What You'll Learn
Prerequisites
- No prior programming experience needed
- Basic computer knowledge
- A laptop with internet access
- Enthusiasm to learn!
Tools & Setup
📖 Tutorial Chapters & Curriculum
Follow the structured learning path from beginner to advanced
1
What Is Data Analytics and Why It Matters
A beginner-friendly introduction to data analytics, its purpose, and why businesses depend on it.
2
Excel Essentials for Data Analysis Students
Build a strong Excel foundation for sorting, filtering, formulas, and analysis.
3
SQL Fundamentals for Beginner Data Analysts
Start with SQL basics including SELECT, WHERE, ORDER BY, and LIMIT.
4
Why Data Cleaning Comes Before Analysis
Understand why messy data leads to weak insights and poor decisions.
5
Introduction to Data Visualization for Beginners
Learn why charts matter and how visuals improve understanding.
6
Python Setup and Basics for Data Analysts
Get started with Python syntax, notebooks, and beginner-friendly examples.
7
Descriptive Statistics Every Analyst Must Know
Learn mean, median, mode, range, variance, and standard deviation.
8
Introduction to Power BI and Tableau
Understand what BI tools do and when to use them.
9
Normalization and Table Design for Analysts
Understand table structure, keys, and normalization with examples.
10
What Makes a Good KPI
- What Makes a Good KPI 8 min
11
A B Testing Basics for Analysts
Understand control groups, variants, and experiment objectives.
12
Introduction to Time Series Data
Learn how time-based data differs from regular tabular datasets.
13
Why Data Governance Matters
Understand ownership, definitions, and trust in business data.
14
Sales Performance Analysis Case Study
Walk through a beginner-friendly sales analysis project.
15
How to Prepare for a Data Analyst Interview
Build a practical interview preparation strategy.
16
The Data Analytics Lifecycle from Raw Data to Decisions
Understand the full analytics lifecycle from collection to action.
17
Excel Formulas Every Data Analyst Should Know
Master practical Excel formulas used in day-to-day reporting.
18
Filtering, Sorting and Aggregating Data in SQL
Use WHERE, GROUP BY, HAVING, and ORDER BY to explore patterns.
19
Handling Missing Values the Smart Way
Learn when to delete, fill, or flag missing values in a dataset.
20
How to Choose the Right Chart for Your Data
Select charts based on comparison, trend, distribution, or composition.
21
Working with Pandas DataFrames Step by Step
Learn to load, inspect, filter, and transform data in Pandas.
22
Probability Basics Explained for Data Analysis
Build intuition for probability in business and analytics problems.
23
Building Your First Power BI Report
Create a simple report with visuals, filters, and business metrics.
24
Indexes and Query Optimization Basics
Learn how databases find rows faster and why performance matters.
25
Sales and Marketing Metrics Every Analyst Sees
Understand conversion rate, CAC, ROAS, revenue, and retention.
26
Sample Size, Randomization and Bias
Learn why experiment design matters before looking at results.
27
Trend, Seasonality and Noise Explained
Break a time series into the main moving parts.
28
Data Privacy, Consent and Responsible Use
Learn privacy basics every analyst should respect.
29
Marketing Funnel Analysis Project
Analyze a simple funnel from impressions to conversion.
30
SQL Interview Questions and Thinking Patterns
Practice the kind of SQL logic interviewers expect.
31
Descriptive, Diagnostic, Predictive and Prescriptive Analytics
Learn the four major types of analytics with simple business examples.
32
Pivot Tables and Pivot Charts Made Simple
Learn how pivot tables summarize data quickly and clearly.
33
Understanding SQL Joins with Real Examples
Learn INNER JOIN, LEFT JOIN, and how tables connect in analysis.
34
Removing Duplicates, Outliers and Inconsistencies
Clean duplicate records and identify suspicious values before analysis.
35
Dashboard Design Principles for Analysts
Create dashboards that are readable, focused, and decision-friendly.
36
NumPy Basics for Fast Numerical Analysis
Understand arrays, vectorized operations, and simple numerical workflows.
37
Understanding Distributions in Real Data
Explore normal distribution, skewness, and spread with easy examples.
38
Tableau Basics for Interactive Dashboards
Learn the Tableau workflow for clean interactive analysis.
39
Window Functions for Analytical SQL
Use ROW_NUMBER, RANK, SUM OVER, and moving calculations.
40
Product and Operations Metrics Explained
Track engagement, churn, utilization, and service quality.
41
Reading P Values and Confidence Intervals
Interpret statistical output without overcomplicating the idea.
42
Moving Averages and Smoothing Techniques
Reduce noise and understand short-term versus long-term behavior.
43
Bias, Fairness and Ethical Analytics
See how biased data can harm decisions and people.
44
Customer Churn Analysis Portfolio Example
Study how analysts investigate retention and churn patterns.
45
Case Study and Business Problem Solving Rounds
Learn how to structure answers in analytics case discussions.
46
A Day in the Life of a Data Analyst
Explore the role, mindset, and daily responsibilities of a data analyst.
47
Creating Clean Excel Dashboards for Reports
Design student-friendly dashboards that look professional and easy to read.
48
Subqueries, CTEs and Analytical Thinking in SQL
Go beyond basics with layered SQL logic that mirrors real business questions.
49
Transforming Raw Data into Analysis Ready Tables
Use formatting, splitting, merging and standardization to prepare data.
50
Storytelling with Data for Business Presentations
Turn numbers into a business story that stakeholders can follow.
51
Creating Charts with Matplotlib and Seaborn
Visualize trends and patterns in Python using common plotting libraries.
52
Hypothesis Testing for Business Decisions
Use p-values and significance to test ideas with confidence.
53
Calculated Fields, KPIs and Drill Downs in BI
Use KPIs, measures, and interactions to make dashboards useful.
54
Building Simple Data Models for Reporting
Connect facts and dimensions in a reporting-friendly structure.
55
How to Build a KPI Tracking Framework
Create a structured metric system aligned with business goals.
56
From Experiment Results to Business Decisions
Translate test outcomes into product or marketing actions.
57
Forecasting Basics for Demand and Sales
Build simple forecasting intuition for business planning.
58
Building Good Data Habits in Teams
Create documentation, definitions, and governance-friendly workflows.
59
Financial Dashboard and Operations Reporting Project
Combine metrics into a reporting project suitable for a portfolio.
60
Portfolio Resume and Dashboard Presentation Tips
Present projects and dashboards with clarity and confidence.
🎯 Interview Preparation
Top Python interview questions organized by difficulty
Easy
50 Questions
Freshers / Entry Level
- Write and explain SQL queries related to WHERE Clause Filtering. Provide optimization considerations and edge case handling. (Q1)
- Write and explain SQL queries related to GROUP BY & Aggregations. Provide optimization considerations and edge case handling. (Q2)
- Write and explain SQL queries related to HAVING Clause. Provide optimization considerations and edge case handling. (Q3)
- Write and explain SQL queries related to INNER JOIN. Provide optimization considerations and edge case handling. (Q4)
- Write and explain SQL queries related to LEFT JOIN. Provide optimization considerations and edge case handling. (Q5)
Medium
60 Questions
Experienced / Mid-Level
- Write and explain SQL queries related to Date Functions. Provide optimization considerations and edge case handling. (Q51)
- Write and explain SQL queries related to String Functions. Provide optimization considerations and edge case handling. (Q52)
- Write and explain SQL queries related to NULL Handling. Provide optimization considerations and edge case handling. (Q53)
- Write and explain SQL queries related to Coalesce & NVL. Provide optimization considerations and edge case handling. (Q54)
- Write and explain SQL queries related to Union vs Union All. Provide optimization considerations and edge case handling. (Q55)
Hard
50 Questions
Senior / Lead Level
- Write and explain SQL queries related to Date Functions. Provide optimization considerations and edge case handling. (Q111)
- Write and explain SQL queries related to String Functions. Provide optimization considerations and edge case handling. (Q112)
- Write and explain SQL queries related to NULL Handling. Provide optimization considerations and edge case handling. (Q113)
- Write and explain SQL queries related to Coalesce & NVL. Provide optimization considerations and edge case handling. (Q114)
- Write and explain SQL queries related to Union vs Union All. Provide optimization considerations and edge case handling. (Q115)
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