University of Michigan
Applied Data Science with Python Specialization
442,053 enrolled
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.

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Get in-depth knowledge of a subject

(26,238 reviews)

Intermediate level
Some related experience required
Flexible schedule
4 months at 10 hours a week
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject

(26,238 reviews)

Intermediate level
Some related experience required
Flexible schedule
4 months at 10 hours a week
Earn a career credential
Share your expertise with employers

Overview

  • 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

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

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: Pandas (Python Package)
Category: Data Manipulation
Category: Python Programming
Category: Probability & Statistics
Category: Sampling (Statistics)
Category: Data Science
Category: NumPy
Category: Data Cleansing
Category: Programming Principles
Category: Statistical Hypothesis Testing
Category: Unstructured Data
Category: Statistical Methods
Category: Data Import/Export
Category: Data Analysis
Category: Statistical Analysis

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: Data Visualization Software
Category: Data Manipulation
Category: Interactive Data Visualization
Category: Visualization (Computer Graphics)
Category: Plot (Graphics)
Category: Data Visualization
Category: Matplotlib
Category: Scatter Plots
Category: Graphic and Visual Design
Category: Data Presentation
Category: Python Programming
Category: Graphing

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: Supervised Learning
Category: Regression Analysis
Category: Applied Machine Learning
Category: Machine Learning
Category: Decision Tree Learning
Category: Artificial Neural Networks
Category: Scikit Learn (Machine Learning Library)
Category: Machine Learning Algorithms
Category: Classification And Regression Tree (CART)
Category: Feature Engineering
Category: Statistical Modeling
Category: Python Programming
Category: Predictive Modeling

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: Text Mining
Category: Supervised Learning
Category: Natural Language Processing
Category: Unsupervised Learning
Category: Applied Machine Learning
Category: Data Science
Category: Unstructured Data
Category: Feature Engineering
Category: Data Processing
Category: Data Cleansing
Category: Python Programming
Category: Data Manipulation

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: Network Analysis
Category: Network Model
Category: Graph Theory
Category: Python Programming
Category: Simulations
Category: Pandas (Python Package)
Category: Algorithms
Category: Matplotlib
Category: General Networking
Category: Predictive Modeling

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Instructors

Christopher Brooks
15 Courses924,794 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

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