Python for Data Science Essential Training Part 1

6h 4mIntermediate2019-10-24

Authors

Lillian Pierson, P.E.

Engineer, CEO, and Head of Product at Data-Mania

Course details

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.

Learning objectives
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

Skills covered

Data Science FoundationsPythonEssential TrainingProgramming LanguagesData ScienceOpen SourceSoftware Development

Concepts

Introduction

Data science life hacks
What you should know

Introduction to the Data Professions

Introduction to the data professions
The four flavors of data analysis
Why use Python for analytics
High-level course road map

Data Preparation Basics

Filtering and selecting
Treating missing values
Removing duplicates
Concatenating and transforming
Grouping and aggregation

Data Visualization 101

The three types of data visualization
Selecting optimal data graphics
Communicating with color and context

Practical Data Visualization

Creating standard data graphics
Defining elements of a plot
Plot formatting
Creating labels and annotations
Visualizing time series
Creating statistical data graphics

Basic Math and Statistics

Simple arithmetic
Basic linear algebra
Generating summary statistics
Summarizing categorical data
Parametric correlation analysis
Non-parametric correlation analysis
Transforming dataset distributions
Extreme value analysis for outliers
Multivariate analysis for outliers

Data Sourcing via Web Scraping

BeautifulSoup object
NavigableString objects
Data parsing
Web scraping in practice
Introduction to NLP
Cleaning and stemming textual data
Lemmatizing and analyzing textual data

Collaborative Analytics with Plotly

Introduction to Plotly
Create statistical charts
Line charts in Plotly
Bar charts and pie charts in Plotly
Create statistical charts

Conclusion

Next steps