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Python for Data Science Essential Training Part 2

Offered By: LinkedIn Learning

Tags

Python Courses Data Science Courses Machine Learning Courses Neural Networks Courses Predictive Analytics Courses Decision Trees Courses Regression Models Courses Dimension Reduction Courses Ensemble Models Courses

Course Description

Overview

Learn Python programming skills for data science and machine learning. Discover how to clean, transform, analyze, and visualize data, as you build a practical, real-world project.

Syllabus

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

Taught by

Lillian Pierson, P.E.

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