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.
Data Science Fundamentals Part 2: Unit 3
This course is part of Data Science Fundamentals, Part 2 Specialization
Instructor: Pearson
Access provided by Thammasat University
Recommended experience
What you'll 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.
Skills you'll gain
- Business Analytics
- Machine Learning
- Statistical Analysis
- Predictive Modeling
- Probability & Statistics
- Predictive Analytics
- Data Analysis
- Scikit Learn (Machine Learning Library)
- Probability Distribution
- Statistical Inference
- Supervised Learning
- Statistical Modeling
- Regression Analysis
- Estimation
- Performance Metric
Details to know
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2 assignments
August 2025
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There is 1 module in this course
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.
What's included
24 videos2 assignments
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