This course centers on building practical data science skills with Python, starting from the basics and steadily moving into real analytical workflows. You'll see how Python is used to work with data, make predictions, and solve common analysis problems, while getting comfortable with key libraries like Pandas, NumPy, and Matplotlib. Everything stays hands-on, drawing on real examples and projects, so you're not just reading about concepts, you're actively putting them to use as you go.
You'll start with core Python fundamentals such as variables, data types, lists, functions, and control flow, then move into working with real datasets. As you progress, you'll clean and organize data, run analysis with Pandas and NumPy, and uncover patterns through charts and visualizations. You'll also get an introduction to machine learning concepts and where they fit into Python-based analysis. By the end, you'll have a solid grasp of how Python powers data science and the confidence to work with data in practical, real-world situations.
What You Will Learn
- Foundational programming concepts, including loops, functions, and objects
- Work with different types of data such as integers, floats, and strings
- Direct the flow of your programs using conditional statements
- Reuse and streamline code through object-oriented programming
- Analyze tabular data with NumPy and pandas
- Build graphs and visualizations with Matplotlib
- Make predictions with linear regression using scikit-learn
This course is available for 30 days. You can choose when to start your access period. Once you activate, you will have 30 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.
