Admissions Open for JANUARY Batch
Learn Python and SQL together to solve real data problems, automate tasks, and build smarter decision-making solutions.
Days : Tue & Thu
Duration : 9 Hours
Timings: 10 AM - 12 PM IST
Try Risk-free, 15 Days Money Back Guarantee
Python & SQL Foundations
Master Python data manipulation with Pandas and SQL for querying, cleaning, integration, and advanced database connections from basics upward.
Online Live Instructor-Led Learning
9 Hours
10 AM - 12 PM
Sat & Sun
By end of this course
Get stronger in
Learn essential Python Pandas for data manipulation, cleaning, and basic operations
Master SQL fundamentals, including queries, filtering, sorting, aggregate functions, joins, and advanced techniques.
Integrate Python with SQL to connect to databases, execute queries, and handle data flow between them.
Get familier with
Become familiar with setting up your development environment, including Python, VS Code, and PostgreSQL.
Understand the importance of virtual environments and version control with GitHub
Learn professional folder organization for data projects..
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is the foundation for anyone starting their data engineering career track
Prerequisites
Basic Python programming knowledge, understanding of SQL fundamentals, familiarity with command line/terminal.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Basic Python programming knowledge, understanding of SQL fundamentals, familiarity with command line/terminal.
Who Should Enroll?
This course is the foundation for anyone starting their data engineering career track
By the end of this course
Get Stronger in
- Learn essential Python Pandas for data manipulation, cleaning, and basic operations.
- Master SQL fundamentals, including queries, filtering, sorting, aggregate functions, joins,
and advanced techniques. - Integrate Python with SQL to connect to databases, execute queries, and handle data
flow between them.
Get Familiar in
- Become familiar with setting up your development environment, including Python, VS Code, and PostgreSQL
- Understand the importance of virtual environments and version control with GitHub.
- Learn professional folder organization for data projects.
Course Contents
Content
Install Python, VS Code, and PostgreSQL on your system. Set up your first virtual environment. Create a GitHub account and understand why version control matters in data jobs. Learn professional folder organization.
Content
Learn why Pandas is essential for data work. Create DataFrames from lists, dictionaries, and CSV files. Explore basic operations: head(), tail(), info(), describe().
Content
Handle missing values with dropna() and fillna(). Filter and select data using conditions. Rename columns and change data types. Clean messy real-world datasets.
Content
Understand databases and tables. Write your first SELECT queries. Filter data using WHERE clause. Sort results with ORDER BY. Use DISTINCT and LIMIT.
Content
Learn aggregate functions: COUNT, SUM, AVG, MIN, MAX. Group data with GROUP BY. Filter groups using HAVING. Understand the difference between WHERE and HAVING.
Content
Master INNER JOIN to combine tables. Learn LEFT, RIGHT, and FULL OUTER joins. Understand when to use each join type. Practice with real multi-table databases.
Content
Write subqueries for complex filtering. Use CASE statements for conditional logic. Learn UNION to combine query results. Understand NULL handling in SQL.
Topics
Connect to PostgreSQL from Python using psycopg2. Execute SQL queries from Python code. Load query results into Pandas Data Frames. Insert data from Python into databases.
Topics
Build a data analysis project: Load a CSV dataset, clean it with Pandas, store it in PostgreSQL, run analysis queries, export results. Document your code with comments.
Content
Install Python, VS Code, and PostgreSQL on your system. Set up your first virtual environment. Create a GitHub account and understand why version control matters in data jobs. Learn professional folder organization.
Content
Learn why Pandas is essential for data work. Create DataFrames from lists, dictionaries, and CSV files. Explore basic operations: head(), tail(), info(), describe().
Content
Handle missing values with dropna() and fillna(). Filter and select data using conditions. Rename columns and change data types. Clean messy real-world datasets.
Content
Understand databases and tables. Write your first SELECT queries. Filter data using WHERE clause. Sort results with ORDER BY. Use DISTINCT and LIMIT.
Content
Learn aggregate functions: COUNT, SUM, AVG, MIN, MAX. Group data with GROUP BY. Filter groups using HAVING. Understand the difference between WHERE and HAVING.
Content
Master INNER JOIN to combine tables. Learn LEFT, RIGHT, and FULL OUTER joins. Understand when to use each join type. Practice with real multi-table databases.
Content
Write subqueries for complex filtering. Use CASE statements for conditional logic. Learn UNION to combine query results. Understand NULL handling in SQL.
Topics
Connect to PostgreSQL from Python using psycopg2. Execute SQL queries from Python code. Load query results into Pandas Data Frames. Insert data from Python into databases.
Topics
Build a data analysis project: Load a CSV dataset, clean it with Pandas, store it in PostgreSQL, run analysis queries, export results. Document your code with comments.
What is covered: Vectors, matrices, operations like addition and multiplication.
Application: Data representation, image processing, neural networks.
Example:
1. Image as Matrix: A grayscale image is a matrix of pixel values. Neural networks process these matrices to recognize objects.
2. Matrix Multiplication: Used to combine weights and inputs in every layer of a neural network.
What is covered: Studying change (derivatives), finding minimum/maximum values.
Application: Training models by minimizing error (loss), adjusting weights.
Example:
1. Gradient Descent: The process of finding the best model parameters by moving in the direction that reduces error, like rolling a ball downhill.
2. Backpropagation: Calculating how much each weight in a neural network should change to improve predictions.
What is covered: Measuring uncertainty, analyzing data, making predictions.
Application: Predicting outcomes, evaluating models, handling randomness.
Example:
1. Spam Detection: Using probability to decide if an email is spam based on words it contains.
2. Model Evaluation: Calculating accuracy, precision, and recall to see how well a model performs.
What is covered: Logic, graphs, counting, combinations.
Application: Social networks, recommendation systems, logical reasoning.
Example:
1.Friend Recommendations: Using graph theory to suggest new friends on social media.
2.Counting Possibilities: Calculating how many ways a password can be formed.
This section includes a comprehensive evaluation covering all course topics, designed to measure understanding and mastery of key mathematical concepts presented throughout the course
Phase 1: From Coders to Creators
You’ll set up your professional coding environment by installing VS Code and Jupyter, introduce ChatGPT as a coding co-pilot, and learn to build effective prompts to generate code, establishing a productivity mindset for modern development.
Learn to reframe coding as building blocks for real applications by working with CSV, JSON, and image datasets from relatable domains like YouTube, food, and books, developing a system-level thinking approach.
Master abstraction, reusability, and clarity in logic by breaking down real-world use cases like meal planners and birthday reminders into modular code components using functions, loops, and conditions.
Build a functional CLI project such as a task tracker or GPA calculator, solving real-world problems like smart schedulers or basic calculators while developing ownership and confidence in your coding abilities