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Python for Data Science and Machine Learning Essential Training Part 1

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Python for Data Science and Machine Learning Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts-giving you even more hands-on, real-world Python experience. In part one, instructor Lillian Pierson takes you step by step through a data science and machine learning 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 web-based graphs using Streamlit. By the end of this course, you'll have acquired basic coding experience that you can take to your organization and quickly apply to your own custom data science and machine learning projects.

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time-all while using a tool that you'll likely encounter in the workplace. Check out the "Using GitHub Codespaces with this course" video to learn how to get started.


01 - Introduction
  • 01 - Data science life hacks
  • 02 - What you should know
  • 03 - How to use Codespaces with this course

  • 02 - 1. Introduction to the Data Professions
  • 01 - Introduction to the data professions
  • 02 - Data science careers Identifying where and how youll thrive
  • 03 - Why to use Python for analytics
  • 04 - High-level course road map

  • 03 - 2. Data Preparation Basics
  • 01 - Intro to data preparation
  • 02 - Numpy and pandas basics
  • 03 - Filtering and selecting
  • 04 - Treating missing values
  • 05 - Removing duplicates
  • 06 - Concatenating and transforming
  • 07 - Grouping and aggregation

  • 04 - 3. Data Visualization 101
  • 01 - Importance of visualization in data science
  • 02 - The three types of data visualization
  • 03 - Selecting optimal data graphics
  • 04 - Communicating with color and context

  • 05 - 4. Practical Data Visualization
  • 01 - Introduction to the matplotlib and Seaborn libraries
  • 02 - Creating standard data graphics
  • 03 - Defining elements of a plot
  • 04 - Plot formatting
  • 05 - Creating labels and annotations
  • 06 - Visualizing time series
  • 07 - Creating statistical data graphics in Seaborn

  • 06 - 5. Exploratory Data Analysis
  • 01 - Simple arithmetic
  • 02 - Generating summary statistics
  • 03 - Summarizing categorical data
  • 04 - Pearson correlation analysis
  • 05 - Spearman rank correlation and Chi-square
  • 06 - Extreme value analysis for outliers
  • 07 - Multivariate analysis for outliers

  • 07 - 6. Getting Started with Machine Learning
  • 01 - Cleaning and treating categorical variables
  • 02 - Transforming data set distributions
  • 03 - Applied machine learning Starter problem

  • 08 - 7. Data Sourcing via Web Scraping
  • 01 - Introduction of web scraping
  • 02 - Python requests for automating data collection
  • 03 - BeautifulSoup object
  • 04 - NavigableString objects
  • 05 - Data parsing
  • 06 - Web scraping in practice
  • 07 - Asynchronous scraping

  • 09 - 8. Collaborative Analytics with Streamlit
  • 01 - Introduction to Streamlit
  • 02 - Environment setup
  • 03 - Create basic charts
  • 04 - Line charts in Streamlit
  • 05 - Bar charts and pie charts in Streamlit
  • 06 - Create statistical charts

  • 10 - Conclusion
  • 01 - Next steps
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