Data science combines statistics, programming, and domain knowledge to extract insights from data. This course teaches the Python data stack — NumPy, Pandas, Matplotlib — alongside the statistical thinking that turns numbers into decisions.
Each lesson uses realistic datasets and shows the actual workflow: import, clean, explore, visualize, conclude. By the end you can take a CSV file and produce useful analysis without copying code blindly.
What you’ll learn
- NumPy for fast numerical arrays and operations
- Pandas for tabular data: DataFrames, filtering, joining, grouping
- Visualization with Matplotlib and Seaborn
- Statistical foundations: descriptive stats, distributions, hypothesis tests
- The full data analysis workflow from import to insight
Who is this for
Designed for analysts ready to outgrow spreadsheets, scientists who need to process larger datasets, and developers who want a data analysis skillset. Students preparing for data engineering or ML roles will find this a strong foundation.
Prerequisites
- Basic Python knowledge (variables, functions, for loops)
- High-school level statistics is helpful
- A computer that can run Python with the standard data libraries
Course outcomes
You’ll be able to clean and analyze real-world datasets, communicate findings with effective visualizations, and apply basic statistical inference responsibly. This course is also a strong preparation for entering machine learning or building data pipelines professionally.