Python for Data Science and Machine Learning Essential Training Part 1
7h 35mIntermediate2026-03-30
Authors

Lillian Pierson, P.E.
Engineer, CEO, and Head of Product at Data-Mania
Course details
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.
Skills covered
Data Science FoundationsMachine LearningPythonEssential TrainingArtificial Intelligence (AI)Programming LanguagesData ScienceOpen SourceSoftware Development
Concepts
Introduction
- Data science life hacks
- How to use Codespaces with this course
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
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
Data Visualization 101
- Importance of visualization in data science
- The three types of data visualization
- Selecting optimal data graphics
- Communicating with color and context
Practical Data Visualization
- Introduction to the matplotlib and Seaborn libraries
- Defining elements of a plot
- Plot formatting
- Creating labels and annotations
- Visualizing time series
- Creating statistical data graphics in Seaborn
- Creating standard data graphics
Exploratory Data Analysis
- Simple arithmetic
- Generating summary statistics
- Summarizing categorical data
- Spearman rank correlation and Chi-square
- Extreme value analysis for outliers
- Multivariate analysis for outliers
- Pearson correlation analysis
Getting Started with Machine Learning
- Cleaning and treating categorical variables
- Transforming data set distributions
- Applied machine learning - Starter problem
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
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
- Portfolio and career readiness
- Next steps
Related courses
- Python for Data Science and Machine Learning Essential Training Part 2
- Python for Data Science Essential Training Part 2
- Google Colab Notebook Essential Training
- NumPy Essential Training: 2 MatPlotlib and Linear Algebra Capabilities
- NumPy Essential Training: 1 Foundations of NumPy
- Scala Essential Training for Data Science
- Azure Spark Databricks Essential Training
- Text Analytics and Predictions with Python Essential Training
Related learn paths
- Advance Your Python Skills for Data Science
- Introduction to Fundamental Skills for Data Work: Data Processing
- Advance Your Skills in AI and Machine Learning
- Mastering Executive-Level Data Analytics
- Advance Your Business Analytics Skills
- Master Microsoft Power BI
- Moving from Data Analyst to Data Scientist
- Become a Data Scientist