Statistics for Data Science Using Python This fully practical course is designed to teach you how to apply core statistical techniques in Python using real datasets. You'll gain hands-on experience with descriptive statistics, probability distributions, and hypothesis testing—all through coding. Ideal for beginners and career switchers, this course emphasizes applied learning, helping you build a strong foundation for data analysis, machine learning, and real-world decision-making MODULES Below are the modules covered in this course. Module 1: Getting Started with Statistical Thinking in Python Module 2: Descriptive Statistics in Python Module 3: Probability and Distributions Using Python Module 4: Inferential Statistics with Python Module 5: Capstone – Applying Statistics to a Real-World Dataset LEARNING OUTCOMES. By the end of this course, learners will: 1. Use Python (NumPy, pandas, SciPy, seaborn) for statistical computation 2. Analyze real-world datasets using descriptive and inferential methods 3. Apply common probability distributions to practical problems 4. Conduct hypothesis tests using code and interpret the results 5. Visualise and interpret data effectively for data-driven decisions TARGET AUDIENCE This course is ideal for: 1. Aspiring Data Scientists and ML Practitioners 2. Python Developers entering the analytics space 3. Students in Bootcamps or STEM programs 4. Professionals in business, finance, healthcare, and public policy 5. Anyone looking to apply statistics practically using Python PREREQUISITE :Nil COURSE DURATION:2-3 Hours ASSESSMENT TYPE: Quizzes, Assignments and Projects TAKE HOME MATERIALS: Cheat Sheet + Further Reading Resources IS A CERTIFICATE OF COMPLETION PROVIDED AFTER THE COURSE? Yes.