Can I take the course for free?

No, you cannot take this course for free. 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. If you cannot afford the fee, you can apply for financial aid.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

Applied Data Science with Python Specialization

Applied Data Science with Python Specialization

Gain new insights into your data. Learn to apply data science methods and techniques, and acquire analysis skills.

Instructors: Christopher Brooks

Included with Coursera Plus

Learn more

5 course series

Get in-depth knowledge of a subject

4.5

(26,265 reviews)

Intermediate level

Some related experience required

4 months at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

5 course series

Get in-depth knowledge of a subject

4.5

(26,265 reviews)

Intermediate level

Some related experience required

4 months at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

Conduct an inferential statistical analysis
Discern whether a data visualization is good or bad
Enhance a data analysis with applied machine learning
Analyze the connectivity of a social network

Overview

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.

Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

20 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 University of Michigan

Specialization - 5 course series

Introduction to Data Science in Python

Course 134 hours4.5 (27,207 ratings)View course

What you'll learn

Understand techniques such as lambdas and manipulating csv files
Describe common Python functionality and features used for data science
Query DataFrame structures for cleaning and processing
Explain distributions, sampling, and t-tests

Skills you'll gain

Pandas (Python Package), Python Programming, Data Manipulation, NumPy, Probability & Statistics, Data Science, Jupyter, Data Cleansing, Programming Principles, Statistical Analysis, Data Analysis, Pivot Tables And Charts, and Data Import/Export

Applied Plotting, Charting & Data Representation in Python

Course 224 hours4.5 (6,278 ratings)View course

What you'll learn

Describe what makes a good or bad visualization
Understand best practices for creating basic charts
Identify the functions that are best for particular problems
Create a visualization using matplotlb

Skills you'll gain

Histogram, Python Programming, Data Manipulation, Visualization (Computer Graphics), NumPy, Data Visualization Software, Interactive Data Visualization, Scientific Visualization, Scatter Plots, Statistical Visualization, Data Visualization, Graphing, Matplotlib, Pandas (Python Package), and Data Presentation

Applied Machine Learning in Python

Course 331 hours4.6 (8,587 ratings)View course

What you'll learn

Describe how machine learning is different than descriptive statistics
Create and evaluate data clusters
Explain different approaches for creating predictive models
Build features that meet analysis needs

Skills you'll gain

Supervised Learning, Regression Analysis, Decision Tree Learning, Applied Machine Learning, Machine Learning, Scikit Learn (Machine Learning Library), Unsupervised Learning, Random Forest Algorithm, Predictive Modeling, Python Programming, Dimensionality Reduction, and Feature Engineering

Applied Text Mining in Python

Course 425 hours4.2 (3,822 ratings)View course

What you'll learn

Understand how text is handled in Python
Apply basic natural language processing methods
Write code that groups documents by topic
Describe the nltk framework for manipulating text

Skills you'll gain

Text Mining, Supervised Learning, Natural Language Processing, Data Manipulation, Feature Engineering, Data Processing, Machine Learning Algorithms, Data Cleansing, Python Programming, Unstructured Data, and Unsupervised Learning

Applied Social Network Analysis in Python

Course 526 hours4.6 (2,715 ratings)View course

What you'll learn

Represent and manipulate networked data using the NetworkX library
Analyze the connectivity of a network
Measure the importance or centrality of a node in a network
Predict the evolution of networks over time

Skills you'll gain

Social Network Analysis, Network Analysis, Pandas (Python Package), Algorithms, Unsupervised Learning, Data Import/Export, Matplotlib, Data Analysis, Predictive Analytics, Graph Theory, Python Programming, and Analysis

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Christopher Brooks

15 Courses933,562 learners

Kevyn Collins-Thompson

University of Michigan

0 Courses0 learners

Daniel Romero

University of Michigan

1 Course4 learners

V. G. Vinod Vydiswaran

University of Michigan

0 Courses0 learners

Offered by

University of Michigan

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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.

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.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Applied Data Science with Python Specialization

What you'll learn

Overview

Skills you'll gain

Tools you'll learn

What’s included

Advance your subject-matter expertise