Data Science Fundamentals
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.
- Pace
- Self-paced, start any week
- Length
- 8 weeks · 32 video lessons
- Repo
- paiml/data-science-fundamentals-applied-program
Weekly email reminders: join the programjoin the program. Or add the 8 weeks to your calendar, starting one week from today:
Outline
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
- Meet your course instructor Alfredo DEZA Members
- Lesson introduction variables and types Members
- Working with different data types Members
- Conditionals and evaluations Members
- Catching and handling exceptions Members
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
- Introduction to lists Members
- Creating and Iterating over lists Members
- Introduction to dictionaries Members
- Creating and Iterating over dictionaries Members
- Extracting data from lists Members
- Extracting data from dictionaries Members
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
- Lesson introduction basic Pandas usage Members
- Introduction to Pandas Members
- Loading data into Pandas Members
- Writing data from Pandas Dataframes Members
- Exploratory analysis with Pandas Members
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)
- Common Dataframe operations Members
- Manipulating text in Dataframes Members
- Applying functions with Pandas Members
- Visualizing data with Pandas Members
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)
- Introduction to Numpy Arrays Members
- Common Numpy array operations Members
- More Numpy array operations Members
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
- Cleaning up data Members
- Scaling data Members
- Labeling data Members
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)
- Identifying and Extracting features Members
- Feature engineering concepts Members
- Graphing data Members
- Data Visualization Members
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
- Lesson Recap working with Dataframes Members
- Clustering data Members
Roles it prepares you for
- Junior Data Analyst
- Data Science Intern
- Business Intelligence Analyst
- Junior Data Engineer
- Analytics Engineer (Entry-Level)
Skills you'll practice
- Python for Data Analysis
- Pandas DataFrames
- NumPy Arrays & Vectorized Computation
- Data Cleaning & Data Quality Checks
- Exploratory Data Analysis
- Basic Data Visualization
- Feature Engineering Fundamentals
Prerequisites
No prior data science experience required. Basic comfort using a terminal helps. Python experience is a plus but not required — Python basics are covered in Weeks 1-2.
Questions
Is there a grade or enrollment gate?
No. There's no grading, no enrollment gate, and no required login to work through the program's repository. Joining just sets up weekly email reminders and an optional calendar schedule.
Can I run this rolling or as a cohort?
Both. Rolling/self-paced means you start whenever you want with no meetings. Cohort runs alongside a shared start date with a dedicated Discord channel and an end-of-run demo call.
Do I need to log in to use the calendar invite?
No. The calendar buttons above work without logging in, with a schedule starting one week from today.
What does membership add?
Week 1's videos are free. Membership opens every week's videos and the full course library they come from. See pricing.