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Learning Path: Python for Data Analytics

Learning Path: Python for Data Analytics

by O'Reilly Media, Inc.
Publisher: O'Reilly Media, Inc.
Release Date: November 2016
ISBN: 9781491977569
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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.

Table of Contents

  1. Introduction to Data Exploration
    1. Opportunities and Goals 00:04:34
    2. The State of Data 00:03:04
    3. Data Optimism 00:02:52
  2. Getting Started
    1. Software Setup, IPython, and Import and Validation 00:11:54
    2. Data Organization 00:04:45
  3. Visualizing Distributions
    1. PMFs and CDFs 00:15:13
  4. Relationships Between Variables
    1. Scatterplots 00:13:53
    2. Correlation and Least Squares 00:11:48
  5. Statistical Inference
    1. Introduction to Statistical Inference 00:05:44
    2. Effect Size 00:13:00
    3. Effect Size, Difference in Proportions 00:06:18
    4. Quantifying Precision 00:20:46
    5. Hypothesis Testing 00:16:35
  6. Regression
    1. Linear Regression 00:20:33
    2. Logistic Regression 00:11:48
  7. Modeling Distributions
    1. Modeling Distributions 00:14:16
  8. Survival Analysis
    1. Survival Analysis 00:17:03
  9. Inspection Paradox
    1. Inspection Paradox 00:16:04
  10. Introduction
    1. About The Course And What To Expect 00:01:01
    2. About The Author 00:01:04
  11. The Basics Of Data Visualization
    1. Storytelling - What Story Do You Want To Tell? 00:03:18
    2. Types Of Charts - Their Purposes And How To Choose The Right One 00:06:22
    3. Choosing The Right Colors 00:04:00
    4. Common Pitfalls In Data Visualization 00:04:01
    5. Good Practices In Data Visualization 00:01:59
    6. Reproducibility In Data Visualization 00:02:41
    7. Data Sources 00:02:28
  12. Data Vis In Python - matplotlib
    1. The Programmatic Visualization Framework 00:03:30
    2. Using matplotlib In The Jupyter Notebook 00:02:32
    3. matplotlib Styles 00:03:49
    4. Making Basics Plots - Lines, Bars, Pies And Scatterplots 00:12:14
    5. Plotting Distributions - Histograms And Box Plots 00:04:35
    6. Subplots And Small Multiples 00:03:59
  13. Conclusion
    1. Wrap Up 00:01:36
  14. Introduction
    1. Welcome To The Course 00:02:13
    2. About The Author 00:01:06
    3. Local Setup, What We'll Be Using 00:03:27
  15. Getting The Data
    1. Basic Files 00:04:56
    2. Excel Files 00:05:47
    3. PDF Files 00:04:00
    4. Using PDF Tables 00:06:17
    5. Streaming And Rest APIs: Twitter 00:10:21
    6. Using APIs Without Libraries 00:04:41
    7. Introduction To Web Scraping 00:03:36
    8. Building Your Own Web Scraper 00:06:44
    9. Python 2 vs Python 3 Encoding 00:06:17
    10. A Word On Encoding 00:06:33
  16. Data Analysis With Pandas
    1. Pandas Data Structures 00:08:06
    2. Pandas Data Types 00:04:23
    3. Filtering With Pandas 00:08:31
    4. Combining Datasets 00:06:25
    5. Joining Datasets 00:08:23
    6. Split-Apply-Combine 00:06:53
    7. Simple Statistics With Pandas 00:07:05
    8. Standardizing Your Data 00:06:58
    9. Normalizing Your Data 00:04:12
  17. Cleaning Your Data
    1. Identifying "Bad" Data 00:08:17
    2. Simple String Parsing With Regex 00:08:46
    3. Fuzzy Matching 00:04:55
    4. Storing Your Data (Local And Cloud) 00:06:51
  18. Pandas. More Advanced Functionality
    1. Identifying Trends 00:04:54
    2. Identifying Outliers 00:05:34
    3. Monitoring Speed/Performance 00:06:05
    4. Parallelizing 00:05:39
  19. Other Advanced Data Libraries
    1. Natural Language Processing 00:05:02
    2. Introduction To Numpy And Scipy 00:04:35
    3. Visualization With Matplotlib And Bokeh 00:05:16
  20. Conclusion
    1. Where To Go Next 00:03:28