English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 6h 02m | 710 MB
Python for Data Science Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts—for even more hands-on experience with Python. In this course, instructor Lillian Pierson takes you step by step through a practical data science project: a web scraper that downloads and analyzes data from the web. Along the way, she introduces techniques to clean, reformat, transform, and describe raw data; generate visualizations; remove outliers; perform simple data analysis; and generate interactive graphs using the Plotly library. You should walk away from this training with basic coding experience that you can take to your organization and quickly apply to your own custom data science projects.
Topics include:
- Why use Python for working with data
- Filtering and selecting data
- Concatenating and transforming data
- Data visualization best practices
- Visualizing data
- Creating a plot
- Creating statistical data graphics
- Performing basic math and linear algebra
- Correlation analysis
- Multivariate analysis
- Data sourcing via web scraping
- Introduction to natural language processing
- Collaborative analytics with Plotly
Table of Contents
1 Data science life hacks
2 What you should know
3 Introduction to the data professions
4 The four flavors of data analysis
5 Why use Python for analytics
6 High-level course road map
7 Filtering and selecting
8 Treating missing values
9 Removing duplicates
10 Concatenating and transforming
11 Grouping and aggregation
12 The three types of data visualization
13 Selecting optimal data graphics
14 Communicating with color and context
15 Creating standard data graphics
16 Defining elements of a plot
17 Plot formatting
18 Creating labels and annotations
19 Visualizing time series
20 Creating statistical data graphics
21 Simple arithmetic
22 Basic linear algebra
23 Generating summary statistics
24 Summarizing categorical data
25 Parametric correlation analysis
26 Non-parametric correlation analysis
27 Transforming dataset distributions
28 Extreme value analysis for outliers
29 Multivariate analysis for outliers
30 BeautifulSoup object
31 NavigableString objects
32 Data parsing
33 Web scraping in practice
34 Introduction to NLP
35 Cleaning and stemming textual data
36 Lemmatizing and analyzing textual data
37 Introduction to Plotly
38 Create statistical charts
39 Line charts in Plotly
40 Bar charts and pie charts in Plotly
41 Create statistical charts
42 Next steps
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