Data Analysis with Python
Learn to clean, explore, and visualise data using pandas, NumPy, Matplotlib, and Seaborn. Transform raw data into actionable insights.
10 Weeks
Duration
Beginner
Level
Live
Sessions
Live Cohort
Data Analysis with Python
₹3,99812% OFF
₹3,500SALE
Course Overview
Everything you will learn in a structured 10-week programme.
What You'll Learn
- Python fundamentals & data structures
- NumPy arrays & vectorized operations
- Pandas DataFrames — cleaning, merging & aggregation
- Matplotlib & Seaborn for data visualization
- Web scraping with BeautifulSoup
- Exploratory Data Analysis (EDA) techniques
What You'll Achieve
- Analyse & visualise real-world datasets
- Build a data analysis portfolio
- Strong Python + data tooling skills
- Foundation for Data Science & analytics roles
Why choose this course?
The practical path to becoming a data analyst.
Continuous Assignments
Doubt clearing sessions
Mock interviews
Real-world projects
Course Curriculum
A weekly roadmap — from Python basics to end-to-end data analysis.
- Jupyter Notebook & Google Colab setup
- Introduction to Python, basic syntax and indentation
- Internal working of Python and Memory Management
- Variables, data types, and operators
- Input/output operations
- Conditional statements (if, elif, else)
- Loops (for, while)
- Break and continue statements
- Nested loops and conditions
- String manipulation and methods
- Lists and Tuples
- Dictionaries, Sets and their operations
- List & Dictionary comprehensions
- Shallow copy and deep copy
- Function definition and calling
- Parameters and return values
- Lambda functions, map, filter, reduce
- Iterators, Generators & Decorators
- Inbuilt and user-defined modules
- File handling operations
- Exception handling techniques
- Regular expressions using regex
- Array creation, operations, indexing & slicing
- NumPy functions, methods & broadcasting
- Logical operations on arrays
- Solving equations using NumPy
- Date, time & basic statistics with NumPy
- Loading & saving data
- Introduction to Pandas — Series & DataFrames
- Indexing, filtering, sorting & extracting data
- Handling CSV and Excel files
- Data merging, joining & transformation
- Handling missing data and outliers
- Groupby and aggregation
- Pandas built-in data visualization
- Data visualization with Matplotlib
- Advanced plotting with Seaborn
- Introduction to requests & BeautifulSoup
- Parsing HTML & extracting data from websites
- Exploratory Data Analysis (EDA) techniques
- Case studies and portfolio projects
- 🚀 Final Project: End-to-end data analysis with visualization