Learning Path: Python for Data Analytics

Video description

According to the latest O’Reilly Data Science Salary Survey, Python is one of the tools that contribute most to a data scientist's salary. If you want to take your Python skills to the next level and perform data analysis, this practical, hands-on learning path will show you how to do vital tasks such as: choosing the correct analytic model for your analytics job; parsing, cleaning and analyzing data using the Python Pandas library; and basic techniques to visualize and present complex data with confidence.

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Table of contents

  1. Introduction to Data Exploration
    1. Opportunities and Goals
    2. The State of Data
    3. Data Optimism
  2. Getting Started
    1. Software Setup, IPython, and Import and Validation
    2. Data Organization
  3. Visualizing Distributions
    1. PMFs and CDFs
  4. Relationships Between Variables
    1. Scatterplots
    2. Correlation and Least Squares
  5. Statistical Inference
    1. Introduction to Statistical Inference
    2. Effect Size
    3. Effect Size, Difference in Proportions
    4. Quantifying Precision
    5. Hypothesis Testing
  6. Regression
    1. Linear Regression
    2. Logistic Regression
  7. Modeling Distributions
    1. Modeling Distributions
  8. Survival Analysis
    1. Survival Analysis
  9. Inspection Paradox
    1. Inspection Paradox
  10. Introduction
    1. About The Course And What To Expect
    2. About The Author
  11. The Basics Of Data Visualization
    1. Storytelling - What Story Do You Want To Tell?
    2. Types Of Charts - Their Purposes And How To Choose The Right One
    3. Choosing The Right Colors
    4. Common Pitfalls In Data Visualization
    5. Good Practices In Data Visualization
    6. Reproducibility In Data Visualization
    7. Data Sources
  12. Data Vis In Python - matplotlib
    1. The Programmatic Visualization Framework
    2. Using matplotlib In The Jupyter Notebook
    3. matplotlib Styles
    4. Making Basics Plots - Lines, Bars, Pies And Scatterplots
    5. Plotting Distributions - Histograms And Box Plots
    6. Subplots And Small Multiples
  13. Conclusion
    1. Wrap Up
  14. Introduction
    1. Welcome To The Course
    2. About The Author
    3. Local Setup, What We'll Be Using
  15. Getting The Data
    1. Basic Files
    2. Excel Files
    3. PDF Files
    4. Using PDF Tables
    5. Streaming And Rest APIs: Twitter
    6. Using APIs Without Libraries
    7. Introduction To Web Scraping
    8. Building Your Own Web Scraper
    9. Python 2 vs Python 3 Encoding
    10. A Word On Encoding
  16. Data Analysis With Pandas
    1. Pandas Data Structures
    2. Pandas Data Types
    3. Filtering With Pandas
    4. Combining Datasets
    5. Joining Datasets
    6. Split-Apply-Combine
    7. Simple Statistics With Pandas
    8. Standardizing Your Data
    9. Normalizing Your Data
  17. Cleaning Your Data
    1. Identifying "Bad" Data
    2. Simple String Parsing With Regex
    3. Fuzzy Matching
    4. Storing Your Data (Local And Cloud)
  18. Pandas. More Advanced Functionality
    1. Identifying Trends
    2. Identifying Outliers
    3. Monitoring Speed/Performance
    4. Parallelizing
  19. Other Advanced Data Libraries
    1. Natural Language Processing
    2. Introduction To Numpy And Scipy
    3. Visualization With Matplotlib And Bokeh
  20. Conclusion
    1. Where To Go Next

Product information

  • Title: Learning Path: Python for Data Analytics
  • Author(s): O'Reilly Media, Inc.
  • Release date: November 2016
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781491977552