Python Complete Course with Swapnjeet S

Python Complete Course

(With Projects)

Complete Python Couse for Data Analyst & Data Engineers

Created by: Swapnjeet S

Founder – Data Tutorials YouTube Channel (245K+ Subscribers)

Lead Data Analyst | 12+ Years of Industry Experience

Learn Python. Analyze Data. Build Practical Projects.

Want to use Python to clean messy data, uncover insights, and create meaningful charts even if you’re starting from scratch?

The Python Complete Course with Projects takes you step by step from Python fundamentals to practical data analysis using NumPy, Pandas, Matplotlib, and Seaborn.

Across 32 modules and 3 projects, you’ll learn to:

✅ Build your foundation in variables, data types, conditions, loops, and functions.

✅ Work with arrays and perform numerical analysis using NumPy.

✅ Read Excel and CSV files, clean data, filter records, group results, and combine datasets using Pandas.

✅ Create charts, analyze trends, and explore relationships using Matplotlib and Seaborn.

✅ Apply your learning through three practical projects.

Whether you’re a beginner or already work with Excel, SQL, Power BI, Fabric, or Tableau, this course will help you add Python to your data analysis toolkit.

Detailed Syllabus - https://docs.google.com/document/d/1EKoUs5yZuPFR0K8Ppd1s8Pig_pSykZYFcvlikEJQ5Zk/edit?usp=sharing

Module 1: Python Introduction

Module 2: Getting Started

Module 3: Data Types

Module 4: Operators

Module 5: Strings

Module 6: Text Standardization

Module 7: f-Strings

Module 8: Conditional Statements

Module 9: Loops

Module 10: Functions

Module 11: NumPy

Module 12: NumPy Operations

Module 13: NumPy Boolean Indexing and Filtering

Module 14: NumPy Statistical Functions

Module 15: NumPy Searching and Sorting

Module 16: Pandas

Module 17: Pandas Reading CSV and Excel Files

Module 18: Pandas Initial Data Checks

Module 19: Pandas Filtering

Module 20: Pandas Sorting Data

Module 21: Pandas Adding, Renaming and Deleting Columns

Module 22: Pandas Data Cleaning

Module 23: Pandas Statistical Functions

Module 24: Pandas GroupBy

Module 25: Pandas Merge and Joins

Module 26: Matplotlib

Module 27: Matplotlib Line Chart

Module 28: Matplotlib Bar Chart

Module 29: Matplotlib Scatter Plot and Histogram

Module 30: Matplotlib Pie Chart

Module 31: Seaborn

Module 32: Projects

Why This Course?

1. Instructed by Swapnjeet S — Founder of the Data Tutorials YouTube Channel
2. Structured learning across 32 modules, from Python fundamentals to practical data analysis
3. Hands-on learning with datasets, examples, and practical demonstrations
4. Coverage of NumPy, Pandas, Matplotlib, and Seaborn
5. Focus on data cleaning, analysis, and visualization
6. Includes 3 projects to apply your learning
7. Builds practical Python skills for aspiring and working data analysts

How This Course Is Taught?

1. Step-by-step explanations with practical demonstrations
2. Concepts explained using datasets and examples
3. Clear explanations of why, when, and how to use the concepts covered
4. Guided practice in data preparation, cleaning, analysis, and visualization
5. Progressive learning from Python basics to libraries and projects
6. Suitable for self-paced learning

What You Will Learn?

1. Understand Python, its architecture, and its role in data analysis
2. Install Anaconda and work with Jupyter Notebook and notebooks in VS Code
3. Use comments, Markdown, variables, and data types
4. Work with arithmetic, assignment, comparison, logical, membership, bitwise, and identity operators
5. Manipulate strings, standardize text, and format text and numbers using f-strings
6. Write conditional statements using if, else, elif, and nested if
7. Use for and while loops through practical examples
8. Create functions and use the return statement
9. Work with NumPy arrays, slicing, two-dimensional arrays, and array operations
10. Apply NumPy Boolean indexing, filtering, statistical functions, searching, and sorting
11. Create Pandas Series and DataFrames
12. Read CSV and Excel files, handle delimiters, and work with multiple sheets
13. Perform initial data checks, filter records, and sort data
14. Add calculated and conditional columns, rename columns, and delete columns
15. Clean data by handling null values, removing duplicates, and standardizing fields
16. Apply statistical functions and summarize data using GroupBy
17. Combine datasets using merges and joins
18. Create and format line charts, bar charts, scatter plots, histograms, pie charts, and donut charts using Matplotlib
19. Create Seaborn visualizations using themes, color palettes, grouped bar charts, count plots, histograms, KDE plots, and line plots
20. Analyze daily and monthly sales trends and add average lines
21. Explore correlations and create correlation, triangular, and pivot-table heatmaps
22. Apply your learning through 3 projects

Who This Course Is For?

1. Beginners who want to learn Python from scratch
2. Students and aspiring data analysts
3. Excel users who want to expand their data analysis skills
4. Power BI and Tableau developers looking to add Python to their toolkit
5. SQL professionals who want to analyze and visualize data using Python
6. Working data analysts looking to strengthen their data cleaning and analysis skills
7. Anyone who wants hands-on experience with Python, NumPy, Pandas, Matplotlib, and Seaborn

📞 For Support or Queries

📧 Email: [email protected]

📱 WhatsApp: +91 9579005495