you must nevertheless persuade them if you want to learn how to read this book.

The book will help you connect with your customers, show them your product as the special formula, & make them feel as though they deserve it. You will also learn how to:

• Employ three distinct placement techniques to your advantage.

• Choose the appropriate market for your goods.

• Use the latest market trends to assist buyers.

• Make an immediate connection with the audience in order to provide value. 

6. Data Science from Scratch: First principle with python by Joel gurus

Data science from the ground up focuses on the fundamental foundation of data science and makes it intelligible to readers. It integrates seamlessly with the main instruments of data science and communicates their execution from the start, with an exposition of the ideas underlying the procedures.

The author stresses key hacking abilities for data scientists in this book and provides a chance to discover more about artificial processors and web mining to enhance data science understanding. In brief, the book covers every facet of data science in detail, providing a quick overview of what to expect from either a data career in engineering.

I hope this list of popular data science books has helped you in your quest of learning. That said, if you want to start from the ground up, you can always head to an online data science  course in Pune, offered by Learnbay. Visit the site for more information. 

Overview of SQL for Data Scientists

The most prevalent tool for analyzing data is SQL, so learning SQL would be a vital skill for professional data scientists. SQL for data science makes it possible to use the enormous amount of information currently stored in RDBMS for advanced actionable insights. You simply can only predict to carve out even a career in analytics but rather data science if you have picked up SQL. Register for a comprehensive data science course in Mumbai, to master the cutting-edge tools used by data scientists worldwide.

SQL Is a type of Programming Language.

SQL is a markup language whose origins can be traced back to the 1970s when IBM researchers invented relational databases. It is now a standard that would be recognized either by American National Standards Institute (ANSI) or instead International Standards organization (ISO)

Two essential qualities of SQL are its accessibility of use and its power: it allows data to be queried, altered, and aggregated to generate insights that guide business decisions.

• Specialized Language

Opensource.com describes a domain-specific dialect (DSL) as a language meant to be employed in the environment of a particular domain. A general-purpose language (GPL), on the other hand, is intended to serve a variety of business applications.

SQL's website is data management. DSLs are capable of taking advantage of each and every feature of the domain. They are indeed easier to understand, and master than GPLs and are tailored to the specific demands of domain developers and specialists.

• Open Source

SQL is provided commercially as Oracle and Azure SQL Server, along with open-source systems such as MySQL, PostgreSQL, etc. SQLite. The key distinction between open-source and paid SQL versions is support services. The latter is supported first by vendors, whilst the latter obtain upgrades, and changes from a user body, sometimes for free and sometimes for a cost.

According to DZone, a language, and DevOps source, companies that use open-source Mysql databases must create their own conceptions and customizations to accommodate their particular requirements. That's why it's essential to consider how well the user population for a precise SQL version can provide the assistance the organization will require.

• Query Language

An inquiry language is a way to obtain valuable data from a database. A "select" query is used to get data, while an "action" query is used to request extra operations on the query, such as edit, update, or "delete".

Data-driven communications provider TechTarget highlights the numerous uses of inquiries in SQL:

• Find relevant data by restricting criteria

• Compute or summarize data

• Tasks related to data management should be automated

What are the Difficulties of Using SQL in Data Science?

SQL covers most data scientists' analytics needs; however, for some applications, it can be more sophisticated than directly using a worksheet or other simple tool. In other circumstances, SQL can be too broad or lack the particular characteristics essential data science application demands.

• Data is hard to modify and convert to other formats.

Typical data science tasks such as quantitative tests, regression testing, and time series necessitate extensive data manipulation, which includes translating the data into multiple representations. While SQL can merge data from various tables, high-level data manipulation required by data science applications might be challenging with simple SQL commands.

• For Complicated Queries, it is more challenging to use than Python.

Python's panda package makes things much easier when writing and understanding complex queries. Python commands are more comprehensive and functional than SQL commands, which typically contain JOINS, aggregate operations, and subqueries. In particular, Python commands as libraries are built for specialized tasks, whereas SQL's are supposed to be applied widely.

All in all, SQL can be the best programming language for data analysis tasks, and there is no doubt about that. So it's high time to start learning SQL for your data scientist career and get certified by IBM, with the top data science course in Pune.