Applied Program

Data Science Fundamentals Applied Program

An 8-week, project-based, beginner program that teaches core data science with Python (pandas, NumPy, data cleaning) by building a real data-quality tool. No prior data science experience required — ship a working project that detects problems in a real dataset.

Starts 1 week from today — add to your calendar:

View Syllabus

Project-based, Portfolio-first

Build a real application over 8 weeks and ship it publicly — no grading, no enrollment gate.

8
Weeks
32
Video Resources
24
Hands-on Tasks
Weekly reminders

Email reminders each week once you join, starting one week out

Weekly demo prompts

Turn each week's work into a portfolio-ready update

Open on GitHub

https://github.com/paiml/data-science-fundamentals-applied-program

Weekly Syllabus

8 weeks, one project, shipped publicly

Week 1
Python for Data Science + Choosing a Dataset
  • Set up Python and a notebook/editor environment for data work
  • Learn core Python variables, data types, and conditionals
  • Choose the dataset your project will analyze, and what "a problem" means for it
Week 2
Data Structures for Data Work
  • Learn lists, dictionaries, tuples, and sets in Python
  • Practice iterating over and extracting data from collections
  • Load your dataset's raw rows into plain Python structures before touching pandas
Week 3
Pandas Basics
  • Load your dataset into a pandas DataFrame
  • Learn core DataFrame exploration (head, info, describe, dtypes)
  • Write your first exploratory pass over the data
Week 4
Working with DataFrames
  • Learn common DataFrame operations, filtering, and text manipulation
  • Apply functions across columns to compute derived values
  • Build the first pass of your data-quality checks (missing values, duplicates)
Week 5
NumPy Basics
  • Learn NumPy arrays and vectorized operations
  • Use NumPy to compute statistics (mean, std, percentiles) for outlier detection
  • Add a numeric outlier check to your project (e.g. values outside N standard deviations)
Week 6
Data Cleaning & Scaling
  • Learn common data-cleaning techniques (handling nulls, fixing types, deduplication)
  • Learn when and why to scale numeric data
  • Extend your project to flag inconsistent types and out-of-range categorical values
Week 7
Feature Engineering & Visualization
  • Learn basic feature engineering and feature extraction concepts
  • Build simple visualizations (distributions, bar charts) to surface data problems visually
  • Turn your project's findings into a clear, readable report (console output, Markdown, or a notebook)
Week 8
Ship & Finalize
  • Run your project against the self-assessment checklist
  • Polish the README with real setup/usage instructions and sample output
  • Publish the repository publicly and write a short portfolio/LinkedIn post linking to it

Frequently Asked Questions

Everything you need to know about the Data Science Fundamentals Applied Program

No. There's no grading, no enrollment gate, and no required login to work through the program's repository. Joining here just sets up weekly email reminders and an optional calendar schedule.

By default, your Week 1 reminder and calendar event land one week after you join — giving you time to prep. You can pick a custom start date for your calendar invite using the date field above.

Yes — use the same button to leave at any time, which stops the weekly email reminders.