Team Sep 21, 2024 No Comments
Here’s an interesting fact: The world generates 402.74 million terabytes of data every day, which will bring the total data generated this year to around 147 zettabytes.
That’s an astonishingly large amount of data. This includes all the videos uploaded on YouTube, emails sent, texts shared, Tweets on Twitter, Snaps posted on Snapchat, and so on.
If we can collect, process, and analyze this raw data, we can make data-driven decisions and solve many real-world problems effectively. This has given rise to the hundreds of data science applications we see today.
In this post, we will explore some of the most amazing data science use cases across different industries. You will understand the impact of data science and how it’s shaping the future.
Every industry has realized the importance of data science. Organizations know how it can help them make data-driven decisions, optimize processes, reduce costs, improve customer experiences, or gain a competitive edge. Here are some of those applications of data science in various fields that have seen unparalleled results:
Data science helps educational institutions keep track of the performances of teachers as well as students. For instance, you can easily analyze test papers to understand how students are performing. Similarly, you can predict the final date of course completion or how many students will drop out by analyzing the pace of teaching, engagement, attendance, etc.
Duolingo is a great example of the application of data science in education. It’s a language learning app that analyzes the strengths and weaknesses of learners to adjust the lessons and exercises. This makes learning more engaging and effective.
Besides, educational institutions can analyze industry trends and design courses that teach the latest skills. This way, students will be relevant in this fast-changing world.
You have already seen the application of data science in e-commerce. The moment you open an online shopping app like Amazon, it recommends products that you like.
That’s personalization. Amazon analyzes vast amounts of data, like browsing behavior, purchasing history, product ratings, etc., to provide recommendations based on your preference. This way, Amazon increases sales and keeps users satisfied.
And have you seen the price of products keep changing on Amazon? Well, the e-commerce giant also uses data science algorithms for dynamic pricing, which lets it change prices based on factors like demand, competition, and market trends. This helps Amazon maximize revenue.
E-commerce platforms also use advanced algorithms and machine learning models for demand forecasting. This helps them maintain an optimal inventory and avoid situations of stockouts and overstock.
One of the biggest applications of data science in finance is fraud detection. Financial institutions use algorithms that identify unusual transactional patterns, prevent fraud, and protect their assets and reputation.
Data science also helps in algorithmic trading that uses computer programming to execute trades at precise moments, taking advantage of small price fluctuations. It analyzes market trends, identifies potential risks, and, most importantly, eliminates emotions from trades.
Other use cases include providing personalized financial services, evaluating the creditworthiness of loan applicants, analyzing the performance of different investment strategies, etc.
You can watch this video to know what are data science career opportunities in finance industry:
Data science helps retailers analyze customer data, identify useful insights, and find actionable ways to keep customers engaged and interested.
For example, retailers can offer personalized product recommendations based on purchase history. This not only makes customers feel valuable but also increases the conversion rates. A McKinsey report found that 76% of consumers are more likely to purchase from a brand that personalizes.
Also, retailers can analyze online reviews, email feedback, and social media comments to understand where they are lacking and how they can improve their products and services. Similarly, they can analyze customer demand using predictive analytics and ensure their store has the optimal stock.
A few years ago, you couldn’t have imagined that the healthcare industry would use technical analysts and mathematical calculations to such an extent that it would become a necessity.
But it’s happening. Nowadays, people are using smart wearables on their wrists to collect data about their health and keep their physicians informed on a real-time basis.
Using predictive analytics, hospitals can analyze patient data to identify patterns and predict future health situations for early diagnosis.
Data science also helps in areas like drug discovery, hospital management, medical imaging, etc.
Data science applications help in optimizing the supply chain process. For instance, companies can track their goods in real-time to monitor shipments, estimate delivery times, and reduce the risk of delays or losses.
Data science also helps optimize delivery routes by considering distance, weather, traffic, and unexpected events. This not only minimizes transportation costs and makes deliveries faster but also reduces fuel consumption and carbon emissions.
You want to know about the features of this smartwatch, so you search it on Google, and the whole internet knows it. You see ads for smartwatches on YouTube, Instagram, Facebook, and almost all the apps you use. You may find a good offer this way and make a purchase.
Well, that’s an application of data science in marketing called targeted marketing. Companies analyze customer behavior and preferences to tailor their marketing campaigns to a specific group. This increases conversion rates and customer satisfaction.
Marketing professionals also analyze social media conversations to understand customer sentiments. This helps them identify strengths and weaknesses, improve products or services, and retain customers.
The incredible data science applications mentioned above show that the data science market is booming. So, if you are interested in this field, you can learn industry-relevant skills and launch your data career.
To make learning easy and quick, you can enroll in a reputed certification program like Ivy Professional School’s Data Science Course with IIT Guwahati. This course will teach you essential skills like data wrangling, analytics, visualization, machine learning, deep learning, and GenAI from scratch.
It’s a 45-week live online course where you will be mentored by IIT professors and industry experts from companies like Amazon, Google, and Microsoft. Plus, you will work on 50+ projects, earn a certification from IIT Guwahati, and be job-ready in just 45 weeks. Visit the IIT data science course page to learn more about it.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Sep 19, 2024 No Comments
Data scientist or data analyst? What are the differences between them? Which one should you choose?
Well, those are common questions asked by people aspiring to become data experts. And I don’t blame them because these two fields are complementary and have several overlapping areas.
But at the same time, they have different roles, skill sets, and salaries that set them apart.
In this post, we will understand the difference between data scientists and data analysts. We will also see what skills they should have and what salary they earn.
These two fields have interconnected roles and are like two sides of the same coin. Data scientists ask the right questions, whereas data analysts find specific answers to those questions.
The role of data scientists is exploratory. They look for hidden patterns and develop models that predict future events. Whereas the role of data analysts is more descriptive, as they focus more on providing a description of what has already happened by looking at historical data.
A data scientist’s work involves predictive modeling, deep learning, artificial intelligence, and working with massive datasets. They often deal with uncertainty and try to uncover new patterns or relationships in data. On the other hand, data analysts analyze data and generate reports that support decision-making. Their skills revolve around data visualization, querying, and basic statistical analysis.
Both of them play a crucial part in helping businesses make data-driven decisions, but data scientists typically deal with more complexity and have a higher earning potential. We will see their salary in more detail, but let’s first understand what skill sets they need.
Related: Data Engineer vs. Data Scientist
Since they have different roles, they need different skill sets. Let’s understand this in detail:
A data scientist may have to work with complex datasets, build predictive models using ML or statistical methods, use programming to develop algorithms, evaluate models, and provide actionable insights to business stakeholders. Here are the skills they usually need:
If you want to know more about topics you should study to become a data scientist, you can check out this data science syllabus. It’s followed by Ivy Professional School’s IIT-certified Data Science Certification Course.
Also, watch this video to understand the data science journey for freshers:
A data analyst’s primary job is to analyze and visualize data to help businesses make informed decisions. They may collect and organize data, analyze it with statistical methods, identify trends, and generate reports and dashboards. Here are crucial skills they need:
You can watch this video to understand how data analysis is actually done to solve real-world problems. This is Mamta Mukherjee, an Ivy Pro student who has analyzed Netflix movies and TV shows using Excel to find valuable insights:
Businesses need data to gather important insights, enhance business performance, and evolve in the market. So, both data scientists and data analysts are in high demand. But there is a difference in the salary they earn.
The average data scientist’s salary is ₹12,00,000 per year in India. If you consider the cash bonus, commission, tips, etc., then the additional pay is ₹1,80,000 per year, which makes the average total salary of a data scientist ₹13,80,000 per year.
It’s obvious experienced data scientists earn more salary. For instance, a senior data scientist with four years of experience may earn between ₹17 lakhs to ₹31 lakhs per year.
The salary also depends on the company size. Bigger and established companies often pay more. For example, the salary of a data scientist at IBM is ₹8 lakhs to ₹20 lakhs per year, whereas in Amazon, the salary can vary between ₹9 lakhs to ₹25 lakhs per year.
The average annual salary of a data analyst in India is ₹7,00,000. Considering the cash bonus, commission, and tips, the average total pay becomes ₹8,00,000 per year.
So, it’s clear data scientists usually earn slightly more than data analysts. However, the salary depends on various factors like experience, company size, industry, location, etc. If you are a senior data analyst with four years of experience, your average annual salary could be between ₹8 lakhs to ₹17 lakhs per year.
Also, there are companies like Accenture, Amazon, Cognizant Technology Solutions, Deloitte, Google, etc., who can pay you anything between ₹4 lakhs to ₹20 lakhs per year.
But what about the future? Well, the global data analytics market is projected to grow from $51.55 billion in 2023 to $279.31 billion by 2030, with a CAGR of 27.3%. So, you can expect increasing opportunities in the job market.
Now that you know the difference between data scientists and data analysts, you can make an informed decision about what you should become.
As I said, both data scientists and data analysts are in great demand. So, if you want to land your career in any one of them, you should get a certification from a reputed institution like Ivy Professional School.
Ivy Pro is a top-ranking data science, analytics, and AI course provider in India with a legacy of 16+ years. The institute has several courses made in partnership with IIT Guwahati and IBM.
Also, Ivy Pro has trained over 29,500 learners and has helped them get jobs in Amazon, Cognizant, Deloitte, Accenture, IBM, etc. Visit this page to learn more about Ivy Pro’s courses.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Sep 05, 2024 No Comments
Joining a data engineering course is the best way to launch your data career.
It not only helps you learn industry-relevant skills like how to collect, store, and process data but also gain practical experience with projects and case studies. Some courses even help you land jobs through resume-building sessions, soft skills classes, and mock interviews.
But with so many courses out there, finding the right one can be overwhelming.
That’s why I have put together this list of the six best courses for data engineering to help you get started. Keep reading to find the course that suits your needs best.
A data engineer designs, develops, and maintains the systems and infrastructure needed for processing, storing, and analyzing massive datasets. They are the backbone of data-driven organizations that need data to make smart decisions. So, here are some courses that can kickstart your career as a data engineer:
This is one of the best data engineering courses that makes you a job-ready candidate. Provided by Ivy Professional School and E&ICT Academy, IIT Guwahati, this course is your opportunity to learn from IIT professors and experts from Amazon, Google, Microsoft, etc.
You can attend this 45-week course live online or in a physical classroom. Either way, you will get to interact with industry-expert instructors and clear your doubts.
The course covers high-value data engineering, AI, and ML skills with tools like Azure, Hive, MongoDB, Spark, and more. You will work on 30+ real-life projects where you will implement your knowledge and gain practical experience.
The course also provides you with essential job-oriented skills such as resume building, LinkedIn profile building, networking, communication, and success in interviews. And after you complete the program, you receive a reputed certificate from E&ICT Academy IIT Guwahati, IBM, and NASSCOM.
This 16-course series by IBM on Coursera is one of the most comprehensive data engineering certification programs. It lets you learn at your own pace and finish the courses in 6 months at a rate of 10 hours a week.
You don’t need any prior data engineering experience, as experts from IBM will teach you everything from scratch. You will learn in-demand skills like NoSQL and Big Data using MongoDB, Cassandra, Cloudant, Hadoop, Apache Spark, etc. The program also teaches you how to implement ETL & data pipelines, build data warehouses, and create BI reports and interactive dashboards.
The specialization also gives access to soft skill sessions, resume review, interview preparation, and career support. Finally, you will earn a valuable IBM certification upon completion of the courses.
This 32-week course, provided by Simplilearn and Purdue University Online, is best for professionals. It can help you master data engineering and make successful career transitions, boost career growth, or get salary hikes.
The 150+ hours of core curriculum are delivered by professionals with decades of industry experience. You will learn useful skills like real-time data processing, data pipelining, big data analytics, data visualization, data protection, data governance, etc. You also learn tools like Python, SQL, NoSQL, Snowflake, AWS, Azure, etc.
The course lets you work on 14+ projects and multiple case studies so that you can implement your knowledge in real-world business problems. Upon completion of the course, you earn a joint completion certificate from Purdue University and Simplilearn.
This is one of the best data engineering certifications, and it includes six courses provided by Google Cloud on Coursera. It’s an intermediate-level course, so you will need an understanding of query languages like SQL and how to develop apps using common programming languages.
The program starts with machine learning fundamentals, covers modernizing data lakes and data warehouses, and teaches how to build batch data pipelines. You also learn to build resilient streaming analytics systems and explore topics like smart analytics and AI.
Google Cloud provides all the training and certifications, helping you gain skills and build credibility. The course also helps you prepare for the Google Cloud Certification exam.
Related: Data Engineer vs. Data Scientist
This course by Udemy teaches the basics of data engineering, focusing on building data pipelines using tools like SQL, Python, and Apache Spark. This is an online course with 56 hours of recorded videos, two articles, and one downloadable resource.
You will learn how to write and optimize SQL queries, use Python for data processing with Pandas, build and troubleshoot data engineering applications, work with Spark SQL for big data processing, and set up and tune Spark environments on Google Cloud.
The data engineering course is perfect for IT students, database developers, BI developers, and professionals looking to transition into data engineering.
This is a two-month online program by Udacity that consists of 7 courses. The courses cover a range of key areas, including building data infrastructure, managing large datasets, and optimizing data workflows.
You will learn how to design and implement data models, construct efficient and scalable data warehouses, build ETL (Extract, Transform, Load) pipelines, and understand data lakes. Additionally, the course provides hands-on experience with tools like Apache Spark, Apache Airflow, and AWS. This way, you can apply what you learn in real-world scenarios.
This course isn’t suitable for absolute beginners. You would need a basic knowledge of relational databases, command line interfaces, and Amazon Web Service, as well as intermediate-level knowledge of Python and SQL.
Next, you can read this post to know how you can become a data engineer or watch this video:
Joining a comprehensive course lets you become an expert in a short time. Whether you are a beginner or a professional, you can go through the above best data engineering courses carefully to see which one fits your requirements. They will surely help you gain a deeper understanding of data engineering concepts, learn the industry’s best practices, stay updated with the latest technologies, and accelerate your career.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Sep 03, 2024 No Comments
“Data engineer or data scientist? Who earns more? Which one is better?”
If you have these questions in mind, you are in the right place.
You see, data engineers build and maintain architectures required for data collection and storage, whereas data scientists analyze the data to find insights and help organizations make smart decisions.
In this post, I will discuss more about the differences between data engineers and data scientists.
You will know what they do, what skills they use, how much they earn, and what career opportunities they get. This will help you decide which career path you should choose.
Though data engineers and data scientists both work with data, there are many differences in their roles and responsibilities. Let’s understand that…
A data engineer basically designs, builds, and maintains the systems and infrastructure that collect, process, and store large datasets.
They create data pipelines, build ETL processes, and manage databases and data warehouses. All this is to make sure the data is clear, reliable, and accessible for analysis.
Data engineers work closely with data scientists, business analysts, and other stakeholders to understand the organization’s data requirements and fulfill them.
They use tools like Apache, Hadoop, Spark, Kafka, SQL, and NoSQL databases (e.g. MongoDB). Their work involves a lot of coding and includes programming languages like Python, SQL, Scala, and Java.
A data scientist simply analyzes data collected by data engineers and finds valuable insights that can solve business problems. They may use statistical methods, machine learning and AI algorithms, predictive models, etc., to solve specific business problems.
They also have to communicate their findings through visualizations like dashboards, charts, graphs, etc. The purpose is to help non-technical stakeholders identify trends and patterns in complex data and make smart decisions.
Data scientists use tools like Adv. Excel, Python, R, SQL, TensorFlow, PyTorch, scikit-learn, and data visualization tools like Tableau and Power BI. Their work is more about statistics, maths, analytical thinking, and problem-solving.
Since their work is different, it’s obvious they will have different sets of skills (although there may be some overlaps):
To be a successful data engineer, you need to learn these skills:
Ivy Professional School’s IIT-certified Cloud Data Engineering course not only helps you learn the above in-demand skills but also lets you work on industry projects to gain hands-on experience. This is a 45-week live online course where you learn to use tools like Azure, Hive, MongoDB, Spark, etc., from industry experts.
You also get mentored by IIT Guwahati professors and professionals in companies like Amazon, Google, Microsoft, etc. Besides, you will earn certifications from IIT Guwahati, IBM, and NASSCOM to boost your credibility as a data engineer.
And to understand what topics you must study to become a data engineer, you can go through the latest data engineering syllabus.
You need to gain the following skills to become a successful data scientist:
If you want to master the above skills, you can join Ivy Professional School’s IIT-certified Data Science and AI course. Again, it’s a 45-week live online course, so you can join from anywhere, engage with instructors, and solve your doubts instantly.
You will be trained by experts and exposed to real-world problems through 10+ projects, 40+ case studies, and 50+ assignments. Ivy also provides CV-building sessions as well as mock interview sessions to make you completely job-ready.
And to understand what topics you must study to become a data scientist, you can go through the latest data science syllabus.
Let’s look at the most asked question about this topic: “Do data scientists earn more than data engineers?” I will simply provide you the average numbers, but remember that the salary is highly influenced by factors like location, industry, company size, etc.
The average salary of a data engineer in India is ₹8,50,000 per year. That’s the base pay amount. If you consider the bonuses and commission, the average total pay for a data engineer becomes ₹9,50,000 per year.
The salary increases as you gain more experience. For instance, the salary of a senior data engineer with 2-4 years of experience can be anything between ₹11 lakhs to ₹24 lakhs per year. And if you are the lead data engineer with 5-7 years of experience, you can expect to earn ₹ 18 lakhs to ₹32 lakhs per year.
The average salary of data scientists in India is ₹12,00,000 per year (base pay). And again, if you consider the bonuses and commission, the average total pay of data scientists is ₹13,60,000 per year.
Senior data scientists with 2-4 years of experience can earn between ₹16 lakhs to ₹30 lakhs per year. Whereas lead data scientists with 5-7 years of experience can earn ₹22 lakhs to ₹38 lakhs per year. So, it’s clear that data scientists earn more than data engineers.
The world is generating more data than ever. In fact, we produce 402.74 million terabytes of data every day. This means businesses need skilled individuals to make sense of the massive amounts of data. And that’s why both data engineers and data scientists are in high demand.
Data engineering provides you amazing career opportunities. The global big data and data engineering services market is expected to be valued at USD 163.80 billion by 2030, growing at a CAGR of 15.48%.
This type of growth means there will be more demand for skilled data engineers in the coming years. And it’s already happening: the demand for data engineers with over six years of experience increased from 27% in 2023 to 38% in 2024.
You will find more and more opportunities in data warehouse engineering, ETL development, data pipeline architecture, big data engineering, and cloud data engineering roles. All these are focused on the technical aspects of data management, like data extraction, transformation, loading, data integration, and data quality.
Related: Is Data Engineering a Good Career?
Data science is one of the high-income skills that is in huge demand. You can open a job platform like GlassDoor or LinkedIn, and you will find over 10,000 data science jobs in India.
And the demand is going to explode in the coming days. This is evident from a report that predicts that the global data science market is expected to grow from $133.12 billion in 2024 to $776.86 billion by 2032.
Data science provides you many opportunities for growth and advancement. You can be a data analyst, business intelligence analyst, machine learning engineer, data storyteller, and so on. The rise of AI will open new applications like natural language processing, computer vision, predictive analytics, etc.
So, both data engineering and data science provide exciting opportunities to grow and succeed in your career. Now comes the most important question…
Related: 7 Reasons to Become a Data Scientist
There is no right answer to that. That’s because what may be better for others may not be a better choice for you. So, the real question is- what do you enjoy, and what kind of work excites you?
If you love coding, solving technical problems, and building systems, data engineering might be the better option for you. As a data engineer, you will design, build, and maintain architectures like data pipelines and databases to collect, store, and organize data.
On the other hand, if you love playing with numbers, analyzing data, and finding useful insights, data science could be a better choice for you. As a data scientist, you will basically study a problem from different angles, think analytically, develop solutions, communicate it to business stakeholders, and help businesses make smart decisions.
Whatever excites you the most is the right career option for you.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Aug 08, 2024 No Comments
Data engineers are the professionals who build the systems and infrastructure that help organizations collect, store, and analyze data.
Since data-driven decision-making has become a necessity, the demand for data engineers has been growing rapidly. After all, the power of data helps companies gain more customers and boost revenue.
So, if you are a skilled data engineer, you can expect an amazing career with endless opportunities to learn and grow. In this post, we will discuss more about what data engineers do, how much they earn, and how to become a data engineer.
Data engineers are like architects of the data world. They build systems that collect data smoothly from its source, like websites, apps, or sensors, and store it in places like databases and data warehouses.
It’s the data engineers who help data scientists and analysts to easily access the data they need. So, they have to make sure that the data is accurate, consistent, and reliable for analysis.
They also have to ensure that systems can handle massive amounts of data and grow as the business grows. And they have to make this process efficient by optimizing the systems for speed and performance.
Various industries like technology, finance, healthcare, and e-commerce rely on skilled data engineers to build and maintain their data infrastructure. Here are some specific tasks that data engineers perform:
Now, let’s take a look at why data engineering is such a great career.
The high demand and the specialized skills of data engineers translate into competitive salaries. For instance, data engineers in India earn an average salary of ₹8,62,000 per year.
But that’s just the average figure. Factors like years of experience, location, and company size affect the salary. For example, Glassdoor reports senior data engineers with 2-4 years of experience earn salaries between ₹ 12 lakhs to ₹ 25 lakhs per year. Whereas lead data engineers with 5-7 years of experience earn salaries between ₹ 18 lakhs to ₹ 32 lakhs per year.
Similarly, the average annual salary of data engineers in Bangalore is ₹11 lakhs, whereas in Gurgaon, it is 11.9 lakhs, and in Mumbai, it is ₹9.6 lakhs. Larger companies with a heavy reliance on data (like tech, finance, and e-commerce) often pay more than smaller companies or those in less data-centric sectors.
If you are considering a career in data engineering, it’s a good idea to research the salary trends in your specific location and industry. Now that we know the perks of being a data engineer let’s understand how to become one.
Here are 6 essential steps to becoming a cloud data engineer:
Although it’s not necessary, if you have a bachelor’s degree in computer science, engineering, math, or a business-related field, it can really help. It will give you a strong foundation and help you understand the basics of programming, databases, algorithms, etc.
But if you don’t have a formal degree, you can join certification courses. This is, in fact, a great way to launch your data career. Courses not only help you learn industry-relevant skills but also help you gain hands-on experience, stay updated with industry trends, and be a credible data engineer.
And if you join top-rated courses, like Ivy Professional School’s IIT-certified data engineering course, you will be mentored by IIT professors, receive career guidance, and get lifetime placement support.
A data engineer needs a set of technical skills to do their tasks effectively. Here are some of them:
Read this post to learn about the latest data engineering syllabus. However, try to stay updated with new trends and technologies. Things change fast, so you must keep learning new skills even after you become a skilled data engineer.
Now, just technical skills aren’t enough to become a data engineer. You also need to sharpen your soft skills to do your job effectively. Here are some of the essential ones:
A good portfolio is a powerful tool that can help you land your dream job. It basically shows the projects you have worked on and how you have applied your skills to solve real problems.
The portfolio helps you show some of your unique work and stand out from other job applicants. Since employers get a direct example of what you can do, you become trustable.
Here are some tips to build your portfolio:
The key to building a solid portfolio is practicing good projects. That’s why Ivy Pro School helps learners work on industry and capstone projects in the data engineering course with E&ICT Academy, IIT Guwahati. It not only helps in the portfolio, but learners also gain practical experience and confidence to solve real business problems.
Now, you are ready to apply for jobs. But first, create a good resume that showcases your technical skills, experience, and projects. You can go through the company’s job description, understand what skills they need, and tailor your resume and cover letter accordingly.
You can go to online job portals like LinkedIn, Indeed, Glassdoor, and company career pages. If there aren’t any open positions at the moment, you can set up job alerts for data engineering roles.
Don’t stop networking. Connect with professionals in your field on LinkedIn. You can attend industry events, webinars, and meetups. A good network can lead you to surprising opportunities.
You also need to prepare for interviews. Just revise what you have learned about data structures, algorithms, and system design. Research common data engineering interview questions and prepare good answers. Also, go through your projects and get ready to discuss your problem-solving approaches.
You can’t avoid this step if you want to become a good data engineer. Like most other fields, data engineering is rapidly changing, so you must continuously learn and update yourself.
Stay up-to-date with the latest tools, technologies, and trends by reading industry blogs, attending webinars, and taking online courses.
You can follow data engineering experts on LinkedIn and spend just 20 minutes every day on the platform. You will be surprised by the quality of knowledge shared daily on the platform.
Now you know how to become a data engineer. It’s a process of learning skills and practicing them consistently. And it’s all worth it. The demand for data engineers in India is only going to increase in the years to come, so now is a great time to get started.
You can join Ivy Professional School’s IIT-certified data engineering course. In this live online course, you will learn in-demand skills, get coached by IIT professors, work on real-world projects, and become an ideal job candidate in 45 weeks. To learn more about it, visit the course page.
Team Aug 02, 2024 No Comments
You will find numerous data science courses in Kolkata. Some are good, some are just average, and some are exceptionally good.
Choosing the right course is a crucial step. You have to consider factors like course syllabus, faculty expertise, projects and case studies, placement support, etc.
This blog post will help you find the perfect data science institutes and training programs. It lists some of the exceptional data science courses in Kolkata that can help you launch your data career, even if you are a beginner.
With these courses, you can learn in-demand skills, become a certified data scientist, and land high-paying jobs in MNCs. But let’s first understand…
You have to admit data science is a hot career. Currently, the average salary of a data scientist in India is an amazing ₹12,85,000 per year. And if you go to cities like Bangalore, Hyderabad, or Gurgaon, the average salary is around ₹14,00,000 per year. That’s huge, right?
As of June 2024, there are more than 17,000 data science job openings in India at top companies like Google, Microsoft, and Accenture. And things are only getting better with time. The global data science market size is growing with a CAGR of 24.7% and is projected to grow from $133.12 billion in 2024 to $776.86 billion by 2032.
So, if you have the skills and expertise, you will have good pay, job security, and a fulfilling career. You will create a real positive impact by helping businesses and organizations make smart, data-driven decisions. The following data science courses in Kolkata will help you achieve this.
Ivy Pro’s executive data science course is one of the best training programs in Kolkata. Made in partnership with E&ICT Academy, IIT Guwahati, this course helps you become an IIT-certified data scientist.
The live online training lets you join classes from anywhere, interact with instructors, and resolve your doubts instantly. Ivy also offers in-person classes in Kolkata. You will be coached by IIT Guwahati professors and experts from companies like Amazon, Google, and Microsoft.
The course is designed by industry experts and covers essential tools like Excel, Python, SQL, Power BI, Keras, Tensorflow, OpenCV, and Tableau. You will learn in-demand skills like data wrangling, analytics, visualization, machine learning, deep learning, and GenAI.
The course lets you go beyond theoretical knowledge. With 10+ projects, 40+ case studies, and 50+ assignments., you implement your knowledge, create an impressive portfolio, and gain the confidence to solve real business problems.
What’s more, Ivy Professional School helps you develop your soft skills, build your resume, prepare for interviews, and make a social presence. After 45 weeks of training, you become an ideal job candidate for high-paying roles in major tech companies.
Coursera’s IBM Data Science Professional Certificate course series covers skills like data analysis, data visualization, statistical analysis, predictive modeling, machine learning, generative AI, etc.
You will also learn to work with the latest data science tools and libraries like Python, SQL, Pandas, NumPy, Scikit-learn, and Matplotlib. You will be taught by industry experts from IBM who have received high ratings for the quality of their teaching style.
Apart from an updated curriculum, the course covers real-world projects to help learners get practical experience and build a portfolio. It’s an online course that you can easily attend from your home.
You can complete this data science course in Kolkata at your own pace. With 10 hours a week, you can finish it within 6 months. And after completing the course, you will earn a certificate from IBM to boost your credibility as a data scientist.
Simplilearn’s data science course, made in collaboration with IBM, is a suitable option for both freshers and professionals. You get live sessions from industry experts as well as masterclasses from IBM professionals.
This is an online course that you can access from anywhere. It explores basic topics like programming essentials before moving to advanced ones like Python, Tableau, Hadoop, Spark, machine learning, generative AI, etc. You learn useful skills like database management, data analysis, data visualization, large language models, conversational AI, etc.
You will receive a valuable certificate from both Simplilearn and IBM after completing the course. Besides, Simplilearn’s job assist program provides you with career mentoring and helps you land your dream job.
Udemy’s Data Science Course offers comprehensive training covering math, statistics, Python, Tableau, machine learning, deep learning, etc. It’s again an online training that you can access from anywhere in Kolkata.
The course has 31 hours of video, 93 articles, and 541 downloadable resources. Besides, the 137 coding exercises help you apply everything you have learned to real-life situations and gain a deeper understanding.
The program doesn’t require prior experience as it starts from the basics. So, it’s perfect for students who want to launch their data careers. You will learn everything you need to become a data scientist and make your resume strong.
This data science course in Kolkata has got 4.6/5 ratings from over 140,000 learners on Udemy. So, it’s a good one.
Massachusetts Institute of Technology’s Applied Data Science Program is a reputed course that is taught by highly experienced MIT faculty. This is a 12-week live online training, so you can attend it from anywhere in Kolkata and interact with instructors.
You will learn how to leverage AI to make data-driven decisions. The course covers in-depth topics like machine learning, deep learning, recommendation systems, ChatGPT, applied data science with Python, generative AI, etc. You learn tools like Python, NumPy, Keras, TensorFlow, etc.
You also work on 6 projects under the guidance of industry experts to gain hands-on experience and solve business problems. You also get live mentorship from data science practitioners on weekends to gain more clarity.
Finally, you will earn a professional certificate from MIT upon completion of the course.
Ivy Professional School stands out as the best data science training institute in Kolkata for several reasons. Since 2008, Ivy Pro has been a top-ranking data science, analytics, and AI education provider. This success is due to its unmatched course quality and smart teaching approach.
Ivy Pro has the top 1% faculty, with instructors who bring decades of industry experience and know how to make learning engaging and memorable. Besides, learners also get mentored by experts from prestigious institutions like IIT and companies like Amazon, Google, and Microsoft.
The course curriculum is designed to meet industry standards and includes practical training on essential tools like Python, SQL, and Tableau. Students work on real-world projects, gaining hands-on experience to solve real business problems.
The institute also collaborates with prestigious organizations like IIT Guwahati, IBM, and NASSCOM to ensure its programs are up-to-date and effective. No wonder that Fortune 500 companies like Tata Steel, Accenture, ITC, Cognizant, and Capgemini trust Ivy Pro’s training and actively recruit Ivy Pro graduates.
In the last 16 years, Ivy Professional School has helped over 29,500 learners get placed in more than 400 organizations. If you want to become the next success story, you can join Ivy Pro’s IIT-certified data science course and achieve your career goals.
Becoming a data science expert isn’t that easy. You will need a comprehensive syllabus and experienced mentors. We have listed some of the best data science courses in Kolkata. Go through their course pages carefully to decide which one best meets your requirements. After all, choosing the right course can significantly impact your career, giving you the skills to become a competitive job candidate.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Jun 07, 2024 No Comments
Updated on 9th August, 2024
Want to be a data scientist? Well, it’s a promising career choice with good job opportunities. As a skilled data scientist, you can expect a good salary, job security, work satisfaction, and, ultimately, a chance to create a real impact in the world.
But where do you start? What should you study? Which institute should you join?
Don’t worry, I have got you covered. In this blog post, I am going to share a list of the 5 best institutes for data science in India. We will take a quick look at what makes each one unique so you can choose the best fit for you. But, let’s first see…
Different institutes have different syllabi for their data science courses. Before you choose an institute, you must go through the syllabus to make sure it’s updated and relevant to present industry needs. To give you an example, here is the latest, industry-relevant syllabus followed by Ivy Professional School in its Data Science and AI Certification course.
Here are some of the best data science institutes you can choose for mastering this high-income skill in India:
Ivy Professional School has been a top-ranked data science, data analytics, and AI upskilling provider since 2008. Ivy offers the Executive Data Science and AI Certification course in partnership with E&ICT Academy, IIT Guwahati.
In this program, you get mentored by IIT professors and experts from Amazon, Google, Microsoft, etc. You learn skills like data analytics, visualization, ML, Cloud, AI, GenAI, etc. Apart from learning industry-relevant skills, this 45-week course helps you complete 50+ real-world projects and build an impressive portfolio. The live online classes and live doubt-clearing sessions help you build solid foundations.
You also get placement assistance that helps you with your CV and interviews. All this helps you land high-paying data science jobs in top MNCs. No wonder Ivy is at the top of the list of the best institutes for data science.
IIT Madras is a premier institute known for its excellence in engineering and applied sciences. This institute offers a 4-year Bachelor of Science degree in Data Science and Applications. This is a hybrid course with pre-recorded sessions, weekly online assignments, and in-person quizzes.
This course is good for both students and working professionals, and it will help you learn ML foundations, deep learning, computer vision, large language models, big data, etc. For admission, you have to apply for an in-built qualifier process in which you will be taught for 4 weeks and then asked to qualify for an admission exam.
One of the best institutes to learn data science, IIM Calcutta is a prestigious business school recognized for its flagship MBA programs. It offers the Advanced Programme in Data Sciences (APDS) that helps students learn various tools and techniques for managing, analyzing, and interpreting data. The course covers tools like Tableau, Python, R, SPSS Modeler, etc.
This course is best for working professionals and young managers who want to learn real-world data science skills. This one-year course is conducted in the form of 3-hour online sessions every Sunday. To apply for this course, you must be a working manager with 3+ years of experience or a graduate/postgraduate in any discipline with 50% marks.
Scaler is an ed-tech platform that helps techies upskill with its various courses. It offers a Data Science and Machine Learning program that helps you learn essential skills to succeed in the field. This online program, which runs for 11 to 15 months, focuses on tools like Git, TensorFlow, PySpark, PyTorch, Kafka, Hive, etc.
With 1:1 mentorship, focus on projects and case studies, and career counseling, Scaler ensures you become an expert at solving problems and making smart business decisions. The course is best for freshers and working professionals who have a good understanding of languages like Python or have a degree in maths, engineering, or statistics.
IIT Delhi is one of the best technical institutes in India, and it is recognized for its research and innovation. The institute offers the Advanced Certificate in Data Science and Decision Science course. It focuses on skills related to data handling, analytics, cognitive sciences, various data science tools, etc.
This is a live online program that spans a total of 12 months, providing 150 hours of teaching. The program is best for professionals and leaders who want in-depth knowledge of decision-making with data. Selection for this course is done by reviewing applications and conducting personal interviews.
And there you have it! A glimpse into some of the best institutes for data science out there. Hopefully, this gives you a starting point to explore which program best aligns with your goals, learning style, and budget.
If you are looking for the top data science institute, you can go for Ivy Professional School. Ivy has over 29,000+ alumni who are working in leading roles in 400+ organizations. Apart from expert faculty, Ivy partners with IIT Guwahati, IBM, and NASSCOM to deliver top-notch learning programs.
Whether you are a student or working professional, Ivy can help you learn in-demand data science skills, complete capstone projects, get practical experience, build your portfolio and resume, prepare for interviews, network with alums, and finally get your dream job.
Visit this page to learn more about Ivy’s Data Science and AI course.
Is data science a well-paying job?
Yes, data science is a well-paying job. Skilled data scientists are in high demand, and companies are willing to offer competitive salaries to attract top talent. In India, the average salary of a data scientist is ₹13,00,000 per year. Senior data scientists with 2-4 years of experience can even earn between ₹17 lakhs to ₹30 lakhs per year. That’s a huge number, right?
What’s the best data science institute in India?
There are many great data science institutes in India, but Ivy Pro School stands out for its comprehensive courses, experienced faculty, and strong track record of student success. Since 2008, Ivy Pro has been a top-ranking data science, AI, and data analytics upskilling provider. This institute helps you work on real-world projects, ensures your holistic development, and gives you lifetime placement support so that you have a bright career.
Is data science a stressful job?
It depends on you. If you love playing with numbers and solving real business problems with data, you may not feel that much stress. However, as it happens in most jobs, there can be pressure to meet deadlines and deliver results. Or you may get stuck in an error for days. But if you love the job and like to face challenges, you will enjoy it.
Which city is best for data science in India?
Cities like Bangalore, Hyderabad, Gurgaon, and Mumbai are well-known for their tech industries and offer many opportunities in data science. Kolkata is also emerging as a strong hub for data science education and jobs, especially with top institutes like Ivy Pro School providing quality training.
Is data science still in demand in 2024?
Absolutely! Data science continues to be in high demand in 2024, and it will only get bigger with time. For instance, the global data science market is projected to expand from $133.12 billion in 2024 to $776.86 billion by 2032. So, the need for skilled data scientists is going to increase in the coming days.
Which college has the best placement for data science?
Several colleges in India offer good placements for data science. The above blog post talks about such institutes in detail. Ivy Professional School is one such institute that has helped over 29,500 learners get placed in some of the biggest companies like Honeywell, Samsung, PWC, IBM, HSBC, Amazon, Cognizant, etc. Ivy Pro has strong industry connections and provides lifetime placement assistance, making it easier for students to land jobs.
Eeshani Agrawal holds an MS in Civil Engineering from Texas A&M University (USA) and has over 16 years of experience in data visualization, storytelling, and analytics. She has consulted for top engineering, manufacturing, and consulting firms worldwide and has coached over 9,000 professionals and students from leading institutions like IITs, IIMs, and ISI.
Team May 27, 2024 No Comments
You want to become a data engineer? That’s a smart choice because data engineers are in high demand right now.
And they earn impressive salaries. In fact, the average annual salary for a data engineer is ₹11,00,000 in India (Glassdoor).
But how do you break into this promising field? Well, you need a solid foundation. And that starts with an effective, up-to-date data engineering syllabus.
The syllabus is the roadmap that guides you through the essential skills and knowledge you need to land your dream job. Since technology is rapidly evolving, you need a relevant curriculum that focuses on the latest tools, techniques, and best practices.
In this blog post, we will explore the latest data engineering course syllabus. This will help you understand what you need to study and what skills you should develop in 2024 to step into this promising career path.
Data engineering is the field of study that involves building and maintaining data systems that collect, store, and manage data in an organization. This field is a mix of software engineering, database administration, and data science skills.
Data engineers ensure that the right data flows seamlessly from various sources into a centralized location like a data warehouse. This becomes possible because of the designing and implementing of data pipelines that extract, transform, and load (ETL) data from databases, APIs, sensors, and other sources.
Data engineers are also responsible for ensuring the quality and reliability of data. This involves cleaning and validating data, handling missing values, and addressing inconsistencies. They also implement data governance policies to ensure data privacy, security, and compliance with regulations.
Data engineers work closely with data scientists and analysts to understand their needs and design data models that are optimized for analysis and reporting. They may also develop custom tools and applications to streamline data workflows and automate repetitive tasks.
This way, data engineers play a crucial role in helping organizations utilize the power of data and gain a competitive edge in today’s fast-changing market. Now that you understand the basics of data engineering, let’s move on to the next section…
Here is the latest data engineering course syllabus. It is divided into four major sections focusing on four primary topics- SQL, Python, Big Data Processing, and Azure Cloud Engineering.
The following industry-relevant syllabus is strictly followed in the Data Engineering Certification course by Ivy Pro School, which is made in partnership with E&ICT Academy IIT, Guwahati.
If you want to learn data engineering and gain practical skills, you can join the course. It’s a live online program, so you can learn from anywhere. We will talk more about the course later. Let’s see the syllabus first:
Here’s an overview of the SQL for Data Engineering section:
This section of the data engineering syllabus provides students with a comprehensive understanding of SQL, from basic to advanced levels.
It begins with foundational SQL queries, including SELECT statements, filtering, and sorting data. Students also learn to clean and modify data, covering essential operations like updating, transforming, and deleting data while handling errors and validating results.
The course then progresses to more complex topics such as data aggregation, advanced data filtering with pattern matching, and the use of window functions.
Next, students explore working with multiple data tables through various JOIN operations and conditional logic with CASE statements.
Advanced topics include creating and managing databases with DDL statements and developing user-defined functions and stored procedures to automate SQL operations.
Throughout the section, students engage in hands-on exercises and case studies using real-world datasets from industries like eCommerce and retail to apply their SQL skills.
Here’s an overview of what happens in the Python Essentials for Data Engineering section:
The second section of the data engineering syllabus introduces students to Python programming, with a focus on its application in data engineering tasks. Starting with the basics, students learn about Python’s data types, variables, and basic operations.
The course then steps into data structures such as lists, dictionaries, and tuples and shows how to manipulate them using Python’s powerful libraries, particularly Pandas. Students are taught to write and use functions and modules, enabling them to create reusable code.
A significant part of the section is dedicated to data wrangling with Pandas, where learners practice creating, cleaning, transforming, and aggregating data within DataFrames.
Additionally, the course covers API interactions, allowing students to fetch and process data from web APIs and database connectivity using SQLAlchemy to perform CRUD operations.
Error handling and debugging are also emphasized, ensuring students can identify and resolve common issues. And finally, hands-on projects throughout the section help solidify these skills.
Here’s what happens in the third section of the data engineering syllabus:
The Big Data Processing section offers a comprehensive overview of big data technologies and their applications in data engineering. The course begins with an introduction to the fundamental concepts of big data and explores key technologies such as Hadoop, Apache Hive, and Apache Spark.
Then, students learn about the Hadoop ecosystem, including HDFS and MapReduce, and gain practical experience in data storage and processing using Hadoop.
The course then covers Apache Hive, teaching students to query large datasets using HiveQL and apply these skills in hands-on projects.
Apache Spark is introduced next, with a focus on its architecture, RDDs, and DataFrames, and students learn to process data in real-time using Spark. The section also addresses data ingestion and storage techniques, highlighting the use of NoSQL databases like MongoDB.
In the final section, students explore real-time data processing with Kafka and its integration with Spark. They complete practical projects that emphasize building and managing real-time data pipelines.
Here’s what happens in this fourth and final section of the data engineering syllabus:
The Azure Cloud Engineering section provides an in-depth understanding of Microsoft Azure and its application in data engineering.
Students begin with the fundamentals of Azure, including an overview of its services, infrastructure, and security concepts such as Azure Active Directory and role-based access control.
The course covers the creation and management of Azure virtual machines, along with the use of Azure storage services like Blob, Queue, and Table for efficient data storage and retrieval.
Advanced topics include building end-to-end data pipelines with Azure Data Factory, which involves data movement, transformation, and integration, and an introduction to Azure Databricks for collaborative data processing.
Real-time data streaming is also covered, focusing on Azure Event Hubs and its integration with Azure Data Factory.
The section addresses hybrid cloud scenarios, teaches students to manage data workloads across on-premises and multi-cloud environments, and emphasizes governance and compliance standards.
Practical, hands-on projects throughout the section ensure students learn to apply their knowledge in real-world settings.
If you want to become a skilled data engineer, you can join Ivy’s certification course. This course follows the exact same data engineering syllabus as above and is developed in partnership with the prestigious E&ICT Academy IIT Guwahati.
Here is why you should choose Ivy’s Cloud Data Engineering course?
The course helps you become job-ready in just 45 weeks. Interested in learning more? Visit our Data Engineering course page for a detailed syllabus and enrollment information.
Is data engineering more difficult than data science?
It depends on your skills, experience, and strengths. Data engineering requires strong programming skills to build data pipelines, handle large amounts of data, and ensure data quality. Data science requires proficiency in statistics, machine learning, data visualization, and communication to find valuable insights from data and convey them easily. You can research and network with professionals in both domains to gain a better understanding.
Can you be a data engineer without coding?
No, you can’t become a skilled data engineer without strong coding skills. Your job as a data engineer involves building data extraction, transformation, and loading systems, working with data pipelines, managing data, and debugging and troubleshooting data systems. All of these require programming skills. That’s why Ivy Pro’s IIT-certified Data Engineering course teaches all the essential coding languages for data engineers.
Which coding languages are best for data engineers?
Python, SQL, Java, R, and Scala are some of the top programming languages used by data engineers. You will also need proficiency in tools like Apache Spark, Hadoop, and ETL frameworks. No matter which language you use, you will need a good understanding of data structures and algorithms.
Is Python enough for a data engineer?
No, Python is not enough. Python is an essential language for data engineers, and it can help you with data manipulation and data analysis. However, you will also need to learn data warehousing concepts, SQL, big data technologies like Hadoop, Spark, and Hive, and cloud platforms like AWS.
Is Java good for data engineering?
Yes, Java is a good language for data engineering. Since it’s an object-oriented programming language, it helps you write code that is easy to read, reuse, and maintain, helping you easily build complex data systems. Besides, Java has excellent performance, wide adoption, numerous libraries, and a supportive community.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team May 15, 2024 No Comments
Data engineers collect, organize, and store raw data. This helps companies uncover valuable insights, improve their products, and better target their marketing. That’s why companies value data engineers.
But is data engineering a good career? Are data engineers in high demand?
There’s actually a high demand for skilled data engineers, and the job offers a competitive salary and good learning opportunities.
If you see the numbers, the global big data and data engineering market is projected to grow from USD 51.7 billion in 2022 to USD 140.80 billion in 2028. So, you can also expect good job security.
Keep reading to find out more. We will explore what data engineers do, why this career path is worth pursuing, and what the future of data engineering is. So, here we go…
Data engineers are the plumbers of the data world, building pipelines that collect data. They extract raw data from different sources and store it in a usable format.
Data engineering is all about making data available, consistent, secure, and recoverable for an organization. For this, they have to build and maintain infrastructures like databases, big data repositories, and data pipelines.
Here’s a breakdown of their key tasks:
As a data engineer, you will often collaborate with data analysts and scientists to ensure data matches their needs and the organization achieves its goals. And that’s how you will be playing a critical role in helping businesses make data-driven decisions.
Here are some key data engineering skills:
SQL: You need to learn SQL queries and operations like data cleaning, aggregation, and error handling, as well as advanced SQL features like subqueries and user-defined functions.
Python: Python programming is essential. You need to learn Python basics, advanced data structures, functions for data preprocessing, data wrangling, debugging, etc. Besides Python, you can also learn programming languages like Java, R, and Scala.
Data Warehouses: A data warehouse is a data management system that stores large amounts of data from various sources in an organized way. You need to know how to design, build, and manage it so that businesses can carry out their BI, analytics, and reporting smoothly.
ETL Pipelines: ETL stands for Extract, Transform, and Load. It helps businesses collect, store, and use data efficiently. This involves taking raw data from various sources, cleaning it up, and then loading it into a single system for further use.
Big Data Processing: This helps you work with large amounts of data. You need to learn the fundamentals of big data and key technologies such as Hadoop, Apache Hive, and Apache Spark. You can work on projects that involve building and managing real-time data pipelines.
AWS, Google Cloud, Microsoft Azure: Cloud services are used to store and manage data over the internet, so you don’t need physical servers. AWS, Google Cloud, and Azure are technologies that allow you to do this. Simply gaining an in-depth understanding of Microsoft Azure and its application in data engineering would be enough.
Read this post to know more about the latest data engineering syllabus and skills.
You also need to develop certain soft skills to work smoothly in the corporate world.
The most important is communication skills, which are necessary to clearly explain technical details to non-technical colleagues and stakeholders. Good communication also helps you better understand the problems of your clients and get relevant feedback to deliver the best work.
You also need attention to detail, problem-solving skills, presentation skills, and the ability to work well in a team.
And obviously, to get a job, you need to know how to perform well in interviews. That’s why we at Ivy Professional School help our students by letting them participate in mock interviews.
So, now you understand that data engineers build the foundation for data-driven decision-making for organizations and what skills they need to do it. But does that mean it’s a good career path for you?
Well, let’s see some of the reasons why data engineering is an attractive career path:
High Demand: Businesses have to deal with massive amounts of data. This creates a constant need for skilled data engineers to collect and manage it. That’s why, in April 2024, there were over 10,500 job openings for data engineers across all industries on online job portals in India.
Impressive Salaries: With high demand comes high pay. Data engineers typically earn competitive salaries because of the value they bring to organizations. The average base pay of data engineers in India is ₹9,41,500 per year. And senior data engineers with 2-4 years of experience have an annual average base pay of ₹18,50,000.
Learning Opportunity: Data engineering requires creativity and problem-solving. You will constantly be challenged to design new data pipelines, clean up raw data, and find innovative ways to make data usable. This means you will get to learn a lot of tech and soft skills.
Creating Impact: You will work on a variety of projects across different industries, making a real impact on how businesses operate and make decisions.
Job Security: According to IDC, the global datasphere is projected to reach a staggering 175 zettabytes by 2025. This ever-increasing data volume creates a constant demand for skilled data engineers. That’s why data engineer jobs are expected to grow at a rate of 21% from 2018-2028 in the US. This means you can have a stable and rewarding career.
The above points make data engineering an irresistible career path. However, there are also certain challenges that you must take care of. Here they are:
Now, you will be able to judge if data engineering is for you or not. Let’s move on to the next section for an even better understanding…
First of all, it’s important to consider if this career aligns with your interests and skills.
If you like programming, are passionate about data, and love challenges, then the benefits of data engineering can outweigh the challenges.
However, you will need a learning attitude. The market is ever-changing, so a love for continuous learning is essential to stay relevant in this field.
You will also need communication and teamwork skills. Because data engineers often work with data scientists, analysts, and other professionals to help an organization achieve its goals.
If you match the above requirements, data engineering will be a rewarding career path for you that will offer good pay, job security, and the chance to make a real impact in the world.
The world of data is constantly growing. And you, as a data engineer, will continue to play a vital role in shaping its future. Here are some exciting trends to look forward to:
With the ever-growing importance of data, data engineers will be even more crucial for businesses of all sizes. If you are looking for a promising career, you can definitely consider data engineering.
If you want to learn data engineering, you can join Ivy’s Cloud Data Engineering Certification course.
This is an online course made in partnership with E&ICT Academy IIT Guwahati. So, you will be coached by IIT Guwahati professors as well as experts from Amazon, Google, Microsoft, etc.
The course will help you learn current industry skills, complete 30+ real-life projects, and become job-ready in just 45 weeks.
And this program is perfect for both college graduates as well as working professionals who want to upskill. Visit this page to learn more about Ivy’s Cloud Data Engineering course.
Is data engineering a good career in India?
Yes, data engineering is a promising career in India. With impressive salaries, job security, and a lot of learning opportunities, data engineering can help you achieve your career goals. And since the global big data and data engineering market is expected to grow from $51.7 billion in 2022 to $140.80 billion in 2028, you can expect the demand for data engineers will only grow with time.
Are data engineers in high demand?
Since data-driven decision-making has become a necessity, data engineers are in high demand. Companies across various industries need them to build and maintain the infrastructure needed for data collection, storage, processing, and analysis. That’s evident from the fact that data engineering jobs are expected to increase at a rate of 21% from 2018-2028 in the US.
Does data engineering involve a lot of coding?
Yes, data engineering involves a lot of coding. Data engineers use programming languages like Python, SQL, Scala, and Java to build data pipelines, manage databases, and make sure data is processed efficiently. If you want to be a skilled data engineer, you have to master the above programming languages, which you can easily do by taking online data engineering certification courses.
Do data engineers make good money?
The average salary for a data engineer in India is ₹8,53,500 per year. Location, industry, company size, and years of experience are some factors that can influence the salary. For instance, data engineers in Bangalore earn an average annual salary of ₹11 lakhs, and in Pune, it is ₹9 lakhs. But whatever the case, as the demand for data engineers continues to rise, salaries are expected to remain high.
Who gets paid more: software engineers or data engineers?
Both software engineers and data engineers are well-paid, but their salaries can vary based on factors like company, location, experience, etc. If you compare the average annual salary, data engineers earn ₹8,53,500, and software engineers earn ₹8,21,152, according to Glassdoor. So, data engineers can earn slightly more due to their specialized skills and the growing necessity of data-driven decision-making.
Is data engineering a stressful job?
The right answer depends on you and the company you will work at. However, like most technical jobs, data engineering can be challenging at times, especially when dealing with complex data systems or tight deadlines. The thing is, you have to ensure that data systems are efficient, reliable, and secure. Which can be stressful. But if you have mastered the skills, have good experience in solving data-related problems, and love programming, the work can be very rewarding.
Will AI replace data engineers?
Okay, that’s a hot question. AI can automate repetitive and laborious tasks like data ETL, data integration, data pipeline creation, etc. But it won’t fully replace data engineers. Skilled data engineers will be needed to perform those complex tasks requiring human expertise and creativity. So, keep improving your skills, stay updated with the industry, and learn to utilize AI. AI is your co-pilot, which can boost your efficiency and problem-solving ability.
Eeshani Agrawal holds an MS in Civil Engineering from Texas A&M University (USA) and has over 16 years of experience in data visualization, storytelling, and analytics. She has consulted for top engineering, manufacturing, and consulting firms worldwide and has coached over 9,000 professionals and students from leading institutions like IITs, IIMs, and ISI.
Team May 06, 2024 No Comments
Mastering data science requires constant learning. Books can help you learn new things, improve your techniques, and change how you approach problems.
No matter whether you are an aspiring data scientist or a professional, reading data science books lets you effectively transform raw data into powerful insights and tell better stories.
To help you on this journey, in this post, we have shared some of the best data science books you must read. So, get ready to become smarter and more skilled.
Here are some of the best books for data scientists that will help you sharpen your skills. They will improve your problem-solving ability and help you use data to make sense of this confusing world:
Author: Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence. Formerly a software engineer at Google and a data scientist at numerous startups.
About: This is one of the best data science books for beginners that goes beyond using basic tools. The book covers data manipulation, machine learning models, and even advanced topics like recommendation systems and natural language processing. You will gain a strong foundation in the math and statistics behind data science, plus the coding skills to put it into practice.
Get the book: Data Science from Scratch
Authors: It’s written by famous data science experts Foster Provost and Tom Fawcett. Provost is a Professor of Data Science at New York University’s Stern School of Business. And Fawcett is a machine learning Ph.D. holder who has worked in industry R&D for over 20 years.
About: This book teaches you the core concepts of data science and how to apply them to solve real business problems. The book emphasizes “data-analytic thinking” to help you extract valuable insights from data. It’s ideal for those wanting to bridge the gap between data science and its practical business applications.
Get the book: Data Science for Business
Author: Wes McKinney is an American software developer, Co-founder of Voltron Data, and creator of the Python pandas project. He studied theoretical mathematics at MIT and graduated in 2006.
About: This data science book teaches you essential Python skills for working with data. You will learn data cleaning, manipulation, and analysis to effectively solve diverse sets of data analysis problems. This book is packed with practical case studies and is perfect if you are new to Python and want to get introduced to scientific computing in Python.
Get the book: Python for Data Analysis
Authors: It’s written by Peter Bruce and Andrew Bruce. Peter Bruce is the founder of the Institute for Statistics Education at Statistics.com. Andrew Bruce is a Ph.D. holder in statistics at the University of Washington and has 30+ years of experience in statistics and data science.
About: This book bridges the gap between traditional statistics and how it’s used in data science. It covers essential statistical methods, shows how to apply them correctly, and helps you avoid common mistakes. You will learn about exploratory analysis, sampling, experimental design, regression, classification, and even machine learning from a statistical viewpoint.
Get the book: Practical Statistics for Data Scientists
Author: Cole Nussbaumer Knaflic is the founder and CEO of Storytelling With Data. She has been analyzing data and telling compelling stories for the last 10 years.
About: “Storytelling with Data” is a must-read book for data scientists. It teaches you how to transform data into clear and compelling visuals that tell an informative story. You will learn the principles of effective data visualization and how to go beyond basic charts to create presentations that engage your audience. If you want to make your data analysis truly impactful, this book is for you.
Get the book: Storytelling with Data
Authors: It’s written by Hadley Wickham and Garrett Grolemund.
Hadley, renowned for his contributions to R, serves as chief scientist at Posit, PBC, and is an adjunct professor at the University of Auckland, Stanford, and Rice University.
Garrett, a Ph.D. holder in statistics from Rice University, serves as the director and developer relations at Posit, PBC.
About: This is a beginner-friendly guide suitable for people who have no previous programming experience. It teaches you R, RStudio, the tidyverse (a set of helpful packages), and the entire data science process. You will learn data cleaning, exploration, modeling, and how to present your results effectively. The book has a lot of exercises that will help you apply your knowledge to solve problems.
Get the book: R for Data Science
Authors: Andreas C. Müller and Sarah Guido wrote this data science book. Andreas Müller, PhD holder in machine learning from the University of Bonn, works at the Center for Data Science at the New York University. Sarah, a data scientist residing in New York City, worked in many startups.
About: This book is a practical guide to building machine-learning applications using Python. The book focuses less on the maths and more on the practical side of using ML algorithms, making it a beginner-friendly book. Apart from the Scikit-learn library, you will also get familiar with NumPy and Matplotlib libraries.
Get the book: Introduction to Machine Learning with Python
Author: Seth Stephens-Davidowitz is a data scientist, economist, and author. Formerly a Google data scientist and a visiting lecturer at the Wharton School of the University of Pennsylvania.
About: This is one of the best books for data scientists who want to understand the application of data science. “Everybody Lies” explores how big data can help us uncover hidden patterns about how people think and behave. The book teaches you to analyze large datasets to answer interesting questions about the world, covering topics like prejudice, decision-making, and even the impact of movies on crime. Aspiring data scientists will learn to think critically about data and see how it can be used to challenge common beliefs.
Get the book: Everybody Lies
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