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.
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What you'll learn
- understand probability and statistical distributions
- estimate parameters and fit models
- perform regression analysis and interpret coefficients
- evaluate model performance
- differentiate between statistics, modeling, and machine learning
- implement predictive algorithms using Scikit-learn
Course objectives
- build robust predictive models
- interpret and analyze data for business applications
- troubleshoot common modeling issues
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