Who this is for
- Complete beginners who want a real Python foundation for analytics before moving into machine learning.
Not for you if
- You already know Python and Pandas — this is the on-ramp
- You want advanced ML — take this first, then Python MachineZ
- You want R — this is Python
What you leave with
- Python syntax and data structures, from scratch
- Data cleaning and analysis with Pandas
- Clear visualizations with Matplotlib and Plotly
- Reproducible project workflows
The full curriculum
9 sections · 66 lessons. Every section is listed with its lesson count; click any section to see its lessons.
01Course Introduction4 lessons
1.1Course Overview
1.2What is Data Science?
1.3Data Science in Sports
1.4Python for Data Science
02Python Basics10 lessons
2.1Introductory Python
2.2Syntax and Variables
2.3Data Types and Structures
2.4Conditional Statements
2.5Loops
2.6Functions
2.7Libraries and Modules
2.8Exception Handling
2.9Overview
2.10Exercises
Career payoffPython and Pandas is the entry ticket to almost every data role.
03The NumPy Library9 lessons
3.1The NumPy Array
3.2Generating Random Numbers
3.3Array Operations
3.4Aggregation and Ufuncs
3.5Filtering and Sorting
3.6Array Manipulation
3.7Linear Algebra
3.8Overview
3.9Exercises
04The Pandas Library12 lessons
4.1Introduction to Pandas
4.2Importing Data
4.3Basic Data Exploration
4.4Filtering and Slicing DataFrames
4.5Data Cleaning
4.6String Manipulation
4.7Aggregating and Summarizing Data
4.8Data Transformations
4.9Combining and Merging
4.10Exporting Data
4.11Overview
4.12Exercises
Career payoffClean, reproducible analysis is what gets you past the take-home.
05Case Study: Data Wrangling4 lessons
5.1Case Study Overview
5.2Data Wrangling
5.3Creating Grid Application
5.4Exercises
06Data Visualization9 lessons
6.1Introduction to Data Visualization
6.2Anantomy of a Plot
6.3The Matplotlib Library
6.4Matplotlib Customization
6.5The Seaborn Library
6.6Seaborn Customization
6.7Interactive Plots in Plotly
6.8NHL Play-by-Play
6.9Exercises
07Introduction to Modeling7 lessons
7.1Models Based on Similarity
7.2Regression vs. Classification
7.3Data Splitting
7.4The k-NN Algorithm
7.5K-Means Clustering
7.6Hierarchical Clustering
7.7Exercises
Career payoffThis is the on-ramp to Python MachineZ and the job market.
08Case Study: Data Analysis9 lessons
8.1The EDA Process
8.2Defining the Problem
8.3Collecting our Data
8.4Cleaning our Data
8.5Aggregating our Data
8.6Assessing Data Quality
8.7Analyzing our Data
8.8Modeling
8.9Clustering
09Looking Ahead2 lessons
9.1Course Recap
9.2Machine Learning in Sports
What's included
- Lifetime accessEvery module and lesson, yours to keep and revisit, forever.
- Posit cloud workspaceA provisioned enterprise IDE and compute. Nothing to install.
- Project files and codeEvery notebook, dataset, and finished build, to keep and adapt.
- All future updatesNew lessons and refreshes as the tools move, at no extra cost.
- Self-pacedStart today, go at your own pace, no cohort to wait for.
- Free with MembershipOr get this course and the full catalog with Membership.
Your instructor
Evan Callaghan
Data Science Instructor, AthlyticZ | Creator of FoundationZ
Known for Creator of FoundationZ
Evan has been a Data Science Instructor at AthlyticZ since 2023, where he created FoundationZ of Data Science and developed the supplementary materials and tutorials for BreeZing Through the Tidyverse. A graduate student and Graduate Teaching Assistant at Queen's University, he teaches calculus and statistics tutorials, and is a Shattuck-St. Mary's alum.
Questions
Is this included with Membership?
Yes. AthlyticZ Membership ($2,999/yr) includes this and the entire course catalog, plus the live Masterclass. If you plan to take more than a couple of courses, Membership is the better math.
Is it self-paced?
Yes. Start today and go at your own pace, with lifetime access to every lesson and all future updates.
Do I need to install anything?
No. You work on the provisioned Posit platform in the browser, on the same enterprise tools professional teams use.