Retail sales performance dashboard
Deliverable: A Power BI report over three years of transaction data with a documented star schema, time-intelligence measures, and a one-page written summary of three findings and their business implications.
Course
Data Analytics
A structured path from spreadsheets to SQL, Power BI and Python, built around the four things a data analyst is actually paid to do — pull the data, clean it, analyse it and explain it.
- Duration: 5 months
- Classroom · Online live
- Level: beginner to advanced
What you will be able to do
- Write SQL joins, aggregations, subqueries and window functions against a real relational database
- Clean and reshape messy data using Excel formulas, Power Query and pandas
- Build interactive Power BI dashboards with a documented data model and DAX measures
- Choose the right chart for a question and explain what the numbers do and do not show
- Present an analysis to a non-technical stakeholder and defend the method behind it
Who this course is for
- Graduates from any stream, including commerce and arts backgrounds
- Final-year students who can commit to two sessions a week
- Working professionals moving into analytics from operations, finance, sales or support
- No prior programming experience is required
Syllabus
7 modules · 5 months
Module 1. Analytical foundations and Excel
- How an analytics question becomes a data question
- Lookup, text, date and conditional functions
- PivotTables and grouped summaries
- Power Query for repeatable cleaning
- Building a reusable Excel reporting workbook
Module 2. SQL for analysts
- Relational modelling, keys and normalisation in practice
- SELECT, WHERE, GROUP BY and HAVING
- INNER, LEFT and self joins
- Subqueries and common table expressions
- Window functions for ranking, running totals and period comparisons
- Query performance and reading an execution plan
Module 3. Statistics that analysts actually use
- Distributions, central tendency and spread
- Correlation versus causation, with worked counter-examples
- Sampling, confidence intervals and what they really claim
- A/B test reading and common misinterpretations
Module 4. Power BI and data modelling
- Connecting, transforming and loading sources
- Star schema modelling and relationship cardinality
- DAX measures, calculated columns and time intelligence
- Interactive report design, bookmarks and drill-through
- Publishing, refresh schedules and row-level security
Module 5. Python for data analysis
- Python fundamentals for people who do not intend to be developers
- pandas Series, DataFrames, indexing and joins
- Grouping, pivoting and handling missing data
- Charting with matplotlib and seaborn
- Automating a recurring report end to end
Module 6. Communicating analysis
- Choosing a chart that answers the question asked
- Writing an executive summary an executive will read
- Documenting assumptions and known data quality gaps
- Presenting findings and handling challenge in the room
Module 7. Portfolio and interview preparation
- Turning coursework into a defensible portfolio
- Writing a data analyst CV that survives keyword screening
- SQL and case-study interview practice
- Mock interviews with structured feedback
Tools and technologies you will use
- Microsoft Excel
- Power Query
- SQL (MySQL and PostgreSQL)
- Power BI Desktop
- DAX
- Python
- pandas
- Jupyter Notebook
- Git
Projects you will build
Customer churn analysis in SQL
Deliverable: A SQL script and short report identifying churn drivers in a subscription dataset, with cohort retention tables and a stated confidence level for each conclusion.
Automated operations report in Python
Deliverable: A Python notebook that ingests a raw CSV export, cleans it, produces four standard charts and writes a formatted summary file, runnable end to end without manual steps.
Public dataset investigation
Deliverable: An independent analysis of an Indian public dataset of your choice, presented in ten minutes with the method, the caveats and the conclusion documented.
Where this course can take you
- Data Analyst
- Business Analyst
- MIS Executive
- Reporting Analyst
- Power BI Developer
Duration, modes and fees
- Duration
- 5 months
- Delivery modes
- Classroom · Online live
- Fees
- Share your details for the current fee
- Fees vary by batch and delivery mode. Share your details and an advisor will confirm the current fee.
Placement assistance
Every student gets placement assistance — that is what 100% placement assistance means. It is support for all, not a job for all. We do not promise a specific salary, a specific number of interviews, or placement at any named company, and you should be wary of anyone who does.
What is included
- A place in the monthly placement drive, held every third Saturday
- The readiness programme every second Saturday — mock interviews and preparation
- CV review against the specific roles you are targeting
- Portfolio review, so your project work is presented the way a reviewer will read it
- Access to the vacancy pool employers send directly to the Samyak network
- Guidance on which roles realistically fit your background and which do not
- A place in the next drive, with coaching, if you are not selected in this one
What is not included
- Any guarantee of a job, an interview, or a particular salary
- Placement at a named or partner company
- Applying to jobs on your behalf
- Support before you have completed the course and its project work
- Visa, relocation or overseas placement assistance
Why this course is sequenced the way it is
Most analytics courses open with Python because it looks impressive. That ordering fails people. Python is a tool for solving data problems you can already recognise, and if you have never struggled to reconcile two spreadsheets or write a join that returns the wrong row count, the language is just syntax.
So we start where the work starts. Excel first, because it is still how most Indian businesses hold their data and how most analysts are first asked to prove themselves. SQL second, because it is the single skill that appears in almost every analyst job description and every analyst interview. Power BI third, because a dashboard is how your analysis reaches the people who act on it. Python last, once you know exactly what you want to automate.
What “job-oriented” means here, specifically
Every module ends in something you could show an interviewer. The four projects are not exercises with a known answer — each one hands you imperfect data and asks for a defensible conclusion, which is the actual job. The portfolio module then teaches you to talk about that work, because an analyst who cannot explain their method does not get past the second round.
Who tends to struggle
We would rather say this than have you find out in month three. Learners who treat SQL as syntax to memorise struggle badly in the window functions module. Learners who skip the statistics module because it looks theoretical produce confident, wrong conclusions in their projects. Both modules reward doing the practice between sessions, and both are where we push hardest during the course.
Questions
Data Analytics — frequently asked questions
Do I need to know programming before joining the data analytics course?
No. The course starts with Excel and SQL, neither of which requires programming experience. Python is introduced in module five, after you already understand the data problems it is solving, which makes it considerably easier to pick up.
Is Power BI enough, or do I need Tableau as well?
For the Indian job market, Power BI appears in substantially more analyst job postings than Tableau, so we teach it in depth rather than covering both shallowly. The modelling and visualisation concepts transfer directly if a later employer uses Tableau.
How much SQL do data analyst interviews actually test?
Most analyst interviews include a live SQL round covering joins, aggregation and at least one window function. Module two covers all three in depth, and the interview preparation module runs timed practice on the same question patterns.
Can I take this course while working full time?
Yes. The course runs as weekday evening and weekend batches in both classroom and online live formats. Expect around six hours a week of class time plus project work between sessions.
What is the difference between this and the data science course?
Data analytics focuses on explaining what happened and why, using SQL, dashboards and statistics. Data science extends into predictive modelling and machine learning. Analytics roles are more numerous at entry level, which is why we recommend starting here.
Enquire about Data Analytics
Three details is all we need. A course advisor will call you back.
Related courses
- Artificial Intelligence
Python, machine learning foundations and applied generative AI in one track — ending with a retrieval-augmented application you have built, evaluated and can explain end to end.
- Full Stack Development
Front end, back end, databases and deployment taught as one connected system, ending with applications you have shipped to a public URL and can walk an interviewer through.
Next step
Talk to a course advisor
Tell us what you want to learn and we will help you pick the right course, batch and mode.