How to learn data science

Vik Paruchuri

Dataquest (www.dataquest.io)

vik@dataquest.io

Dataquest

• In-browser data science tutorials
• Learn theory + application

How I got started

• BA in American History
• Learned using online resources

The problem

• Learning about data science is a nebulous process that isn't aligned with application

What is data science?

• Relatively new term
• Intersection of many fields

Increasing specialization

• Data engineer
• Data analyst
• Machine learning engineer

DS is about solving problems

• Have to learn by doing

Entry level job/project-readiness

• Basic algo skills
• "The rest"

Application

• Data cleaning 90% of work
• Tradeoffs when applying algorithms

Communication

• Explaining results
• Working with other teams

Systems

• Writing faster-running code
• Scaling to multiple machines

Programming

• Writing well-structured code
• Testing

Traditional ways to teach

• MOOCs
• Lectures

What not to do: videos

• Great if you want to be entertained
• Can't learn anything unless you're solving problems

What not to do: "syntax only"

• If you just learn syntax, you won't be able to transfer skills
• Critically important to learn how to think

What not to do: only follow guided tutorials

• Need to work on projects
• Have to take off the training wheels

What not to do: learn with multiple languages

• Highly recommend python
• Get very comfortable/good with one language first

How should you learn?

Step 1: Learn to code

• Python! (hopefully)
• Go to programming meetups
• Work on projects
• Go to hackathons

Step 2: Learn to work with data

• Find datasets.
  • UCI machine learning repository
  • Census.gov
  • Reddit /r/datasets
• Answer questions with the data
• Think about what/how you might predict
• Go to data science meetups

Step 3: Start with applied ML

• Get familiar with implementing linear regression
  • Scikit-learn docs
• Go to machine learning meetups
• Work on a project
• Progressively add pieces (error measurement, cross validation, etc)

Step 4: More application, and some theory

• Write blog posts, speak at meetups, and put projects on github
• Consult
• Learn statistics
• Learn linear algebra
• Learn more algorithms (knn/k-means/logistic regression)

Step 5: Get better at coding

• Unit test your algorithms
• Learn some basic computer science (sorting/search/etc)
• Refactor old code
• Get better at bash scripting

Step 6: Broaden the scope

• Keep working on projects
• Learn a couple more algorithms
• Work on larger datasets
• Learn about hardware, storage, and networking

How long will this take?

• Ongoing process
• 6 months to a couple of years upfront
• Things change quickly

How Dataquest can help

• Teaches fundamentals, not syntax
• Focuses on real-world datasets
• Adding in mentorship features
• Adding in projects

Questions?