Data Science Fundamentals Part 2: Unit 3

About this course

This course takes a step-by-step approach to the process of building robust models to predict real-world outcomes and uncover valuable insights from your data. You’ll start with a solid foundation in probability and statistical distributions, learning how to estimate parameters and fit models using industry-standard libraries such as SciPy and NumPy. You'll dive into the theory and practice of regression analysis, learning about modeling correlations and interpreting coefficients for actionable business intelligence. Beyond model building, you’ll gain critical skills in evaluating model performance, troubleshooting common pitfalls, and understanding the nuanced differences between statistics, modeling, and machine learning. By the end of the course, you’ll confidently leverage Scikit-learn to implement predictive algorithms, distinguish between inference and prediction, and apply your knowledge to solve complex, real-world problems.

What you will learn

• Build and evaluate statistical models to predict outcomes using Python libraries such as SciPy, NumPy, and Scikit-learn.
• Understand and apply the fundamentals of probability, statistical distributions, and regression analysis.
• Identify and overcome common challenges in model fitting and performance evaluation.
• Distinguish between statistical inference and prediction, and leverage machine learning algorithms for real-world applications.