Master essential concepts, theory, and hands-on techniques to become an effective data scientist. Guided by real-world case studies and applied Python programming, you'll learn to acquire, analyze, and model complex datasets, drawing actionable insights using industry-standard tools like pandas, NumPy, SciPy, and scikit-learn. Confidently tackle data problems, apply machine learning algorithms, and communicate your findings through compelling visualizations—equipping you with the foundational skills needed for impactful data-driven decision making in any field.
Data Science Fundamentals, Part 2 Specialization
Discover new skills with 30% off courses from industry experts. Save now.
Data Science Fundamentals, Part 2 Specialization
Applied Data Science with Python. Analyze realworld datasets, building applications, & applying machine learning with Python’s PyData
Instructor: Pearson
Included with 
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 5 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 5 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Acquire, clean, and manipulate real-world data using Python libraries, APIs, and databases, and perform exploratory data analysis and visualization.
Build, evaluate, and interpret statistical and machine learning models to make predictions and draw inferences from complex datasets.
Apply best practices in hypothesis testing, A/B testing, and model validation to solve practical problems and communicate results effectively.
Overview
Skills you'll gain
- Exploratory Data Analysis
- Descriptive Statistics
- Matplotlib
- Machine Learning
- Regression Analysis
- Predictive Modeling
- Statistical Methods
- Statistical Inference
- Data Visualization
- Sampling (Statistics)
- Box Plots
- Data Analysis
- Data Science
- Statistical Modeling
- A/B Testing
- Probability & Statistics
- Statistical Hypothesis Testing
Tools you'll learn
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
August 2025
4 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Pearson
Specialization - 3 course series
What you'll learn
Gain a foundational understanding of Exploratory Data Analysis (EDA) and its historical context.
Develop practical skills in Python data visualization using matplotlib and seaborn.
Learn to identify and interpret relationships and correlations within datasets using advanced charting techniques.
Recognize and avoid common pitfalls in data analysis, including mixed effects and Simpson’s Paradox.
Skills you'll gain
Category: Scatter Plots
Category: Seaborn
Category: Exploratory Data Analysis
Category: Matplotlib
Category: Data Visualization Software
Category: Box Plots
Category: Histogram
Category: Statistical Methods
Category: Correlation Analysis
Category: Statistical Analysis
Category: Descriptive Statistics
Category: Data Analysis
Category: Data Visualization
What you'll learn
Master foundational and modern techniques for statistical inference and data analysis.
Apply computational and sampling-based approaches to real-world data problems.
Conduct hypothesis tests and optimize processes using A/B testing methodologies.
Distinguish between correlation and causation to ensure robust, actionable insights.
Skills you'll gain
Category: Correlation Analysis
Category: Estimation
Category: A/B Testing
Category: Statistical Inference
Category: Analytics
Category: Statistical Analysis
Category: Probability & Statistics
Category: Data-Driven Decision-Making
Category: Statistical Methods
Category: Sampling (Statistics)
Category: Data Analysis
Category: Statistical Hypothesis Testing
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
Category: Machine Learning
Category: Probability & Statistics
Category: Regression Analysis
Category: Scikit Learn (Machine Learning Library)
Category: Statistical Modeling
Category: Predictive Modeling
Category: Business Analytics
Category: Statistical Inference
Category: Statistical Analysis
Category: Data Analysis
Category: Performance Metric
Category: Estimation
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
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
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
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.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
More questions
Financial aid available,