Short answer: To become a data analyst, learn the core tools in this order: Excel, SQL, basic statistics, one dashboard tool (Power BI or Tableau) and Python for data analysis. Then build three to five portfolio projects on real datasets, write up what each one found, and apply for junior data analyst, MIS analyst or reporting analyst roles. Most beginners need a few months of steady practice. A degree in any subject is usually accepted; a coding background is not required.
This guide is for students, fresh graduates and working professionals, including people from non-IT backgrounds, who want a practical plan rather than a list of buzzwords. It covers what the job involves, the skills employers look for, a step-by-step learning plan and how to prove you are ready.
What does a data analyst do?
A data analyst turns raw data, such as sales records, website visits or customer feedback, into clear answers, reports and dashboards that help people make decisions. A typical week involves:
- Talking to a team (sales, finance, marketing or operations) to understand the question they need answered.
- Pulling data from databases with SQL, or from spreadsheets and business systems.
- Cleaning it: fixing missing values, duplicates and inconsistent formats.
- Analysing it to find trends, comparisons and causes.
- Presenting the result as a dashboard, a short report or a few clear charts.
Analysts answer four kinds of questions: what happened (descriptive), why it happened (diagnostic), what is likely to happen (predictive) and what to do next (prescriptive). Most junior roles focus on the first two.
Data analyst skills employers look for
| Skill | What you use it for | How deep to go at first |
|---|---|---|
| Excel | Quick analysis, cleaning and reporting | Formulas, lookups, pivot tables, charts |
| SQL | Getting data out of databases | SELECT, WHERE, GROUP BY, JOINs, subqueries, window functions |
| Statistics | Knowing whether a pattern is real | Averages, spread, distributions, correlation, sampling, basic hypothesis tests |
| Power BI or Tableau | Interactive dashboards for managers | Data modelling, measures, filters, one polished dashboard |
| Python | Cleaning and analysing larger or messier data | pandas, simple charts, reading CSV and Excel files |
| Communication | Explaining findings to non-technical people | A one-page summary: question, finding, recommendation |
| Business sense | Asking the right question | Understanding the metrics of the team you support |
You do not need machine learning or big data tools to get a first analyst job. Those belong to data science and data engineering roles, and are worth learning later if you want to move in that direction.
How to become a data analyst, step by step
- Excel. Start here because almost every office uses it. Learn formulas, XLOOKUP or VLOOKUP, conditional formatting, pivot tables and charts. Ready when you can take a raw sales file and produce a clean monthly summary with a chart.
- SQL. SQL is how analysts get data out of databases. Practise on a free database such as MySQL or PostgreSQL with a sample dataset. Ready when you can join three tables and answer a business question, such as “top ten customers by revenue last quarter”.
- Basic statistics. Learn mean, median, standard deviation, distributions, correlation versus causation, and what a sample is. Ready when you can explain why an average can mislead and when to use the median instead.
- One dashboard tool. Pick Power BI or Tableau; the ideas transfer. Learn to connect data, build a simple data model, write measures and design a clean one-page dashboard. Ready when a manager could use your dashboard without you explaining it.
- Python for analysis. Learn enough Python to load, clean, group and chart data with pandas (pandas: Getting started). Ready when you can clean a messy CSV file faster in Python than in Excel.
- Portfolio projects. Build three to five projects on real, public datasets (see the next section). Ready when each project has a clear question, a finding and a short write-up.
- Job applications. Tailor your resume to each role, link your portfolio, and practise SQL and case questions out loud. Ready when you can walk through one project in five minutes without notes.
- AskThe business question
- GetSQL, Excel
- CleanExcel, Python
- AnalyseStatistics, pandas
- SharePower BI, Tableau
Portfolio projects that show you are job-ready
A portfolio does more than any certificate to show you can do the work. Good beginner projects are small, finished and clearly explained:
- Sales dashboard: monthly revenue, top products and regions, and a target-versus-actual view in Power BI or Tableau.
- SQL case study: ten business questions answered with SQL on a sample retail or e-commerce database, with the queries and results.
- Data cleaning project: a messy public dataset cleaned in Python, with a before-and-after summary of what you fixed.
- Exploratory analysis: a short report on a public dataset (for example, open government data) with three findings and the charts that support them.
For each project, write a short README or one-page summary: the question, the data source, what you did, what you found and one thing you would do next. Use public or sample data only, never data from an employer.
Do certifications help?
A certification is not required, but it gives your learning a clear target and shows employers you have covered a standard syllabus. One widely used option is:
| Certification | Focus | Who it suits |
|---|---|---|
| Microsoft Certified: Power BI Data Analyst Associate (exam PL-300) | Preparing, modelling and visualising data in Power BI | Anyone targeting roles where Power BI is the main reporting tool |
Tableau also offers its own certifications, now run through Salesforce. Check each provider’s page for the current exam details and fees before you book, because they change. Projects still matter more than certificates in most interviews.
Can you become a data analyst without a technical degree?
Yes. Many analysts come from commerce, economics, science, management or other non-IT backgrounds. Business knowledge from those fields is useful, because the hardest part of the job is often understanding the question rather than writing the query. What matters is that you can show the skills: SQL, a dashboard tool, clear thinking about numbers and a portfolio that proves it.
If you are switching careers, look for analyst roles in the field you already know, such as finance, sales operations, HR or supply chain. Your domain knowledge is an advantage there.
Common mistakes beginners make
- Collecting courses instead of finishing projects. Two finished projects beat ten half-watched tutorials.
- Skipping SQL. It is the skill most often tested in analyst interviews.
- Learning every tool at once. Pick one dashboard tool and get good at it first.
- Charts without a message. Every chart should answer a question. Write the answer as the chart title.
- Ignoring data quality. Check for duplicates, blanks and odd values before you trust any result.
Learning data analytics at ASTI
ASTI’s Data Analytics course follows the same order as this guide: statistics, Excel and SQL foundations, then dashboards in Power BI and Tableau, and Python for data analysis, with practical assignments and real-world data projects. It runs for about four months, in the classroom at BTM 1st Stage, Bangalore, or live online, and no prior programming experience is required. It includes placement and career support; placement assistance does not guarantee a job.
Not sure whether analytics or data science suits you better? Read data analytics vs data science. When you reach the dashboard step, see the Power BI certification (PL-300) guide and Power BI jobs in Bangalore. If you are also considering data science, read how to start learning data science from scratch, or compare all your options in which software course to choose.
Keep going: read SQL for data analysis, Tableau vs Power BI, data analyst interview questions, data analyst resume for freshers and data analytics syllabus.
Frequently asked questions
How long does it take to become a data analyst?
It depends on your starting point and how many hours you practise each week. As a reference, ASTI’s Data Analytics course runs for about four months. Allow extra time after that to finish portfolio projects and apply for jobs.
Do data analysts need to know coding?
You need SQL, which is a query language, and it helps to know basic Python. You do not need to be a software developer. Many analysts work mainly in SQL, Excel and Power BI or Tableau.
Should I learn Power BI or Tableau first?
Either is fine, because the ideas transfer. Search job posts in your city for the roles you want and pick the tool they mention more often.
What is the difference between a data analyst and a data scientist?
A data analyst mainly explains what happened and why, using SQL, Excel and dashboards. A data scientist more often builds predictive models with Python and machine learning. Analyst roles are usually the easier entry point.
Which job titles should I search for?
Try data analyst, junior data analyst, business analyst, MIS analyst, reporting analyst and Power BI developer. The work overlaps, so read the job description rather than relying on the title.
Sources
- Microsoft Certified: Power BI Data Analyst Associate, Microsoft Learn
- pandas: Getting started, pandas documentation
- MySQL tutorial, MySQL reference manual
- Course details: the Data Analytics course page, checked 29 Oct 2026