UCLA Extension

UCLA Extension Introduction to Data Science (COM SCI X450.1) and Exploratory Data Analysis and Visualization (COM SCI X450.2) class materials. Use this repo to access supplemental learning resources, eBooks, and other handouts. Additional content may be added throughout the course. Past students are always welcome!

Supplemental Resources - Data Science

• UVA Data Points: a podcast exploring the world of data science - Cool podcast from School of Data Science, University of Virginia
• Data Scientist vs Machine Learning Engineer - A great distinction!
• Data Is An Art, Not Just A Science—And Storytelling Is The Key - Data storytelling is key to successful data science
• What Is Causal Inference? An Introduction for Data Scientists - A great intro to this growing area
• My Journey into AI Webinar - Panel discussion hosted by DeepLearning.AI
• All my opendatascience.com articles - Many article keeping pace with the field of data science
• Google Dataset Search - NEW! Resource for data scientists
• The Importance of SQL in Practicing Data Science - Reinforcing my advice in class!
• What is Data Science 'Impostor Syndrome'? - Avoid the fear of what you don't know
• 6 Reasons Why Data Science Projects Fail - A report from down in the trenches.
• The Difference Between Data Scientists and Data Engineers - A guide to becoming a unicorn.

Supplemental Resources - Machine Learning

• forester: an R package for automated building of tree-based models -
• A UseR’s Introduction to Machine Learning in AWS - Nice Youtube presentation for doing ML on AWS with R
• The Hitchhiker’s Guide to Responsible Machine Learning - Educational comic book
• Feature Engineering and Selection: A Practical Approach for Predictive Models - Nice learning resource by Max Kuhn and Kjell Johnson
• Data Science Meetup (Feb. 26, 2020) Gradient Boosting Machines (GBM): From Zero to Hero - Slides from a great Meetup
• Data Science Meetup (Feb. 26, 2020) Gradient Boosting Machines (GBM): From Zero to Hero - GitHub repos with R and Python code
• NGBoost: Natural Gradient Boosting for Probabilistic Prediction - HOT new machine learning algorithm using boosting
• VIDEO presentation from LA West R Meetup group - Better Than Deep Learning Gradient Boosting Machine 2019
• Linear Regression with Healthcare Data for Beginners in R - Nice starter exercise for newbie data scientists
• Evaluate your R model with MLmetrics - Using R’s MLmetrics to evaluate machine learning models. MLmetrics provides several functions to calculate common metrics for ML models, including AUC, precision, recall, accuracy, etc.
• Assessment Metrics for Clustering Algorithms - Metrics for clustering and unsupervised machine learning

Supplemental Resources - R Coding

• R-universe - Topical search engine for R packages
• R advantages over python - R has many advantages over python that should be taken into consideration when choosing which language to use for Data Science
• Time Series Analysis in R: How to Read and Understand Time Series Data
• Tidyverse vs. Base-R: How To Choose The Best Framework For You - The base R vs. tidyverse debate rages on!
• Static and Dynamic Web Scraping with R - Over view of web scraping with R
• Nice self-contained data project example - CO2 Emissions Comparing and Modeling for Global Warming
• Presentations from So Cal R User Group Meetup - Youtube channel for the So Cal R Users Group
• How to Create a Dataframe in R with 30 Code Examples - Useful data frame tutorial in R
• Getting started simulating data in R: some helpful functions and how to use them - Nice blog article on simulated data in R
• TidyverseSkeptic - Tidyverse Skeptic essay by Norm Matloff
• useR! Conference - Premiere global R conference
• Python vs R in Data Science: Which one is better? - Balanced overview of the R and Python data science programming languages
• R vs Python: Different similarities and similar differences - Nice cross-comparison of the R and Python data science programming languages
• Descriptive Statistics in R - Good resource for Week 7 of class on EDA
• Demystifying Regular Expressions in R - Intro to text analytics
• Text Data Analysis in R: Understanding grep, grepl, sub and gsub - Practical use of regular expressions
• Unearthing Golden Nuggets of Data: A RegEx Treasure Hunt in R - RegEx tutorial in R with stringr
• Vignette: data.table - Data.table is an extension of data.frame package in R
• How Tidyverse Guides R Programmers Through Data Science Workflows - A cohesive way of approaching data science projects
• Type conversion and you (or and R) - More examples on type conversion and coercion in R
• Essential list of useful R packages for data scientists - Great list of important R packages for data scientists
• R plot pch symbols : The different point shapes available in R - An examination of all the popular PCH argument values for data vizualizations
• R color names
• A Complete Tutorial on Time Series Modeling in R
• Web scraping 101 - A vignette for the rvest R package

Supplemental Resources - Python Coding

• Deep Learning with Python - Second edition of the popular book
• Intro to Python Programming - Kaggle course on very basics of Python language
• An Introduction to Statistical Learning: with Applications in R... with Python! - Solutions to ISL exercises in Python
• Python Programming Tutorials - Many tutorial resources for Python coding for data science and machine learning
• Talk Python - A podcast on Python and related technologies

Supplemental Resources - Mathematics

• Linear Algebra via MIT OpenCourseWare - Learn linear algebra from Gil Strang, the best of the best!
• Calculus — Multivariate Calculus And Machine Learning -- A Must Know Concept For Every Professional - Here is the bare minimum Calculus necessary for machine learning.

Supplemental Resources - Statistics

• Regression Modeling Strategies - A new 2025 resource for regression methods using R.
• All of Statistics: A Concise Course in Statistical Inference - A widely respected book on statistics with R code.
• The Book of R: A First Course in Programming and Statistics - As mentioned in class, a great way to learn statistics.
• Do my data follow a normal distribution? - A note on the most widely used distribution and how to test for normality in R.
• Fisher's exact test in R: independence test for a small sample - Focuses on the Fisher’s exact test. Independence tests are used to determine if there is a significant relationship between two categorical variables.

Supplemental Resources - Books

• Introduction to Statistical Learning with R - Great book on ML to use following this class.
• Elements of Statistical Learning - The "Machine Learning Bible."
• Mathematics for Machine Learning - Wonderful all-in-one text for getting up to speed with the mathematics required for machine learning.
• Linear Algebra Done Right - ML is all about Linear Algebra. Great learning resource.
• Bookdown - A collection of free eBooks on data science and R.
• Probabilistic Machine Learning: An Introduction - Machine learning built on a foundation of probability theory.

Excellent Post-Class Project Examples - Past Intro to Data Sciencce Students

• Using NFL Analytics to Contextualize Player Performance & Predict Playoff Probability - Work done by student Jesse Lubell (Summer 2021)
• Analysis of Reptile & Amphibian Observations in Los Angeles County - Work done by student Timothy Stegman (Fall 2020)
• Analysis of Match Statistics and Team Performances in the Premier League From Season 2015/16 to Season 2019/20 - Work done by student Tara Nguyen(Fall 2020)
• What Makes Us Happy - Using Kaggle "Young People Survey" data set - Work done by student Alexander Fichtl, taking course from Germany (Spring 2020)
• Data Analysis Evolution of Popular Music - Work done by student William Toth (Spring 2020)
• Airbnb Price Prediction for different areas in NYC - Work done by student Hashneet Kaur (Winter 2020)
• Predicting-Hotel-booking-demand-and-cancellation - Work done by student Elaine Kuang (Winter 2020)
• Analysis-of-Coronavirus-COVID-19-New-Confirmed-Cases - Work done by student Micky Lee (Winter 2020)
• Data Analysis for 2019 Indian General Election - Work done by student Junhui Yang (Winter 2020)
• FIVB Beach Volleyball Historic Top 8 Teams Analysis - Work done by student Tyler Widdison (Fall 2019)
• Data Analysis for PM2.5 in Beijing - Work done by student Xiaozhu Zhang (Spring 2019)

Excellent Class Project Examples - Past EDA and Dataviz Students

• Diabetic Patient Readmission — Exploratory Data Analysis - Work done by student Nami Sharifzadeh (Summer 2026)
• Credit Card Fraud Detection: An Exploratory Data Analysis - Work done by student Kingsley Egei (Spring 2026)
• E-Commerce-Purchase-Conversion-Analysis - Work done by student Shuotong Li (Spring 2026)

Authors

• Daniel D. Gutierrez - LinkedIn

License

This project is licensed under the MIT License - see the LICENSE.md file for details