Python | SQL Live Course with Pragya Rathi

Timings for the Course - Starting Date - 18 November

Tuesday - 10 pm IST - 11 PM IST

Saturday & Sunday - 8pm - 9 pm IST

Python & SQL for Data Analytics Course Outline

Section 1: Introduction to Python for Data Analytics

• 1.1 Python Basics - Overview of Python, setting up the environment.
• 1.2 Data Types and Variables - Understanding data types and basic operations.

Section 2: Python Data Structures

• 2.1 Lists - Creating, accessing, and modifying lists.
• 2.2 Tuples - Using immutable sequences for data.
• 2.3 Sets - Unique values and set operations.
• 2.4 Dictionaries - Key-value pairs and manipulation.
• 2.5 Strings - String manipulation and methods.

Section 3: Control Flow and Functions

• 3.1 Conditionals - Using if, elif, else for decisions.
• 3.2 Loops - for and while loops for iteration.
• 3.3 Functions - Defining and using functions.
• 3.4 List Comprehensions - Concise data manipulation.

Section 4: Numpy for Numerical Data

• 4.1 Introduction to Numpy - Arrays and basic operations.
• 4.2 Numpy Operations - Array math, reshaping, and aggregations.
• 4.3 Indexing and Slicing - Accessing array elements.

Section 5: Pandas for Data Analysis

• 5.1 Introduction to Pandas - Working with DataFrames and Series.
• 5.2 Data Cleaning - Handling missing values, filtering, and type conversion.
• 5.3 Data Manipulation - Sorting, grouping, merging, and aggregations.

Section 6: Data Visualization

• 6.1 Matplotlib and Seaborn Basics - Basic plotting and customization.
• 6.2 Quick Plotting with Pandas - DataFrame visualizations.

Section 7: Problem-Solving with Python

• 7.1 Problem-Solving Strategies - Planning and structuring solutions.
• 7.2 Practice Exercises - Data manipulation and analytics problems.

Section 8: Advanced Pandas Techniques

• 8.1 Time Series Analysis - Date-time functions and time-based analysis.
• 8.2 Window Functions - Rolling and expanding calculations.

Section 9: Web Scraping with Python

• 9.1 Introduction to Web Scraping - Basics and ethics.
• 9.2 Fetching Content with Requests - Accessing web data.
• 9.3 HTML Parsing with BeautifulSoup - Extracting data from HTML.

Section 10: Data Analysis Project

• 10.1 End-to-End Data Analysis - Importing, cleaning, and analyzing data.

SQL Course Outline

Section 1: Introduction to SQL and Databases

• 1.1 Data, Database, DBMS, SQL - Overview of data types, databases, DBMS, and SQL.
• 1.2 Installing MS SQL Server - Guide to setting up Microsoft SQL Server.

Section 2: DDL and DML Commands

• 2.1 DDL vs. DML Commands - Differences and use cases.
• 2.2 DDL Commands - CREATE, ALTER, DROP, TRUNCATE.
• 2.3 DML Commands - SELECT, INSERT, UPDATE, DELETE.

Section 3: SQL Functions

• 3.1 Introduction to SQL Functions.
• 3.2 Aggregate Functions - SUM, COUNT, AVG, MAX, MIN.
• 3.3 String Functions - LEN, UPPER, LOWER, SUBSTRING.
• 3.4 Date/Time Functions - GETDATE, DATEADD, DATEDIFF.
• 3.5 Conversion/Math Functions - CONVERT, CAST, ROUND, ABS.

Section 4: SQL Clauses

• 4.1 Basic Clauses - SELECT, FROM, WHERE, ORDER BY, TOP.
• 4.2 GROUP BY and HAVING Clauses - Grouping and conditions on groups.

Section 5: CASE WHEN Statements

• 5.1 CASE WHEN Basics - Conditional logic in SQL.
• 5.2 CASE WHEN with Aggregates and ORDER BY.

Section 6: SQL Joins

• 6.1 INNER, LEFT, RIGHT Joins - Combining tables.
• 6.2 OUTER and CROSS Joins.
• 6.3 Self Join - Joining a table to itself.

Section 7: SQL Set Operations

• 7.1 UNION and UNION ALL - Merging query results.
• 7.2 INTERSECT and EXCEPT - Common and unique records.

Section 8: Common Table Expressions (CTEs)

• 8.1 Introduction to CTEs - Simplifying complex queries.
• 8.2 Practical CTE Use.

Section 9: Subqueries

• 9.1 Introduction to Subqueries - Nested queries.
• 9.2 Types of Subqueries - Single-row and multi-row.
• 9.3 Correlated Subqueries - Advanced subqueries.

Section 10: Window Functions

• 10.1 Window Functions Basics.
• 10.2 Using OVER and PARTITION BY.
• 10.3 ROWS BETWEEN Clause.
• 10.4 Ranking Functions - RANK, DENSE_RANK, ROW_NUMBER.
• 10.5 LEAD and LAG - Accessing adjacent rows.