Data Science Fundamentals Part 2: Unit 3

Overview

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.

Syllabus

Data Science Fundamentals Part 2: Unit 3

This module introduces the fundamentals of statistical modeling and machine learning using Python. You’ll learn to analyze Airbnb listing data, starting with probability and statistical distributions, then progress to parameter estimation and regression analysis. The module covers building and evaluating predictive models, understanding model performance, and overcoming common challenges. You’ll also explore the distinctions between statistics, modeling, and machine learning, and gain hands-on experience with Scikit-learn to make predictions. By the end, you’ll know how to create, interpret, and assess statistical models for real-world data analysis and prediction tasks.