Python for Data Science Essential Training Part 2

Introduction

• Data science life hacks
• What you should know
• How to use Codespaces with this course

1. Introduction to the Data Professions

• Introduction to the data professions
• Data science careers: Identifying where and how you'll thrive
• Why to use Python for analytics
• High-level course road map

2. Data Preparation Basics

• Intro to data preparation
• Numpy and pandas basics
• Filtering and selecting
• Treating missing values
• Removing duplicates
• Concatenating and transforming
• Grouping and aggregation

3. Data Visualization 101

• Importance of visualization in data science
• The three types of data visualization
• Selecting optimal data graphics
• Communicating with color and context

4. Practical Data Visualization

• Introduction to the matplotlib and Seaborn libraries
• Creating standard data graphics
• Defining elements of a plot
• Plot formatting
• Creating labels and annotations
• Visualizing time series
• Creating statistical data graphics in Seaborn

5. Exploratory Data Analysis

• Simple arithmetic
• Generating summary statistics
• Summarizing categorical data
• Pearson correlation analysis
• Spearman rank correlation and Chi-square
• Extreme value analysis for outliers
• Multivariate analysis for outliers

6. Getting Started with Machine Learning

• Cleaning and treating categorical variables
• Transforming data set distributions
• Applied machine learning: Starter problem

7. Data Sourcing via Web Scraping

• Introduction of web scraping
• Python requests for automating data collection
• BeautifulSoup object
• NavigableString objects
• Data parsing
• Web scraping in practice
• Asynchronous scraping

8. Collaborative Analytics with Streamlit

• Introduction to Streamlit
• Environment setup
• Create basic charts
• Line charts in Streamlit
• Bar charts and pie charts in Streamlit
• Create statistical charts

Conclusion

• Next steps