Data Analytics Bootcamp
An 8-week, project-led bootcamp that takes you from raw spreadsheets to dashboards using Excel, SQL, Python and Power BI.
Curriculum
GAU-01Excel for Analysis3 lessons · 6 h
- Pivot tables
- Lookup functions
- Data cleaning
GAU-02SQL Fundamentals4 lessons · 6 h
- SELECT statements
- Joins
- GROUP BY
- Subqueries
GAU-03SQL for Business Questions3 lessons · 6 h
- Window functions
- Running totals
- Cohort-style queries
GAU-04Python for Data Work3 lessons · 6 h
- Pandas and NumPy
- Cleaning, merging and reshaping data
- Automating recurring reports
GAU-05Statistics for Decisions3 lessons · 6 h
- Distributions
- Correlation vs causation
- A/B test basics
GAU-06Power BI: Building Dashboards3 lessons · 6 h
- Data modelling
- DAX fundamentals
- Stakeholder-friendly dashboard design
GAU-07Data Storytelling & Communication1 lessons · 6 h
- Turning dashboards into written recommendations
GAU-08Capstone Project4 lessons · 6 h
- SQL extraction
- Python cleaning
- Power BI dashboard
- Written findings summary
What's included
Questions
Do I need prior experience?
This course is set at beginner level. The first module starts from the basics, so you only need the time and a laptop or phone with a stable connection.
What if I miss a live class?
Every live class is recorded and posted within 24 hours. You keep access to the recordings for 12 months.
When are the classes?
The next cohort starts soon (date to be announced) and runs for 8 weeks, ~6 hrs per week. The full timetable and class link appear in My courses before the first class.
Which language is the course taught in?
English.
How do I pay?
Online through UPI, cards, net banking or wallets, processed by Razorpay. You get a receipt by email and the course appears in My courses right away.
Q01What is the difference between WHERE and HAVING?SQLBasic
WHERE filters individual rows before grouping. HAVING filters groups after aggregation.
Example: WHERE country = 'IN' keeps Indian orders, then HAVING SUM(amount) > 10000 keeps only customers whose total crosses ₹10,000.
Q02What is the difference between an INNER JOIN and a LEFT JOIN?SQLBasic
An inner join keeps only rows that match in both tables. A left join keeps every row from the left table and fills NULLs where there is no match.
Use a left join to find customers with no orders: LEFT JOIN orders and keep rows where the order ID is NULL.
Q03In Power BI, what is the difference between a calculated column and a measure?Power BIBasic
A calculated column is computed row by row when the data refreshes and is stored in the model. A measure is computed at query time, in the filter context of the visual.
Use measures for totals, ratios and anything that should respond to slicers.
Q04How do ROW_NUMBER, RANK and DENSE_RANK differ?SQLIntermediate
They differ only on ties. For scores 90, 90, 80: ROW_NUMBER gives 1, 2, 3; RANK gives 1, 1, 3; DENSE_RANK gives 1, 1, 2.
Use ROW_NUMBER to pick exactly one row per group, such as each customer's latest order.
Q05How do you calculate month-over-month revenue growth in SQL?SQLIntermediate
Aggregate revenue by month first, then use LAG(revenue) OVER (ORDER BY month) to bring in the previous month.
Growth = (current − previous) / previous. Guard against division by zero, and handle the first month, which has no previous value.
Q06How do you handle missing values?Data cleaningIntermediate
First find out why the data is missing, because random gaps and systematic gaps need different treatment.
Options: drop rows when few are affected, impute with the median or mode, forward-fill time series, or add a flag column marking imputed values. Record what you did and why.
Q07What does a p-value tell you in an A/B test?StatisticsIntermediate
It is the probability of seeing a difference at least this large if there were really no difference between the variants.
A small p-value, commonly below 0.05, is evidence against 'no difference'. It is not the probability that the variant is better. Fix the sample size in advance and avoid stopping the test early when the result looks good.
Q08Give an example of correlation without causation.StatisticsIntermediate
Ice cream sales and drowning incidents rise together, but both are driven by hot weather. Hot weather is a confounder.
To show causation you need a controlled experiment or careful methods that account for confounders.
Q09How would you analyse customer churn for a subscription business?CaseAdvanced
Start by defining churn precisely, for example not renewing within 30 days of expiry. Churn rate = customers lost in the period ÷ customers at the start.
Then run cohort analysis by signup month, compare churn across plans, channels and usage levels, separate voluntary cancellations from failed payments, and recommend actions for the segments that churn most.
Also on its own page: Data Analytics Bootcamp interview questions