-
Complete introduction to Python programming for data analysis
-
Hands-on exercises in data cleaning, analysis, and visualization
-
Practical skills with Numpy, Pandas, Matplotlib, and Seaborn
This comprehensive course is designed to take you from a Python beginner to a proficient data analyst. You’ll start with the fundamentals, including variables, data types, operators, and control flow, building a solid foundation for Python programming. As you progress, you'll dive into Python libraries essential for data analysis, such as Numpy for numerical computations and Pandas for data manipulation. You'll also explore Matplotlib and Seaborn, powerful visualization tools that help transform raw data into insightful graphs and charts.
The course structure incorporates a variety of hands-on exercises, ensuring you can apply what you've learned to real-world problems. You'll learn to clean and analyze datasets, create customized visualizations, and handle larger, more complex data structures. Advanced Pandas techniques such as pivot tables, data merging, and exporting data will be covered to ensure you can manage data efficiently in any scenario.
In the final section, you’ll work on a real-world project where you'll apply all the concepts learned throughout the course. You’ll use GitHub for version control, analyze skill trends, salary data, and more, building a portfolio-worthy project. By the end of this course, you’ll have the skills to confidently take on data analysis tasks using Python.
The course is perfect for aspiring data analysts, beginners in programming, and anyone looking to switch to a data-driven career. No prior programming experience is needed. You'll learn through clear, step-by-step guidance, hands-on exercises, and a real-world project that reinforces core concepts and prepares you for practical data analysis tasks.
-
Master Python basics for data analysis
-
Clean and analyze datasets using Pandas
-
Create compelling visualizations with Matplotlib and Seaborn
-
Handle large datasets using advanced Pandas techniques
-
Set up and use GitHub for version control
-
Apply Python in real-world data analysis projects
United States
Great Britain
India
Germany
France
Canada
Russia
Spain
Brazil
Australia
Singapore
Canary Islands
Hungary
Ukraine
Luxembourg
Estonia
Lithuania
South Korea
Turkey
Switzerland
Colombia
Taiwan
Chile
Norway
Ecuador
Indonesia
New Zealand
Cyprus
Denmark
Finland
Poland
Malta
Czechia
Austria
Sweden
Italy
Egypt
Belgium
Portugal
Slovenia
Ireland
Romania
Greece
Argentina
Netherlands
Bulgaria
Latvia
South Africa
Malaysia
Japan
Slovakia
Philippines
Mexico
Thailand
