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.

Taught in English

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Christopher Brooks
Kevyn Collins-Thompson
Daniel Romero

Instructors: Christopher Brooks

408,747 already enrolled

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Specialization - 5 course series

Get in-depth knowledge of a subject

4.5

(25,983 reviews)

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

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

Details to know

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Specialization - 5 course series

Get in-depth knowledge of a subject

4.5

(25,983 reviews)

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

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  • Earn a career certificate from University of Michigan
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Specialization - 5 course series

Introduction to Data Science in Python

Course 134 hours4.5 (26,949 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: Natural Language Toolkit (NLTK)
Category: Text Mining
Category: Python Programming
Category: Natural Language Processing

Applied Plotting, Charting & Data Representation in Python

Course 224 hours4.5 (6,228 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

Skills you'll gain

Category: Graph Theory
Category: Network Analysis
Category: Python Programming
Category: Social Network Analysis

Applied Machine Learning in Python

Course 331 hours4.6 (8,474 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: Python Programming
Category: Machine Learning (ML) Algorithms
Category: Machine Learning
Category: Scikit-Learn

Applied Text Mining in Python

Course 425 hours4.2 (3,792 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: Numpy
Category: Pandas
Category: Data Cleansing

Applied Social Network Analysis in Python

Course 526 hours4.6 (2,689 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: Python Programming
Category: Data Virtualization
Category: Data Visualization
Category: Matplotlib

Instructors

Christopher Brooks
15 Courses860,043 learners
Kevyn Collins-Thompson
University of Michigan
0 Courses0 learners

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