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A first data job feels far away because the road is not clear: which role, which tools, how many projects, where to apply. This guide gives one clear road for 12 weeks. It does not need a special degree. It needs steady work on a few things in the right order.
Data analyst, data engineer or analytics engineer. Pick one for now.
SQL first. Python for cleaning and small scripts.
Small, finished and written up with a README.
Skills, one project, education. Nothing else.
Apply, solve problems and track every reply.
Step 1: choose one role
Data jobs have different names, and the work overlaps. For a first job, pick the one that fits you best, because your learning plan depends on it.
| Role | What you do all day | Main tools | Good fit if you like |
|---|
| --- | --- | --- | --- |
|---|
| Data analyst | Answer business questions with data and make reports | SQL, Excel, a dashboard tool | Finding answers and explaining them |
|---|
| Data engineer | Move and clean data so others can use it | SQL, Python, pipelines | Building systems that run on their own |
|---|
| Analytics engineer | Turn raw tables into clean, trusted tables | SQL, dbt | Careful SQL and tidy modelling |
|---|
If you cannot decide, start with SQL. It is the first skill for all three, so no time is wasted. Read data engineer vs data analyst vs data scientist for a longer comparison.
Step 2: learn the skills in this order
Learn in the order that entry-level interviews test, not the order that looks most exciting.
- SQL. SELECT, WHERE, JOIN, GROUP BY, then window functions. This is the first filter in almost every interview. Start with SQL practice.
- Python. Lists, dictionaries, loops, functions and reading a file. Then pandas for cleaning data. Start with Python practice.
- How data moves. What a pipeline is, what raw and clean layers mean, and why a rerun must not create duplicates.
- One extra tool. A dashboard tool for analyst roles, dbt for analytics engineering, or Airflow for data engineering. One is enough at the start.
The roadmap page shows the same order with the lessons for each step.
Step 3: build one project and finish it
One finished project is worth more than five half-finished ones. Choose a project that uses real or realistic data, has a clear question, and produces something you can show: a table, a chart or a small pipeline.
Finish means: the code runs from a fresh copy, the README says what the project does and how to run it, and you can explain one thing that went wrong and how you fixed it. The projects page has projects with step-by-step guidance and a README template.
Prepare a five-minute story: the problem, the data, what you built, one mistake, and what you would improve. Interviewers spend most of their time on this story.
Step 4: write a one-page CV
A fresher CV is one page. Put these in order: name and contact, a two-line summary, skills, projects, education. Add internships if you have them.
For each project write two or three lines: what it does, the tools, and one number. A number can be rows processed, queries written, time saved or checks added. Do not invent numbers. If a project has no honest number, describe what it does in plain words. See how to write a fresher resume for examples.
Step 5: apply every week and track it
Most students apply in a burst and then stop. A steady routine works better. Aim for a fixed number of applications each week, for example ten, and track them in a simple table.
Date | Company | Role | Where I applied | Status | Next step
12 Nov | ExampleCo | Data analyst | Company site | Applied | Follow up on 19 NovApply through several routes: company career pages, job sites, your college placement cell, and referrals. A referral often gets your CV read, which is why LinkedIn and referrals matter. Read each job post and apply only where you meet most of the basic requirements. Years of experience asked in a fresher post are often flexible, but a missing core skill is not.
A 12-week plan
| Weeks | Focus | What you can do at the end |
|---|
| --- | --- | --- |
|---|
| 1 to 3 | SQL basics and joins | Solve easy and medium SQL problems without help |
|---|
| 4 to 5 | Window functions and Python basics | Solve top-N per group and write simple Python scripts |
|---|
| 6 to 8 | One project | A finished project with a README |
|---|
| 9 | CV and GitHub | A one-page CV and a clean repository |
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| 10 to 12 | Apply and practise | Ten applications a week and two mock interviews |
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If you have less than an hour a day, stretch each row and keep the order.
Mistakes that slow people down
- Collecting courses. Watching more videos feels like progress. Solving problems and building something is progress.
- Learning every tool. Entry-level interviews rarely ask about ten tools. They ask about SQL, one language and your project.
- Waiting until you feel ready. Start applying while you are still learning. Interviews teach you what to practise.
- No tracking. Without a table you forget who replied and when to follow up.
- Giving up after a few rejections. Many people need dozens of applications. A rejection with no reason tells you nothing about your ability.
What if replies are slow
Slow replies are common for freshers. Check three things. First, does your CV show a project and skills near the top? Second, are you applying to roles that match your skills? Third, are you asking people for referrals, not only sending applications into forms?
If you get interviews but no offers, practise out loud with a friend and review the questions you missed. If you get no interviews, change the CV and the role mix before you change the skills.
Where to start today
Pick one role and write it at the top of a page. Then open the SQL practice problems, solve three, and note what confused you. That is your first day of the plan.