Prateek Agrawal Jul 11, 2026 No Comments
Every digital business depends on reliable data. Customer transactions, website events, financial records, application logs and sensor readings must be collected, cleaned, organised and delivered to the people and systems that need them. Data engineers build the infrastructure that makes this possible.
A strong data engineering skillset goes beyond knowing a programming language or operating a cloud tool. It combines database knowledge, software engineering, data architecture, distributed processing, security, problem-solving and business understanding.
This guide explains the core data engineer skills employers expect, the tools associated with each skill and a practical roadmap for developing job-ready capability.
A data engineering skillset is the combination of technical, analytical and professional abilities required to design, build, operate and improve data systems.
A data engineer may extract data from databases, APIs and applications; transform raw data into consistent datasets; build batch and streaming pipelines; design warehouses and lakes; maintain data quality; and support analytics, machine learning and AI applications.
| Skill area | What you should know | Common tools |
| SQL | Queries, joins, windows and optimisation | PostgreSQL, MySQL, SQL Server |
| Programming | Automation, APIs, transformation and testing | Python, Java, Scala |
| Data modelling | Schemas, facts, dimensions and normalisation | dbt, modelling tools |
| Pipelines | ETL, ELT, retries and incremental loading | Airflow, ADF, AWS Glue |
| Distributed processing | Large-scale data processing | Spark, Databricks |
| Streaming | Events, producers, consumers and offsets | Kafka, Kinesis |
| Cloud | Storage, compute, identity and cost control | AWS, Azure, Google Cloud |
| Quality and governance | Testing, lineage and access control | Great Expectations, Purview |
| DevOps | Version control and automated deployment | Git, Docker, CI/CD tools |
| Business skills | Requirements, documentation and communication | Jira, Confluence |
SQL is the foundation of the data engineering skillset. Engineers use it to inspect source systems, transform data, validate results, create warehouse models and troubleshoot pipeline failures.
A job-ready professional should be comfortable with joins, common table expressions, subqueries, aggregate functions, window functions, date and string functions, deduplication, views, stored procedures, indexes, partitions and query execution plans.
Returning the correct result is only the first step. A data engineer must also consider how a query performs when a table contains millions of records. This requires knowledge of filtering, indexing, partition pruning, data distribution and unnecessary data movement.
Python is commonly used for data ingestion, transformation, API integration, automation and testing. Its standard data structures and extensive ecosystem make it practical for reusable data workflows.
Important skills include:
Libraries such as pandas are useful for moderate-sized data, while PySpark supports distributed processing. Python should be learned through practical tasks such as extracting paginated API data, validating schemas, processing files and loading results into databases.
Pipelines create value only when their outputs are structured for business use. Data modelling is therefore a core part of the data engineering skillset.
Engineers should understand:
Operational databases are designed for frequent inserts and updates. Analytical systems are designed for large scans, aggregations and historical analysis. Recognising this difference helps engineers build models that are both accurate and efficient.
Modern frameworks such as dbt allow teams to create modular SQL models and combine them with testing, documentation, lineage and version-controlled workflows.
ETL means extract, transform and load. ELT means extract, load and transform. Both patterns move data from source systems into analytical platforms.
A capable data engineer should know how to:
Reliable pipelines should be idempotent, meaning they can be rerun safely without creating duplicates or corrupting results. They should also account for late files, network failures, API rate limits and changing source schemas.
A complete data engineering skillset includes the major patterns used to store analytical data.
A data warehouse stores curated data optimised for reporting and analysis. Common examples include Snowflake, Google BigQuery, Amazon Redshift and Azure-based warehouse services.
A data lake stores large volumes of structured, semi-structured and unstructured data, typically in object storage.
A lakehouse combines the flexibility of data-lake storage with warehouse-style performance, management and governance.
Important storage concepts include:
The objective is not to memorise every product interface. It is to understand why a storage pattern suits a workload and what trade-offs it creates in performance, governance and cost.
When data becomes too large or processing too complex for one machine, distributed computing becomes necessary. Apache Spark is widely used for data engineering, data science and machine learning workloads.
A practical Spark skillset includes:
Many beginners focus only on PySpark syntax. Employers need engineers who can diagnose why a job is slow, why a join creates excessive shuffling or why thousands of small files reduce performance.
Batch pipelines process data at scheduled intervals. Streaming systems process events continuously or in short windows.
Apache Kafka is a distributed event-streaming platform used to publish, retain and process streams of events.
Engineers working with streaming data should understand:
Managed alternatives include Amazon Kinesis and Azure Event Hubs. AWS describes Kinesis as a service for collecting, processing and analysing real-time streaming data.
A practical streaming project could process e-commerce clickstream events, calculate rolling product views and store the resulting metrics for a dashboard.
Production data platforms contain dependent tasks that must run in the correct order. Orchestration tools schedule these tasks, manage dependencies and monitor their status.
Apache Airflow is designed for developing, scheduling and monitoring batch-oriented workflows, typically represented as directed acyclic graphs or DAGs.
Relevant orchestration skills include:
Other options include Azure Data Factory, Google Cloud Composer, Databricks Workflows and Microsoft Fabric Data Factory.
The core skill is understanding how workflows behave when tasks fail, data arrives late or historical periods must be reprocessed.
Most modern data engineering roles require exposure to at least one major cloud platform: Microsoft Azure, Amazon Web Services or Google Cloud.
A cloud-ready data engineering skillset should cover:
AWS, for example, provides analytics services across querying, processing, governance, warehousing and streaming, including Athena, EMR, Glue, Redshift, Lake Formation and Kinesis. Azure and Google Cloud offer comparable capability categories under different service names.
Beginners should not attempt to master all three clouds simultaneously. Choose one ecosystem, build an end-to-end project and then map the concepts to equivalent services elsewhere.
A pipeline is not successful merely because it finishes without an error. It must deliver accurate, complete, timely and trustworthy data.
Engineers should create checks for:
Testing operates at several levels. Unit tests validate code components. Integration tests confirm that systems work together. Data tests verify the properties of output datasets.
Observability extends this approach by monitoring pipeline duration, failure rates, data freshness, volume changes, lineage and downstream impact.
Engineers must also decide what happens when a check fails. Depending on the level of risk, the pipeline may stop, quarantine records, issue a warning or continue with an audit flag.
Data engineering is a software engineering discipline. Production pipelines should not depend on manually edited scripts stored on individual computers.
Core engineering practices include:
dbt explicitly applies software engineering practices such as version control, modularity, testing, CI/CD and documentation to data transformation workflows.
Data engineers frequently work with financial, customer, employee and operational data. Security must be built into the system.
Relevant skills include:
Governance ensures users understand where data came from, what it means, who owns it and who may access it. A catalogue, business glossary and lineage system make data easier to discover while reducing misuse.
Engineers do not need to act as legal experts, but they must translate security and governance requirements into technical controls.
Technical skill alone does not create useful data products. Data engineers must understand the business meaning behind the requested data.
If a stakeholder asks for “daily sales data,” the engineer must clarify:
Useful professional skills include requirement gathering, documentation, estimation, prioritisation, stakeholder communication and incident reporting.
Engineers should be able to explain technical trade-offs in terms of reliability, time, risk and cost.
AI coding assistants can generate SQL, explain unfamiliar code, create documentation and accelerate troubleshooting. Data platforms are also adding AI-supported development features.
dbt, for example, documents AI capabilities grounded in project context such as lineage, tests, contracts and metric definitions.
However, generated code may be inefficient or apply incorrect business logic. Suggested configurations may also create security, performance or cost issues.
The emerging skill is not simply using AI. It is using AI with sufficient context, review, testing and governance. Engineers who understand the fundamentals can use these tools to move faster without sacrificing reliability.
Beginners do not need to master every technology before applying for roles. A practical sequence is:
This sequence develops depth before breadth. It is more effective than gaining superficial exposure to dozens of tools.
Experienced professionals should move beyond tool operation towards architecture, platform reliability, cost optimisation, governance, reusable components and technical leadership.
A portfolio should demonstrate complete workflows and engineering decisions.
Extract order and customer data from an API, store raw files in object storage, transform the data, load warehouse tables and produce daily sales metrics.
Generate website events, publish them to a streaming platform, calculate windowed metrics and store the results for analysis.
Create configurable checks for duplicates, nulls, row counts, freshness and source-to-target totals. Produce an audit report after every pipeline run.
Build a dimensional model, implement incremental loading, add role-based access and document cost-optimisation decisions.
Each project should contain a README, architecture diagram, data model, source code, tests, sample outputs and deployment instructions. Explain trade-offs and limitations instead of presenting the work as flawless.
Common mistakes when building data engineer skills include:
A balanced data engineering skillset combines conceptual depth, implementation ability and operational discipline.
The most important skills are SQL, Python, data modelling, ETL or ELT, databases, cloud platforms, orchestration, data quality, Git and communication. Spark and streaming tools become important for large-scale or real-time systems.
No. Python is valuable, but data engineering also requires SQL, database concepts, modelling, pipeline design, cloud services, testing and operational skills.
Deep machine learning expertise is not mandatory for most roles. However, understanding model-training data, feature pipelines and production inference helps when supporting AI and machine learning teams.
Azure, AWS and Google Cloud all support enterprise data engineering. The best starting platform depends on your target employers, existing experience and technology environment. Learn one platform deeply before covering all three.
Yes. Entry-level candidates should focus on SQL, Python, databases, ETL, Git and one cloud ecosystem, then demonstrate these skills through complete projects.
The timeline depends on prior programming and database experience. Readiness is better measured by whether you can independently design, build, test, explain and troubleshoot an end-to-end pipeline.
The ideal data engineering skillset is not a checklist of fashionable tools. It is the ability to build data systems that are accurate, scalable, secure, maintainable and useful to the business.
Start with SQL, Python, databases and data modelling. Progress to pipelines, cloud platforms, orchestration, distributed processing, data quality and governance. Reinforce each stage with practical projects and professional engineering practices.
As organisations expand analytics, machine learning and enterprise AI, reliable data engineering becomes increasingly valuable. Professionals who combine technical depth with business understanding will be better positioned to build the trusted data foundation these systems require.
To develop these capabilities through structured training and hands-on projects, explore the Data Engineering Programme at Ivy Professional School.
Prateek Agrawal May 16, 2026 No Comments
Many people believe that data science is only for software engineers, coders, or people from a computer science background. This is one of the biggest myths stopping talented professionals from entering the field. The truth is simple: Can a non-IT person learn data science? Yes, absolutely.
Data science is not just about writing code. It is about understanding data, asking the right questions, finding patterns, solving business problems, and communicating insights clearly. In fact, many non-IT professionals already use data in their daily work without calling it “data science.” Sales teams analyze targets, finance teams study costs, HR teams review attrition, marketing teams track campaigns, and operations teams monitor performance. Data science simply gives structure, tools, and advanced techniques to do this better.
So, if you are from commerce, management, economics, statistics, engineering, HR, sales, finance, operations, or even a completely different background, this blog will help you understand how you can enter the field confidently.
Before answering Can a non-IT person learn data science, it is important to understand what data science really is.
Data science is the process of collecting, cleaning, analyzing, visualizing, and interpreting data to solve problems or support decision-making. It combines different skills such as statistics, business understanding, programming, machine learning, and communication.
For example, a retail company may want to know why sales dropped in a particular region. A data science approach would include collecting sales data, comparing it across locations and time periods, finding possible reasons, visualizing the trends, and recommending business actions.
Similarly, a bank may use data science to identify customers who are likely to default on loans. A hospital may use it to predict patient demand. An e-commerce company may use it to recommend products. A manufacturing company may use it to forecast defects or machine downtime.
This shows that data science is not limited to IT companies. It is used across industries and functions.
The most common fear beginners have is coding. Many people think, “I am not from IT, so how will I learn Python, SQL, or machine learning?”
Here is the reality: coding is a skill, not a background requirement. Nobody is born knowing Python or SQL. Even IT professionals learn them step by step.
So, Can a non-IT person learn data science without coding experience? Yes. You can start with beginner-friendly tools and gradually move toward programming.
A good learning path usually begins with Excel, statistics, and business problem-solving. Then you can learn SQL for working with databases. After that, Python becomes easier because you already understand what you want to do with data.
Python for data science is not the same as advanced software development. You do not need to build complex applications at the beginning. You mainly need to learn how to import data, clean it, analyze it, create charts, and build basic models.
For many learners, the fear of coding disappears once they start applying it to real examples.
A non-IT background can become a strength in data science, especially if you already understand business processes.
For example, a finance professional understands revenue, cost, profit, margins, and risk. A marketing professional understands customer behavior, campaign performance, segmentation, and conversion. An HR professional understands hiring, attrition, employee engagement, and performance. A supply chain professional understands inventory, logistics, demand, and vendor performance.
These domain skills are extremely valuable.
Many technical learners know how to build models but may struggle to understand the business context. On the other hand, a non-IT professional may understand the business problem better and can learn the required tools to analyze it.
This is why the answer to Can a non-IT person learn data science is not only yes, but also that they may bring a unique advantage.
To become good at data science, you need a combination of technical and analytical skills. You do not need to master everything on day one. You can build these skills gradually.
Statistics is the foundation of data science. You should understand concepts like average, median, percentage, variance, correlation, probability, hypothesis testing, and distribution.
The good news is that you do not need advanced mathematics at the beginner stage. Most real business problems require practical statistical thinking rather than complicated formulas.
Excel is a great starting point for non-IT learners. It helps you understand rows, columns, formulas, filters, pivot tables, charts, and basic analysis.
If you are already comfortable with Excel, you already have a strong foundation for data science.
SQL is used to extract and work with data from databases. It is one of the most important skills for data analysts and data scientists.
SQL is easier than most programming languages because it uses a structured query format. You can learn basic SQL queries like SELECT, WHERE, GROUP BY, JOIN, and ORDER BY within a few weeks of practice.
Python is widely used in data science because it is simple and powerful. Libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn help you clean data, analyze it, visualize it, and build machine learning models.
For beginners, the focus should be on Python for data analysis, not advanced software development.
A data scientist must know how to present insights clearly. Tools like Power BI, Tableau, Excel dashboards, and Python visualization libraries are useful here.
Good visualization helps decision-makers understand what the data is saying.
Machine learning helps computers learn patterns from data. As a beginner, you can start with simple concepts like regression, classification, clustering, and decision trees.
You do not need to become a machine learning researcher. You need to understand how models work, when to use them, and how to evaluate their performance.
This is where non-IT learners can shine. Data science is valuable only when it solves real problems. You should learn how to convert a business question into a data question.
For example, “Why are customers leaving?” becomes a churn analysis problem. “Which product should we promote?” becomes a sales and customer segmentation problem.
If you are wondering Can a non-IT person learn data science in a structured way, follow this practical path.
Start with Excel and basic statistics. Learn how to clean data, create pivot tables, calculate key metrics, and build simple dashboards. Then move to SQL and learn how to extract data from databases.
Once you are comfortable with SQL, start Python. Focus on Python basics first, then move to Pandas for data cleaning and analysis. After this, learn visualization using Power BI, Tableau, or Python libraries.
Then move to machine learning basics. Start with simple projects like predicting house prices, classifying customers, forecasting sales, or analyzing employee attrition.
Finally, build a project portfolio. This is extremely important for career transition. Employers want to see whether you can apply your skills to real-world problems.
Learning data science as a non-IT person is possible, but it does come with challenges.
The first challenge is fear of coding. Many learners give up before they even start because Python looks unfamiliar. The solution is to learn coding through practical examples rather than theory.
The second challenge is trying to learn too much at once. Data science has many topics, and beginners often feel overwhelmed. The solution is to follow a step-by-step roadmap.
The third challenge is lack of practice. Watching videos is not enough. You need to work on datasets, solve problems, and build projects.
The fourth challenge is not connecting data science with business use cases. Many learners focus only on tools and forget the problem-solving part. This makes their learning incomplete.
The fifth challenge is comparison. Non-IT learners often compare themselves with coders. This is unnecessary. Your journey will be different, but it can still be successful.
The timeline depends on your background, consistency, and learning approach.
If you study regularly for 8 to 10 hours per week, you can build a strong foundation in 6 to 9 months. This includes Excel, SQL, Python, statistics, visualization, and basic machine learning.
If you already know Excel, business analytics, finance, or statistics, your journey may be faster. If you are completely new to data, it may take longer.
But the real answer is not just about duration. The quality of practice matters more. A learner who completes 5 strong projects in 6 months may be more job-ready than someone who watches videos for one year without applying anything.
So, when people ask Can a non-IT person learn data science, the better question is: Are they willing to practice consistently?
Data science opens up multiple career paths. You do not have to become a data scientist immediately. Many non-IT professionals begin with roles that match their current strengths.
Some popular roles include:
| Role | Suitable For |
| Data Analyst | Beginners, Excel users, business professionals |
| Business Analyst | Management, operations, finance, sales backgrounds |
| BI Analyst | People interested in dashboards and reporting |
| Marketing Analyst | Marketing and digital campaign professionals |
| HR Analyst | HR and talent management professionals |
| Financial Analyst | Commerce, finance, accounting backgrounds |
| Machine Learning Analyst | Learners comfortable with Python and models |
| Data Scientist | Learners with stronger statistics, coding, and ML skills |
This means you do not need to jump directly into the most advanced role. You can enter through analytics and gradually grow into data science.
Many non-IT backgrounds are suitable for data science.
Commerce students can understand business numbers, accounting, finance, and reporting. MBA graduates can connect data with strategy and decision-making. Economics students often have good analytical and statistical thinking. Engineers from non-computer branches can bring logical thinking and process understanding. HR, sales, marketing, and operations professionals bring domain knowledge.
Even teachers, researchers, entrepreneurs, and consultants can learn data science if they follow the right roadmap.
So, Can a non-IT person learn data science from any background? Yes, provided they are ready to learn the tools, practice regularly, and build projects.
A portfolio is one of the most important parts of your career transition. It shows employers that you can work with real data.
Your portfolio should include projects from different areas such as sales analysis, customer segmentation, financial analysis, HR attrition analysis, inventory analysis, social media analysis, and predictive modeling.
Each project should clearly explain the business problem, dataset used, steps followed, tools applied, insights found, and recommendations given.
Do not simply upload code. Tell a story through your project. Recruiters and hiring managers should be able to understand what problem you solved and what value your analysis created.
A strong portfolio can help non-IT learners compete with technical candidates.
Start small. Do not begin with advanced machine learning or deep learning. Build your foundation first.
Learn one tool at a time. For example, do not try to learn Excel, SQL, Python, Power BI, and machine learning all in the same week.
Practice on real datasets. Use business datasets whenever possible because they are easier to relate to.
Focus on problem-solving. Tools will keep changing, but analytical thinking will always remain valuable.
Build projects and publish them on LinkedIn or a portfolio website. Visibility matters.
Learn how to explain your work. A data professional must communicate insights, not just produce charts or code.
Yes. Can a non-IT person learn data science? Definitely. Data science is not reserved for IT professionals. It is open to anyone who is curious, analytical, consistent, and willing to learn.
You do not need to know coding before starting. You do not need a computer science degree. You do not need to be a mathematics genius. What you need is a structured roadmap, regular practice, real projects, and the ability to connect data with business problems.
In fact, non-IT professionals often bring valuable domain knowledge that can make them stronger data professionals. A finance person can become a finance analytics expert. A marketing person can become a marketing analyst. An HR person can become an HR analytics specialist. An operations professional can become a supply chain analytics expert.
The best way to start is simple: learn Excel and statistics, move to SQL, then Python, then visualization and machine learning. Build projects at every stage.
So, the next time someone asks Can a non-IT person learn data science, the answer is clear: yes, and with the right guidance, they can build a strong and rewarding career in the data field.
Yes, a non-IT person can learn data science with the right roadmap. You can start with Excel, basic statistics, and business analysis before moving to SQL, Python, dashboards, and machine learning.
No, you do not need coding knowledge to start. Coding can be learned step by step. Many beginners first learn Excel, SQL, and basic analytics before learning Python.
Data science may feel challenging in the beginning, but it becomes easier when you learn through practical examples and real projects. The key is to follow a structured learning path instead of trying to learn everything at once.
Students and professionals from commerce, economics, statistics, management, finance, marketing, HR, operations, and engineering backgrounds can all learn data science. A strong business understanding can actually be an advantage.
With regular practice, a non-IT learner can build a strong foundation in around 6 to 9 months. The timeline depends on your current skills, learning consistency, and project practice.
A non-IT beginner should start with Excel, basic statistics, and data interpretation. After that, they can learn SQL, Python, data visualization, and machine learning basics.
Yes, you can get a data-related job without an IT degree if you build strong practical skills and a good project portfolio. Many learners start with roles like Data Analyst, Business Analyst, BI Analyst, or Marketing Analyst before moving into advanced data science roles.
Python is not compulsory at the very beginning, but it is highly recommended for long-term growth in data science. It is widely used for data cleaning, analysis, visualization, and machine learning.
Non-IT learners should build business-focused projects such as sales analysis, customer segmentation, HR attrition analysis, financial analysis, marketing campaign analysis, inventory analysis, and basic prediction models.
Yes, a non-IT person can become a data scientist by learning the right skills, practicing consistently, building projects, and gaining confidence in statistics, SQL, Python, machine learning, and business problem-solving.
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.
Prateek Agrawal May 02, 2026 No Comments
Prateek Agrawal Apr 18, 2026 No Comments
A few years ago, enrolling in a data science program almost guaranteed a job. But in today’s evolving landscape, the question has changed.
In 2026, the real question is not whether data science is in demand—it is whether a data science course in 2026 will actually deliver meaningful ROI for your career.
Let’s break this down across three key dimensions: ROI, salary, and long-term career growth.
Before evaluating whether a data science course in 2026 is worth it, you need to understand one major shift.
Data science hasn’t declined—it has matured.
Companies today are not hiring people who just know tools. They are hiring professionals who can use data and AI to drive decisions.
What has changed:
This means that simply completing a data science course in 2026 is not enough—you need to bring real-world value.
Let’s address the most practical concern—earning potential.
The average salary is around ₹10 LPA.
However, completing a data science course in 2026 does not guarantee these numbers.
Your salary depends on:
A certificate alone does not create value—execution does.
Let’s evaluate the ROI of a data science course in 2026 in practical terms.
A well-designed data science course in 2026 offers:
The key is not the course—it’s how you leverage it.
One of the strongest arguments for a data science course in 2026 is long-term career flexibility.
A data science course in 2026 is not just about a job—it is a foundation for the AI economy.
Let’s be direct.
A data science course in 2026 is NOT worth it if:
But it is worth it if:
This is the difference between:
₹4 LPA candidate vs ₹15 LPA candidate
A common concern is whether AI will replace data scientists.
The reality is different.
AI is:
Companies now expect professionals to:
A data science course in 2026 should prepare you for this shift.
The future role is not just a “data scientist”—it is an AI-enabled decision-maker.
A data science course in 2026 is ideal for:
Short answer: Yes—but only if done right.
A data science course in 2026 is worth it if:
Because:
But one thing is clear:
The shortcut era is over.
The “learn → build → apply → communicate” era has begun.
If you treat a data science course in 2026 as:
❌ Just a certificate → Not worth it
✅ A career transformation tool → One of the best investments you can make
Success in 2026 is not about knowing more tools.
It’s about creating real impact with data.
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.
Prateek Agrawal Apr 16, 2026 No Comments
A few years ago, becoming a data scientist meant learning Python, a few machine learning algorithms, and building some dashboards.
That playbook is broken.
In 2026, companies are no longer hiring “data scientists.”
They are hiring decision-makers who can use data and AI to move the business forward.
This is why data scientist skills 2026 look very different today.
So instead of listing generic skills, let’s answer a better question:
What skills make someone valuable in today’s data-driven organizations?
Data science is no longer a support function. It is now directly tied to revenue, efficiency, and strategy.
Three major shifts are redefining the role:
This shift is exactly why data scientist skills 2026 are becoming more business-focused than ever before.
Let’s break this down in a way that actually reflects real-world expectations of data scientist skills 2026.
Before you touch data, you need to define the problem correctly.
Most professionals jump straight into analysis. The best ones step back and ask:
If you get this wrong, even the best model won’t help. This is one of the most critical data scientist skills 2026.
Yes, statistics is important. But what defines data scientist skills 2026 is data intuition.
This means:
Python is still essential, but expectations have changed in data scientist skills 2026.
The focus is now on:
Clean datasets are a myth.
A core part of data science skills 2026 is handling:
Machine learning is still relevant, but companies don’t need academic experts.
They need professionals who reflect real-world data scientist skills 2026:
This is no longer optional.
Modern data scientist skills 2026 include working with AI systems effectively.
This includes:
In 2026, visualization is about decision clarity.
A key part of data scientist skills 2026 is asking:
What should the user do after seeing this?
Companies now expect ownership.
This shift defines data scientist skills 2026:
A major shift in data scientist skills 2026 is understanding systems, not just datasets.
You should know:
Top performers use AI to build workflows, not just ask questions.
This is what separates average vs top-tier data scientist skills 2026.
It’s not about how many tools you know.
It’s about how you combine them.
The real power of data scientist skills 2026 lies in skill stacking:
Data science is evolving rapidly.
What’s changing:
This reinforces why data scientist skills 2026 are focused on impact, not just tools.
The biggest mistake people make is preparing for yesterday’s roles.
If you build the right data scientist skills 2026, you won’t just stay relevant, you’ll become indispensable.
Because the future doesn’t belong to people who know tools.
It belongs to people who know how to use data to make decisions.
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 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.