Data Science for Real People
Like Duolingo, but for Data Science for Real People. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
For the part of you with thirty open tabs that never became anything.
Free during early access · No credit card · iPhone & Android
Key ideas in Data Science for Real People
- Analysts focus on reporting historical trends
- Objectives must be defined before data is touched
- Data scientists build predictive models to anticipate future outcomes
- Difference between a data scientist and a traditional analyst
- Models must be evaluated against business goals before being launched
- The logical flow of the CRISP-DM framework
- Vague requests must be translated into measurable metrics
- Defining success criteria prevents wasted technical effort
- Outcomes can be automated decision systems
- Outcomes can be strategic recommendations based on evidence
- Business Understanding
- Data Science Outcomes
- Data must contain a signal or pattern related to the problem
- Some problems are better solved through policy or design than data
- Why Business Understanding is the critical first step
- Domain knowledge identifies 'hidden' variables
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Data Science for Real People the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
Before ye start diggin' through the data treasure, what must a data scientist define first?
Get it right to open this lesson and 174 more in the app.
Where Data Science for Real People takes you
Stop staring at spreadsheets and start telling stories with data. Learn to solve real-world problems using Python, SQL, and visual tools while mastering the art of the data-driven decision.
- 1
Your First Big Discovery
- What does a data scientist actually do?
- Finding patterns in a simple table
- Making your first chart in Tableau
- Asking questions that data can answer
- 2
Cleaning Up the Mess
- Why real data is always broken
- Fixing missing info and typos
- Handling outliers that ruin your average
- Standardizing dates and names
- The art of data janitorial work
- 3
Talking to Databases with SQL
- Asking a database for exactly what you need
- Filtering out the noise
- Combining data from different places
- 4
Automating with Python
- Python basics for the impatient
- Using Pandas to handle huge files
- Writing scripts to do your chores
- Visualizing data with code
- Reading data from the web
- Cleaning data 10x faster with Python
- 5
Thinking Like a Statistician
- The danger of averages
- Understanding spread and variance
- Probability in plain English
- How to tell if a change is actually meaningful
- 6
Predicting the Future
- Drawing a line through your data
- Grouping similar things together
- Teaching a computer to recognize patterns
- Testing if your prediction is any good
- Common traps in machine learning
- 7
The Professional Workflow
- The CRISP-DM roadmap
- Defining the business problem
- Deploying your results to the world
- 8
Storytelling and Ethics
- How to present to a boss
- Avoiding charts that lie
- The ethics of data privacy
- Bias in algorithms and how to spot it
- Building your first portfolio project
8 sections · 35 units · 175 levels. Built to play, not to enroll.
You pick the voice
Data Science for Real People is taught in the The Pirate style: arr, learning be an adventure!. Want a different feel? In the app you can spin up the same topic in any of Tomo's teaching styles. Same facts, totally different vibe.
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Start Data Science for Real People today.
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