Adrenaline-Education

Course 2 of 4 in this specialization

Analyze and Visualize Data with Python

Coming soon 29 topics Self-paced · Online
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Where this comes from

Built from the Wiley title of the same name — professionally edited, technically reviewed, and already relied on by people doing this work.

Source ISBN 9781119547662

How it was built

Sejal Learning Systems rebuilt the book as a course: objectives first, then short video, worked examples, and practice with real feedback. It runs on Coursera at your own pace, with a certificate on completion.

Analyze and Visualize Data with Python is a self-paced online course built from the Wiley title of the same name.

What this course covers

  • Introduction
  • Part 1: Getting Started with Data Science and Python
  • Chapter 1: Discovering the Match between Data Science and Python
  • Chapter 2: Introducing Python’s Capabilities and Wonders
  • Chapter 3: Setting Up Python for Data Science
  • Chapter 4: Working with Google Colab
  • Part 2: Getting Your Hands Dirty with Data
  • Chapter 5: Understanding the Tools
  • Chapter 6: Working with Real Data
  • Chapter 7: Conditioning Your Data
  • Chapter 8: Shaping Data
  • Chapter 9: Putting What You Know in Action
  • Part 3: Visualizing Information
  • Chapter 10: Getting a Crash Course in MatPlotLib
  • Chapter 11: Visualizing the Data
  • Part 4: Wrangling Data
  • Chapter 12: Stretching Python’s Capabilities
  • Chapter 13: Exploring Data Analysis
  • Chapter 14: Reducing Dimensionality
  • Chapter 15: Clustering
  • Chapter 16: Detecting Outliers in Data
  • Part 5: Learning from Data
  • Chapter 17: Exploring Four Simple and Effective Algorithms
  • Chapter 18: Performing Cross-Validation, Selection, and Optimization
  • Chapter 19: Increasing Complexity with Linear and Nonlinear Tricks
  • Chapter 20: Understanding the Power of the Many
  • Part 6: The Part of Tens
  • Chapter 21: Ten Essential Data Resources
  • Chapter 22: Ten Data Challenges You Should Take