University of Michigan
Applied Data Science with Python Specialization
University of Michigan

Applied Data Science with Python Specialization

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

Christopher Brooks
Kevyn Collins-Thompson
Daniel Romero

Instructors: Christopher Brooks

Sponsored by University of Michigan

426,236 already enrolled

Get in-depth knowledge of a subject
4.5

(26,100 reviews)

Intermediate level
Some related experience required
4 months
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
4.5

(26,100 reviews)

Intermediate level
Some related experience required
4 months
at 10 hours a week
Flexible schedule
Learn at your own pace

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

Skills you'll gain

Details to know

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Taught in English

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Specialization - 5 course series

Introduction to Data Science in Python

Course 134 hours4.5 (27,081 ratings)

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

Category: Computer Programming
Category: Data Analysis
Category: Data Management
Category: Python Programming
Category: Statistical Programming
Category: Computer Programming Tools
Category: Critical Thinking
Category: General Statistics

Applied Plotting, Charting & Data Representation in Python

Course 224 hours4.5 (6,246 ratings)

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

Applied Machine Learning in Python

Course 331 hours4.6 (8,515 ratings)

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

Category: Algorithms
Category: Machine Learning
Category: Machine Learning Algorithms
Category: Python Programming
Category: Applied Machine Learning
Category: Data Analysis
Category: Regression
Category: Human Learning
Category: Statistical Programming
Category: Computer Programming

Applied Text Mining in Python

Course 425 hours4.2 (3,809 ratings)

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

Category: Python Programming
Category: Machine Learning
Category: Data Analysis
Category: Applied Machine Learning
Category: Machine Learning Algorithms
Category: Algorithms
Category: Human Learning

Applied Social Network Analysis in Python

Course 526 hours4.6 (2,703 ratings)

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

Category: Graph Theory
Category: Python Programming
Category: Computer Programming
Category: Data Analysis
Category: Network Model
Category: Computer Networking
Category: Network Architecture

Instructors

Christopher Brooks
15 Courses891,034 learners
Kevyn Collins-Thompson
University of Michigan
0 Courses0 learners

Offered by

University of Michigan

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