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.
Publisher resources
Table of contents
-
Introduction to Data Exploration
- Opportunities and Goals 00:04:34
- The State of Data 00:03:04
- Data Optimism 00:02:52
-
Getting Started
- Software Setup, IPython, and Import and Validation 00:11:54
- Data Organization 00:04:45
-
Visualizing Distributions
- PMFs and CDFs 00:15:13
-
Relationships Between Variables
- Scatterplots 00:13:53
- Correlation and Least Squares 00:11:48
-
Statistical Inference
- Introduction to Statistical Inference 00:05:44
- Effect Size 00:13:00
- Effect Size, Difference in Proportions 00:06:18
- Quantifying Precision 00:20:46
- Hypothesis Testing 00:16:35
-
Regression
- Linear Regression 00:20:33
- Logistic Regression 00:11:48
-
Modeling Distributions
- Modeling Distributions 00:14:16
-
Survival Analysis
- Survival Analysis 00:17:03
-
Inspection Paradox
- Inspection Paradox 00:16:04
-
Introduction
- About The Course And What To Expect 00:01:01
- About The Author 00:01:04
-
The Basics Of Data Visualization
- Storytelling - What Story Do You Want To Tell? 00:03:18
- Types Of Charts - Their Purposes And How To Choose The Right One 00:06:22
- Choosing The Right Colors 00:04:00
- Common Pitfalls In Data Visualization 00:04:01
- Good Practices In Data Visualization 00:01:59
- Reproducibility In Data Visualization 00:02:41
- Data Sources 00:02:28
-
Data Vis In Python - matplotlib
- The Programmatic Visualization Framework 00:03:30
- Using matplotlib In The Jupyter Notebook 00:02:32
- matplotlib Styles 00:03:49
- Making Basics Plots - Lines, Bars, Pies And Scatterplots 00:12:14
- Plotting Distributions - Histograms And Box Plots 00:04:35
- Subplots And Small Multiples 00:03:59
-
Conclusion
- Wrap Up 00:01:36
-
Introduction
- Welcome To The Course 00:02:13
- About The Author 00:01:06
- Local Setup, What We'll Be Using 00:03:27
-
Getting The Data
- Basic Files 00:04:56
- Excel Files 00:05:47
- PDF Files 00:04:00
- Using PDF Tables 00:06:17
- Streaming And Rest APIs: Twitter 00:10:21
- Using APIs Without Libraries 00:04:41
- Introduction To Web Scraping 00:03:36
- Building Your Own Web Scraper 00:06:44
- Python 2 vs Python 3 Encoding 00:06:17
- A Word On Encoding 00:06:33
-
Data Analysis With Pandas
- Pandas Data Structures 00:08:06
- Pandas Data Types 00:04:23
- Filtering With Pandas 00:08:31
- Combining Datasets 00:06:25
- Joining Datasets 00:08:23
- Split-Apply-Combine 00:06:53
- Simple Statistics With Pandas 00:07:05
- Standardizing Your Data 00:06:58
- Normalizing Your Data 00:04:12
-
Cleaning Your Data
- Identifying "Bad" Data 00:08:17
- Simple String Parsing With Regex 00:08:46
- Fuzzy Matching 00:04:55
- Storing Your Data (Local And Cloud) 00:06:51
-
Pandas. More Advanced Functionality
- Identifying Trends 00:04:54
- Identifying Outliers 00:05:34
- Monitoring Speed/Performance 00:06:05
- Parallelizing 00:05:39
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Other Advanced Data Libraries
- Natural Language Processing 00:05:02
- Introduction To Numpy And Scipy 00:04:35
- Visualization With Matplotlib And Bokeh 00:05:16
-
Conclusion
- Where To Go Next 00:03:28
Product information
- Title: Learning Path: Python for Data Analytics
- Author(s):
- Release date: November 2016
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491977552
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