📊 SQL + Python for Data Analytics

🗓 Starting: 21st February

🎯 Goal: Make you industry-ready Data Analyst (Product / Business / Reporting)

🔹 MODULE 1: DATA ANALYTICS FOUNDATION

(For absolute clarity before tools)

1. What is Data?
2. Types of Data (Structured / Semi / Unstructured)
3. What is Data Analytics?
4. Data Analyst vs Business Analyst vs Product Analyst
5. Analytics Lifecycle
6. Real-life use cases (E-commerce, EdTech, FinTech)
7. Tools used by Data Analysts
8. How SQL & Python fit in analytics

🔹 MODULE 2: SQL – CORE TO ADVANCED (MOST IMPORTANT 🔥)

🟦 SQL BASICS

1. What is SQL?
2. Databases & DBMS
3. Tables, Rows, Columns
4. SQL Data Types
5. DDL, DML, DCL, TCL
6. CREATE, DROP, ALTER, TRUNCATE
7. INSERT, UPDATE, DELETE
8. SELECT statement
9. WHERE clause
10. DISTINCT
11. ORDER BY
12. LIMIT / TOP

🟦 SQL FILTERING & CONDITIONS

1. AND, OR, NOT
2. BETWEEN
3. IN
4. LIKE
5. IS NULL / IS NOT NULL
6. CASE WHEN (Business logic)

🟦 AGGREGATION & GROUPING

1. COUNT, SUM, AVG, MIN, MAX
2. GROUP BY
3. HAVING
4. Real business examples (sales, users, revenue)

🟦 JOINS (VERY IMPORTANT)

1. INNER JOIN
2. LEFT JOIN
3. RIGHT JOIN
4. FULL JOIN
5. SELF JOIN
6. CROSS JOIN
7. One-to-One
8. One-to-Many
9. Many-to-Many
10. Join vs Subquery

🟦 SUBQUERIES

1. Scalar subquery
2. Row subquery
3. Column subquery
4. Correlated subquery
5. Nested queries
6. Real interview questions

🟦 WINDOW FUNCTIONS (ADVANCED 🔥)

1. ROW_NUMBER
2. RANK
3. DENSE_RANK
4. LEAD & LAG
5. Running total
6. Moving average
7. Partition vs Order
8. Salary comparison problems
9. Product analytics use cases

🟦 CTE (WITH CLAUSE)

1. What is CTE
2. Why use CTE
3. Recursive CTE
4. CTE vs Subquery
5. Update using CTE

🟦 DATE & STRING FUNCTIONS

1. Date formats
2. Date difference
3. Month, Year extraction
4. String functions
5. Data cleaning using SQL

🟦 SQL PERFORMANCE & OPTIMIZATION

1. Indexes (Clustered / Non-clustered)
2. Query optimization basics
3. Execution order of SQL
4. Common SQL mistakes
5. Writing clean & readable SQL

🟦 REAL-TIME SQL SCENARIOS

1. User funnel analysis
2. Retention queries
3. Error logs analysis
4. Consecutive event problems
5. Product metrics using SQL
6. Interview-level questions (Google, Amazon, Meta style)

🔹 MODULE 3: PYTHON FOR DATA ANALYTICS

🟦 PYTHON BASICS

1. What is Python?
2. Why Python for Analytics?
3. Variables
4. Data types
5. Input / Output
6. Type conversion

🟦 CONTROL STRUCTURES

1. if-else
2. nested conditions
3. loops (for, while)
4. break, continue, pass

🟦 DATA STRUCTURES (VERY IMPORTANT)

1. List
2. Tuple
3. Set
4. Dictionary
5. List vs Tuple vs Set
6. Dictionary for analytics logic

🟦 FUNCTIONS

1. User-defined functions
2. Arguments & return
3. Reusability
4. Real-life examples

🟦 FILE HANDLING

1. Reading CSV files
2. Writing files
3. Data cleaning using Python

🟦 PYTHON FOR DATA ANALYSIS (CORE 🔥)

1. Working with datasets
2. Data cleaning
3. Handling missing values
4. Data transformation
5. Business logic implementation
6. SQL + Python combined thinking

(Optional libraries if needed based on batch)

1. pandas (basic)
2. numpy (basic)

🔹 MODULE 4: ANALYTICS THINKING & METRICS

1. KPI vs Metrics
2. Business questions → SQL/Python logic
3. Product analytics metrics
4. Funnel, Retention, Conversion
5. Case studies

🔹 MODULE 5: PROJECTS (REAL-WORLD 🔥)

1. SQL-based analytics project
2. Python data analysis project
3. End-to-end business problem
4. How to explain projects in interviews

🔹 MODULE 6: INTERVIEW PREPARATION

1. SQL interview questions (Easy → Hard)
2. Python interview questions
3. Case-study rounds
4. Resume SQL project explanation
5. How to think like interviewer
6. Mock interview guidance

🎯 FINAL OUTCOME

After this course, you’ll be able to:

1. Write production-level SQL
2. Solve real interview SQL problems
3. Use Python for data analysis
4. Crack Data Analyst / Product Analyst interviews
5. Think like an industry Data Analyst