Data Analytics — Live Training

An industry-standard data analytics program taught live every weekend on real datasets. You build the complete analyst toolkit — spreadsheets, Python and Pandas, statistics, SQL, dashboards and predictive modeling — the same stack analysts use at product companies and consultancies. Every class is recorded into your LMS with notes, quizzes and assignments, so you can rewatch anything anytime. After the curriculum you move straight into a guaranteed analytics internship project from a real brief, reviewed by our team — and finish with placement and interview preparation built on the analysis you actually delivered.

What you will learn

• Analyse business problems with a structured, data-first approach
• Master spreadsheets for quick analysis and reporting
• Clean, transform and explore data with Python and Pandas
• Visualize data and tell clear stories with charts
• Apply statistics: distributions, hypothesis tests and A/B experiments
• Query, join and aggregate data with SQL and window functions
• Design KPIs and build dashboards with Power BI and Streamlit
• Build baseline predictive models with scikit-learn
• Deliver a reviewed analytics internship project and get interview-ready

Syllabus

What the training covers. Sessions, recordings and material open up once you join a batch.

1. 01

   Week 1 · Thinking in Data & Spreadsheets

   What analysts actually do. Framing business questions, spreadsheet analysis: formulas, lookups, pivot tables and charts.

2. 02

   Week 2 · Python for Analysts

   Python setup and Jupyter notebooks, syntax, data types, loops and functions — the working minimum every analyst needs.

3. 03

   Week 3 · NumPy & Pandas I

   Arrays, series and dataframes. Loading CSVs, Excel and JSON, selecting and filtering, and first real-dataset explorations.

4. 04

   Week 4 · Pandas II: Cleaning & Transforming

   Missing data, duplicates and outliers. Merges and joins, groupby aggregations, reshaping and feature columns.

5. 05

   Week 5 · EDA & Visualization

   The exploratory data analysis workflow. Matplotlib and Seaborn, choosing the right chart and telling stories with data.

6. 06

   Week 6 · Statistics I

   Descriptive statistics, probability, distributions and sampling — the foundations of every serious analysis.

7. 07

   Week 7 · Statistics II: Testing & Experiments

   Confidence intervals, hypothesis testing, p-values, and designing and reading A/B experiments.

8. 08

   Week 8 · SQL for Analytics I

   SELECTs, filters, joins and aggregations on a real warehouse-style schema.

9. 09

   Week 9 · SQL II & Python + Databases

   Window functions, CTEs and subqueries. Connecting Python to databases and automating data pulls.

10. 10

    Week 10 · Dashboards & KPI Design

    Metric trees and KPI design. Building dashboards in Power BI and Streamlit that people actually use.

11. 11

    Week 11 · Predictive Analytics

    Regression and classification with scikit-learn, feature engineering, model evaluation and honest reporting of results.

12. 12

    Week 12 · Analytics in Production & Capstone Kickoff

    Automated reporting, data quality, analytics case studies — and scoping your capstone and internship project.

13. 13

    Internship Project

    A guaranteed analytics internship project from a real brief and dataset. End-to-end analysis and dashboard, submitted and reviewed by our team — pass to earn your verifiable certificate and offer letter.

14. 14

    Placement & Interview Prep

    Portfolio and resume built on your delivered analysis, SQL and case-style interview questions, and mock interviews so you walk into placements with proof.

Who it is for

• College students from any stream aiming for analyst roles
• Final-year students preparing for placements
• Commerce, business and engineering students alike — no coding background needed
• Anyone who wants to work with data professionally

Questions & answers