Which is better for freshers in India: data analytics, data engineering or data science?

For most freshers, data analytics is the most accessible start, because SQL, Excel and Power BI can be learned in months and used in many industries. Computer science graduates who enjoy building systems may prefer data engineering. Data science usually needs stronger statistics and project work, so many people reach it after an analytics or engineering role.

Do I need to know coding for data analytics?

You need SQL, which is a query language rather than general programming, plus Excel formulas and DAX in Power BI. Python helps and many analyst job descriptions ask for it, but you can start without it. Learn SQL well first, then add Python once you are comfortable cleaning and analysing data with SQL and Power BI.

Is there still a Microsoft data scientist certification?

Not under that name. Microsoft retired the Azure Data Scientist Associate certification and its DP-100 exam on 1 June 2026. The replacement is the Machine Learning Operations Engineer Associate certification, exam AI-300, which focuses on training, deploying and monitoring machine learning models and generative AI applications in production on Azure.

Can a data analyst become a data engineer?

Yes, and it is one of the most common moves. Analysts already know SQL and data modelling. The extra skills are Python, PySpark, pipeline orchestration, version control and cloud storage concepts. Fabric makes the step easier because analysts and engineers work on the same platform, and DP-600 then DP-700 is a natural certification sequence.

Which Microsoft certification should I take first for a data career?

Take PL-300 if you are aiming for analytics, or if you are unsure, because Power BI skills help in all three roles. Choose DP-700 first only if you already write SQL and Python confidently and want engineering work. Aim for AI-300 after you can build and evaluate machine learning models, since it assumes a data science background.

Data Science vs Data Engineering vs Data Analytics: Which Fits You?

Data analysts turn data into answers for the business, mostly with SQL, Excel and Power BI. Data engineers build and run the pipelines and storage that everyone else depends on. Data scientists build models that predict or classify. Choose analytics if you enjoy business questions, engineering if you enjoy building reliable systems, and data science if you enjoy statistics and experiments.

At a glance

Role	Core question	Daily tools	Microsoft certification
Data analyst	What happened, and why?	SQL, Excel, Power BI, DAX	PL-300
Data engineer	Is the data right and on time?	SQL, Python, PySpark, Fabric or Databricks	DP-700 or DP-750
Data scientist	What is likely to happen next?	Python, statistics, scikit-learn, MLflow	AI-300 (MLOps)

Certification codes are as of October 2026. Microsoft retired the Azure Data Scientist exam (DP-100) on 1 June 2026, which changes the picture for aspiring data scientists; more on that below.

What each role actually does all week

Job titles in India vary a lot between companies. A "data analyst" at one firm writes Python every day, while at another the same title means Excel reporting. Look at the work, not the title. Here is what a typical week looks like in each role.

Data analyst

Picture an analyst at a retail or D2C brand. On Monday they refresh the weekly sales dashboard and check that the numbers match finance. On Tuesday a manager asks why returns rose in one region, so they slice the data by product, courier and pin code. Midweek they build a festive season comparison ahead of Diwali planning. On Friday they present three findings and one recommendation to the business head.

Most of the time goes into cleaning data, writing SQL, building measures in DAX, and explaining what the numbers mean in plain words. The analyst is judged on whether their work changes a decision.

Data engineer

Picture an engineer at a fintech or a large bank's technology centre. Every night, settlement files from partner banks land in cloud storage, and the engineer's pipelines load them into a lakehouse, clean them, and publish trusted tables by morning. When a partner changes a file format without notice, the pipeline must fail safely and alert someone. The engineer also manages access, cost, data quality checks and the bronze, silver and gold layers that analysts query.

The engineer is judged on reliability. If the 9 am dashboard is wrong, the problem usually started in a pipeline.

Data scientist

Picture a data scientist at a grocery chain forecasting demand before festival peaks, or at a telecom predicting which customers might leave. They frame the business problem, pick and build features, train and compare models, and then explain the result to people who do not care about algorithms. After deployment they watch for drift, because customer behaviour changes. In 2026 many data scientists also evaluate generative AI applications, measuring whether answers are grounded and relevant.

The data scientist is judged on whether the model improves a business outcome compared with the simple method it replaced.

Skills compared

Skill	Analyst	Engineer	Scientist
SQL	Core	Core	Useful
Excel	Core	Light	Light
Power BI and DAX	Core	Useful	Light
Python	Useful	Core	Core
Spark and PySpark	Light	Core	Useful
Statistics	Useful	Light	Core
Data modelling	Core	Core	Light
Pipelines and scheduling	Light	Core	Useful
Talking to the business	Core	Useful	Core
Cloud and DevOps basics	Light	Core	Useful

Two things stand out. SQL is the shared language of all three roles, so it is never wasted effort. And communication matters most at the two ends: analysts and scientists both spend a large part of their week explaining.

Certifications in 2026: what changed

Microsoft's data certifications moved a lot this year, and older blog posts and course brochures are often out of date. All role-based exams need a score of 700 to pass, and associate certifications renew free online every year.

For analysts

Microsoft Certified: Power BI Data Analyst Associate (exam PL-300) remains the standard first certification. It covers preparing, modelling, visualising and analysing data, and managing and securing Power BI, with Power Query and DAX at the centre. The natural next step is Fabric Analytics Engineer Associate (DP-600), which adds lakehouses, warehouses and semantic models using SQL, KQL and DAX.

For engineers

Microsoft Certified: Fabric Data Engineer Associate (exam DP-700) covers ingesting and transforming data, securing and managing an analytics solution, and monitoring and optimising it, using SQL, PySpark and KQL. Microsoft has also launched DP-750, Implementing Data Engineering Solutions Using Azure Databricks, with skills measured updated on 19 October 2026 around Unity Catalog, Lakeflow pipelines and jobs. The older Azure Data Engineer Associate (DP-203) retired in March 2025, so ignore study plans built around it.

For data scientists

This is the biggest change. The Azure Data Scientist Associate certification (DP-100) retired on 1 June 2026, and Microsoft's replacement is the Machine Learning Operations Engineer Associate (AI-300), available since 12 May 2026. The AI-300 study guide describes a candidate with a data science background and Python experience who also handles MLOps in Azure Machine Learning and GenAIOps in Microsoft Foundry, including GitHub Actions and infrastructure as code.

In plain terms, Microsoft no longer certifies "data science" on its own; it certifies getting models into production and keeping them healthy. Learn the fundamentals first (statistics, Python, scikit-learn, experiment tracking), for example through Microsoft's free learning path on data science and machine learning in Fabric, then aim for AI-300 once you can build and evaluate models. Our AI-300 guide covers that exam in detail.

A quick self-check: which role fits you?

Answer these eight questions honestly and note A, B or C for each.

A dashboard shows sales fell last month. Your first instinct is to: A) find which region and product caused it; B) check whether last night's data load finished properly; C) wonder whether the drop could have been predicted.
Which compliment would please you most? A) "Your report changed our decision." B) "That pipeline hasn't failed in months." C) "Your model beat our old forecast."
Which subject did you enjoy most? A) commerce or business studies; B) computer science; C) mathematics or statistics.
Writing code every day sounds: A) fine in small doses, mostly SQL and formulas; B) great, I like building things; C) good when it serves an analysis or experiment.
When a problem is vague, you: A) enjoy talking to people until the question is clear; B) prefer a clear specification to build against; C) enjoy researching a problem nobody has solved yet.
Presenting to senior managers is: A) something you enjoy; B) something you would rather avoid; C) fine when it is about results you produced.
Being on call when a system breaks early in the morning is: A) not for you; B) acceptable as part of the job; C) rare, you hope.
Maths beyond the basics, such as probability and linear algebra: A) you would rather avoid it; B) neutral; C) you enjoy it.

Mostly A points to data analytics. Mostly B points to data engineering. Mostly C points to data science. A mix of A and B often suits an analytics engineer working in Fabric, and a mix of B and C suits machine learning engineering and MLOps.

Which is easiest to enter from India today?

This is guidance from a trainer's view, not survey data. Analytics usually has the lowest entry barrier, which is why many commerce graduates, MBAs and career switchers start there with SQL, Excel and Power BI. Our data analyst roadmap sets out the order to learn them.

Engineering suits computer science and IT graduates, and people coming from database administration, ETL or support roles who already think about systems. The data engineer roadmap runs from Python to Fabric.

Data science usually needs the most preparation: solid statistics, programming, and projects that show you can frame a problem and evaluate a model honestly. Many entrants come through a strong analytics or engineering role first.

Whatever you pick, a portfolio of two or three real projects, built on public data and documented clearly on GitHub, says more in an interview than a list of certificates.

Switching later is normal

These roles overlap, and people move between them all the time. A common path is analyst to analytics engineer to data engineer, as SQL work grows into pipelines. Engineers often move into MLOps, because deploying and monitoring models is largely an engineering problem. Data scientists who enjoy production work drift towards machine learning engineering. Start where your strengths are today; you are not choosing for life.

How CertTulen Academy can help

CertTulen Academy runs live, MCT-led courses with hands-on labs for each path: PL-300 for analysts, DP-700 for Fabric data engineers, and AI-300 for those moving into machine learning operations. Professional certification courses include an exam voucher, mock practice tests and lab access; see each course page for details. Not sure which path fits? Contact us with your background and we will suggest a sensible starting point.