Python Essentials
Variables, control flow, functions, and notebooks—clean, modern patterns you’ll reuse everywhere.
FoundationZ — Data Science Course
Unleash the Power of Data Science Foundations
The perfect on-ramp to Python for analytics. Master core syntax, data wrangling, plotting, and best practices—so you’re ready to thrive in MachineZ (and on the job).
Python Basics
Pandas
Data Cleaning
Visualization
Analysis Patterns
What You’ll Learn
A hands-on, beginner-friendly path that sets you up for Machine Learning success.
Pandas for Analysis
Import, clean, join, pivot, group, and summarize data with confidence and speed.
Visualization
Tell clear stories with Matplotlib/Plotly—bar/line/scatter, small multiples, and publication-ready charts.
Project Workflow
Reproducible environments, tidy project structure, and reporting so your work is easy to share.
What You’ll Learn
Summary
In this lesson we emphasize the importance of data splitting in machine learning. You’ll see both manual and randomized splitting using pandas, numpy, and scikit-learn.
Key Definitions
- Data Splitting: Dividing a dataset into training and testing sets.
- Training Set: Used to fit the model.
- Testing Set: Used to evaluate performance.
- Manual Splitting: Predefined criteria (e.g., % split).
- Randomized Splitting: Random allocation for unbiased representation.
Example Code
# Manual data splitting
train = data.iloc[:427]
test = data.iloc[427:]
# Randomized data splitting
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, stratify=y)Reflection Point
Randomized splitting prevents biases and ensures fair model evaluation—critical for industry-quality pipelines.
Thoughts from AthlyticZ
Understanding data splitting is fundamental for reliable, generalizable models. This principle is core to how employers expect ML projects to be structured in production.
Start for $39 — Enroll Now▶ Preview Lesson: Data Splitting
Feeling Overwhelmed by Data Science?
You’re not alone. We make fundamentals practical and global—so you can use Python at work in analytics, finance, health, retail, media, and sports.
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Python syntax & core concepts
Variables, control flow, functions—taught with patterns you’ll reuse everywhere.
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NumPy & Pandas
Get comfortable cleaning, joining, aggregating, and reshaping real datasets.
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Visualization that tells a story
Publication-ready charts with Matplotlib/Plotly that stakeholders understand.
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Too many tools
A curated stack that scales—from notebook to shareable report or app.
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First steps in ML
Train/test split, metrics, and starter models the right way—no fluff.
Unlock Globally Relevant Python Skills
The curriculum is designed so your work transfers between domains—business analytics, healthcare, finance, retail, media, and sports.
Python Essentials
Write clean, modern Python with confidence—functions, modules, and notebook best practices.
NumPy & Pandas
Vectorized transforms, joins, pivots, and time-aware workflows that scale to millions of rows.
Data Wrangling
From raw CSV to analysis-ready tables with bulletproof cleaning & validation steps.
Visualization
Bar/line/scatter, small multiples, and executive-ready plots using Matplotlib and Plotly.
Modeling Basics
Train/test split, metrics, and first models—groundwork for MachineZ and on-the-job ML.
Decision-Ready Insights
Turn analysis into clear, defensible recommendations stakeholders can act on quickly.
Meet Your Data Science Coach
Evan Callaghan — helping you build portable, industry-relevant Python skills.
Passionate about the intersection of data science and sports analytics.
Evan is a data scientist and instructor focused on teaching globally relevant Python skills. His work centers on clean analysis workflows, practical visualizations, and the first steps into machine learning the right way.
In FoundationZ of Data Science, you’ll master Python essentials, NumPy and Pandas for fast data manipulation, and storytelling charts with Matplotlib/Plotly. You’ll also learn project structure and evaluation habits that transfer to any industry—analytics, finance, health, retail, media, and sports.
“Evan’s course is a must for anyone diving into data science. NumPy, Pandas, data wrangling, and machine learning concepts were explained flawlessly. Great experience overall!”
Interactive Syllabus
A practical, employer-ready path—from Python basics to analysis patterns and first models. Expand any section for lessons and the concrete skills you’ll gain.
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1) Course Introduction
1) Course Introduction
Set expectations and language. You’ll see what “data science” really means at work, how sports examples map to any industry, and how Python fits your toolkit.
DS lifecycleroles & workflowsPython setup
- Course Overview
- What is Data Science?
- Data Science in Sports (transferability)
- Python for Data Science (environments, notebooks)
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2) Python Basics
2) Python Basics
Write clean, modern Python. Master variables, control flow, functions, exceptions, and modules so your analysis code is reliable and reusable.
syntax & styledata structuresfunctionsmodulesrobust code
- Introductory Python; Syntax & Variables
- Data Types & Structures
- Conditionals & Loops
- Functions
- Libraries & Modules
- Exception Handling
- Overview • Exercises • Quiz
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3) The NumPy Library
3) The NumPy Library
Think in vectors. Use arrays, broadcasting, ufuncs, and linear algebra to speed up operations by orders of magnitude—critical for ML and large data.
vectorizationrandomnessaggregationLinalg
- Arrays & Random Numbers
- Array Operations; Aggregation & Ufuncs
- Filtering, Sorting, Manipulation
- Linear Algebra
- Overview • Exercises • Quiz
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4) The Pandas Library
4) The Pandas Library
From raw data to tidy tables. Import, clean, filter, reshape, aggregate, join, and export with patterns you’ll reuse on the job.
IO & cleaninggroupbyjoins & mergesreshapingexport
- Intro to Pandas; Importing Data
- Exploration; Filtering & Slicing
- Cleaning & String Ops
- Aggregating & Summaries
- Transformations; Combining & Merging
- Exporting Data
- Overview • Exercises • Quiz
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5) Case Study: Data Wrangling
5) Case Study: Data Wrangling
Put it together end-to-end. Clean messy inputs, engineer features, and build a small grid app to interact with results.
EDA workflowfeature craftlight UI
- Case Study Overview
- Data Wrangling
- Creating Grid Application
- Exercises • Quiz
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6) Data Visualization
6) Data Visualization
Tell clear, credible stories. Master chart anatomy, customization, and interactive visuals—then apply it to NHL play-by-play.
chart designseaborn stylesinteractivesports case
- Intro & Chart Anatomy
- Matplotlib + Customization
- Seaborn + Customization
- Interactive Plots in Plotly
- NHL Play-by-Play; Exercises • Quiz
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7) Introduction to Modeling
7) Introduction to Modeling
From patterns to predictions. Learn similarity-based thinking, regression vs. classification, sound data splitting, and clustering methods.
train/testk-NNk-meanshierarchical
- Models Based on Similarity
- Regression vs. Classification
- Data Splitting
- The k-NN Algorithm
- k-Means & Hierarchical Clustering
- Exercises • Quiz
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8) Case Study: Data Analysis
8) Case Study: Data Analysis
Ship an analysis like a pro. Frame a problem, collect/clean/aggregate, assess data quality, analyze, and prototype a model + clustering.
problem framingquality checksinsight comms
- The EDA Process; Defining the Problem
- Collecting & Cleaning Data
- Aggregating; Assessing Data Quality
- Analyzing; Modeling; Clustering
- Quiz
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9) Looking Ahead
9) Looking Ahead
Plan your next move. Recap key wins and roll into MachineZ to build predictive models with an MLB analytics lead.
career focusportfolio plan
- Course Recap
- Up Next: Machine Learning with Pat McFarlane
What Industry Experts Are Saying About AthlyticZ
Discover how leaders view the quality and impact of our training.
“Here’s your chance to learn future sports data gurus!”
“AthlyticZ has completely transformed the learning approach to data science through the use of sports-based problems. The course structure is intuitive, the content is comprehensive, the instructors are the best of the best, and the practical projects have immediate impact to students.”
Bill Geivett, M.Ed.
President @ IMA Team • Author: Do You Want To Work In Baseball?
Sr. VP: Colorado Rockies (2001–2014) • Asst. GM: Los Angeles Dodgers (1998–2000)
“Empowering and engaging.”
“I’ve always been intrigued by the intersection of sports analytics and data science. AthlyticZ provides students with the perfect platform to explore this passion. The hands-on projects are particularly empowering, allowing them to apply theoretical concepts to real-world scenarios.”
Brad Smith, PhD
Senior Performance Analyst @ University of Nebraska • Sports Analytics Professor, Data Science Program @ Northwestern • Player Development Analyst @ New York Yankees (2018–2021)
How We Conduct Our Course
At AthlyticZ, we design for flexibility and impact—mixing clear instruction, continuous assessment, and applied projects so you can learn at your pace and show real results.
Prerecorded Video Lessons
Learn on your schedule with concise, high-quality videos you can pause, replay, and revisit anytime.
Assignments & Exercises
Reinforce concepts with practical exercises that build intuition and confidence in real workflows.
Continuous Assessment
Low-friction quizzes and checkpoints give you immediate feedback and keep you on track.
Hands-On Projects
Tackle real analytics problems—from quick wins to capstones—so you graduate with portfolio-ready work.
Supplementary Resources
Get curated readings, slides, and short references to deepen understanding and speed up execution.
Interactive Features
Use lesson-embedded notes, preloaded IDEs, and live widgets for an engaging, hands-on experience.
Frequently Asked Questions
Quick answers about FoundationZ of Data Science and how it supports career-ready Python skills.
Can I expense this course with my employer? ▶
Professional Development
Many learners use professional-development budgets to cover FoundationZ. The course delivers skills your team can apply immediately:
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Streamline Data Analysis
Build essential Python skills to speed up data prep, exploration, and reporting—reducing manual effort and time to insight.
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Optimize Workflows
Create clean, reusable notebooks and pipelines tailored to your organization’s datasets and KPIs.
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Cost-Efficient Solutions
Leverage open-source Python libraries (NumPy, Pandas, Matplotlib/Seaborn/Plotly) rather than costly analytics tools.
If you need an invoice or proof of enrollment for reimbursement, simply use the receipt from checkout or reach out to us for a formal invoice.
Do I need access to any commercial software? ▶
No. Everything runs on open-source tools in Python—no paid licenses required.
Does the course assume extensive knowledge of Python? ▶
No—FoundationZ is an introductory course designed to jumpstart your data-science journey and prepare you for MachineZ.
Is the course graded? What defines successful completion? ▶
We focus on mastery through practice, not test-taking. Completion requires:
- Score 80%+ on all quizzes.
- Finish 100% of the modules.
Meet these criteria and you’ll receive a certificate of completion.
What if I need to drop out? Is there a refund policy? ▶
We want you to succeed. Our policy:
- Full refund if you withdraw within 3 days of course commencement.
- No refunds after that date, or if you have completed 25%+ of the material.
Thanks for choosing AthlyticZ for your learning journey.
Looking for something else? Check the syllabus or reach out—happy to help.
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