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.

Discover new skills with $120 off courses from industry experts. Save now.


Data Science Fundamentals Part 2: Unit 3
This course is part of Data Science Fundamentals, Part 2 Specialization

Instructor: Pearson
Included with
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
Details to know

Add to your LinkedIn profile
August 2025
2 assignments
See how employees at top companies are mastering in-demand skills

Build your subject-matter expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate

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
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Explore more from Data Analysis
- Status: Free Trial
University of California, Irvine
- Status: Preview
The University of Chicago
- Status: Free Trial
Edureka
- Status: Free Trial
Alberta Machine Intelligence Institute
Why people choose Coursera for their career





Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
More questions
Financial aid available,