Answer by William Chen:
Here are some amazing and completely free resources online that you can use to teach yourself data science.
Besides this page, I would highly recommend theas your comprehensive guide to data science! It includes resources similar to this one, as well as advice on preparing for data science interviews. Additionally, follow the Quora topic if you haven't already to get updates on new questions and answers!
Fulfill your prerequisites
Before you begin, you need Multivariable Calculus, Linear Algebra, and Python. If your math background is up to multivariable calculus and linear algebra, you'll have enough background to understand almost all of the probability / statistics / machine learning for the job.
Numerical Linear Algebra / Computational Linear Algebra / Matrix Algebra: , (starts 2/2/2015)
Multivariate calculus is useful for some parts of machine learning and a lot of probability. Linear / Matrix algebra is absolutely necessary for a lot of concepts in machine learning.
You also need some programming background to begin, preferably in Python. Most other things on this guide can be learned on the job (like random forests, pandas, A/B testing), but you can't get away without knowing how to program!
Python is the most important language for a data scientist to learn. To learn to code, more about Python, and why Python is so important, check out
If you're currently in school, take statistics and computer science classes. Check out
Plug Yourself Into the Community
Check outto find some that interest you! Attend an interesting talk, learn about data science live, and meet data scientists and other aspirational data scientists. Start reading data science blogs and following influential data scientists:
Setup and Learn to use your tools
- Install Python, iPython, and related libraries ()
- Installand (I would say that R is the second most important language. It's good to know both Python and R)
- Learn R with
- (You can practice it using the sqlite package in Python)
Learn Probability and Statistics
Be sure to go through a course that involves heavy application in R or Python. Knowing probability and statistics will only really be helpful if you can implement what you learn.
- Python Application:( ) (Python focus)
- R Applications:( )( ) (R focus)
- Print out a copy of
Complete Harvard's Data Science Course
This course is developed in part by a fellow Quora user, Professor. Note that I recommend completing the 2013 version of the class instead of the 2014 version.
Here are all of the materials!
Intro to the class
Lectures and Slides
- Intro to Python, Numpy, Matplotlib () ( )
- Poll Aggregation, Web Scraping, Plotting, Model Evaluation, and Forecasting () ( )
- Data Prediction, Manipulation, and Evaluation () ( )
- Predictive Modeling, Model Calibration, Sentiment Analysis () ( )
- Recommendation Engines, Using Mapreduce () ( )
- Network Visualization and Analysis () ( )
Do most of Kaggle's Getting Started and Playground Competitions
I would NOT recommend doing any of the prize-money competitions. They usually have datasets that are too large, complicated, or annoying, and are not good for learning ()
Start by learning scikit-learn, playing around, reading through tutorials and forums atfor a simple, synthetic, binary classification task. Next, play around some more and check out the tutorials for with a slightly more complicated binary classification task (with categorical variables, missing values, etc.)
Afterwards, try some multi-class classification with. Now, try a regression task that involves incorporating timestamps. Try out some natural language processing with . Finally, try out any of the other knowledge-based competitions that interest you!
Learn Some Data Science Electives
- Product Metrics will teach you about what companies track, what metrics they find important, and how companies measure their success:
- Optimization will help you with understanding statistics and machine learning:
- A/B Testing is just a rebranded version of what pharmaceutical companies have been doing for decades. Learn more about A/B testing here:
- Visualization – I would recommend picking up ggplot2 in R to make simple yet beautiful graphics and just browsingand for ideas and inspiration.
- User Behavior – This set of blogs posts looks useful and interesting –
Feature Engineering – Check outand this great example:
Big Data Technologies – These are tools and frameworks developed specifically to deal with massive amounts of data.
Machine LearningThis is an extremely rich area with massive amounts of potential. Andrew Ng's Machine Learning course on Coursera is one of the most popular MOOCs, and a great way to start!
Natural Language Processing – This is the practice of turning text data into numerical data whilst still preserving the "meaning". Learning this will let you analyze new, exciting forms of data.
Time Series Analysis –
Building a Data Culture –
Do a Capstone Product / Side Project
Use your new data science and software engineering skills to build something that will make other people say wow! This can be a website, new way of looking at a dataset, cool visualization, or anything!
Create public github repositories, make a blog, and post your work, side projects, Kaggle solutions, insights, and thoughts! This helps you gain visibility, build a portfolio for your resume, and connect with other people working on the same tasks.
Get a Data Science Internship or Job
Check outfor more discussion on internships, jobs, and data science interview processes! The data science FAQ also links to more specific versions of this question, like or the counterpart,
Think like a Data Scientist
In addition to the concrete steps I listed above to develop the skillset of a data scientist, I include seven challenges below so you can learn to think like a data scientist and develop the right attitude to become one.
(1) Satiate your curiosity through data
As a data scientist you write your own questions and answers. Data scientists are naturally curious about the data that they're looking at, and are creative with ways to approach and solve whatever problem needs to be solved.
Much of data science is not the analysis itself, but discovering an interesting question and figuring out how to answer it.
Here are two great examples:
Challenge: Think of a problem or topic you're interested in and answer it with data!
(2) Read news with a skeptical eye
Much of the contribution of a data scientist (and why it's really hard to replace a data scientist with a machine), is that a data scientist will tell you what's important and what's spurious. This persistent skepticism is healthy in all sciences, and is especially necessarily in a fast-paced environment where it's too easy to let a spurious result be misinterpreted.
You can adopt this mindset yourself by reading news with a critical eye. Many news articles have inherently flawed main premises. Try these two articles. Sample answers are available in the comments.
Challenge: Do this every day when you encounter a news article. Comment on the article and point out the flaws.
(3) See data as a tool to improve consumer products
Visit a consumer internet product (probably that you know doesn't do extensive A/B testing already), and then think about their main funnel. Do they have a checkout funnel? Do they have a signup funnel? Do they have a virility mechanism? Do they have an engagement funnel?
Go through the funnel multiple times and hypothesize about different ways it could do better to increase a core metric (conversion rate, shares, signups, etc.). Design an experiment to verify if your suggested change can actually change the core metric.
Challenge: Share it with the feedback email for the consumer internet site!
(4) Think like a Bayesian
To think like a Bayesian, avoid the. This means to form new beliefs you must incorporate both newly observed information AND prior information formed through intuition and experience.
Checking your dashboard, user engagement numbers are significantly down today. Which of the following is most likely?
1. Users are suddenly less engaged
2. Feature of site broke
3. Logging feature broke
Even though explanation #1 completely explains the drop, #2 and #3 should be more likely because they have a much higher prior probability.
You're in senior management at Tesla, and five of Tesla's Model S's have caught fire in the last five months. Which is more likely?
1. Manufacturing quality has decreased and Teslas should now be deemed unsafe.
2. Safety has not changed and fires in Tesla Model S's are still much rarer than their counterparts in gasoline cars.
While #1 is an easy explanation (and great for media coverage), your prior should be strong on #2 because of your regular quality testing. However, you should still be seeking information that can update your beliefs on #1 versus #2 (and still find ways to improve safety). Question for thought: what information should you seek?
Challenge: Identify the last time you committed the Base Rate Fallacy. Avoid committing the fallacy from now on.
(5) Know the limitations of your tools
“Knowledge is knowing that a tomato is a fruit, wisdom is not putting it in a fruit salad.” – Miles Kington
Knowledge is knowing how to perform a ordinary linear regression, wisdom is realizing how rare it applies cleanly in practice.
Knowledge is knowing five different variations of K-means clustering, wisdom is realizing how rarely actual data can be cleanly clustered, and how poorly K-means clustering can work with too many features.
Knowledge is knowing a vast range of sophisticated techniques, but wisdom is being able to choose the one that will provide the most amount of impact for the company in a reasonable amount of time.
You may develop a vast range of tools while you go through your Coursera or EdX courses, but your toolbox is not useful until you know which tools to use.
Challenge: Apply several tools to a real dataset and discover the tradeoffs and limitations of each tools. Which tools worked best, and can you figure out why?
(6) Teach a complicated concept
How does Richard Feynman distinguish which concepts he understands and which concepts he doesn't?
Feynman was a truly great teacher. He prided himself on being able to devise ways to explain even the most profound ideas to beginning students. Once, I said to him, "Dick, explain to me, so that I can understand it, why spin one-half particles obey Fermi-Dirac statistics." Sizing up his audience perfectly, Feynman said, "I'll prepare a freshman lecture on it." But he came back a few days later to say, "I couldn't do it. I couldn't reduce it to the freshman level. That means we don't really understand it." – David L. Goodstein, Feynman's Lost Lecture: The Motion of Planets Around the Sun
What distinguished Richard Feynman was his ability to distill complex concepts into comprehendible ideas. Similarly, what distinguishes top data scientists is their ability to cogently share their ideas and explain their analyses.
Check outfor examples of cogently-explained technical concepts.
Challenge: Teach a technical concept to a friend or on a public forum, like Quora or YouTube.
(7) Convince others about what's important
Perhaps even more important than a data scientist's ability to explain their analysis is their ability to communicate the value and potential impact of the actionable insights.
Certain tasks of data science will be commoditized as data science tools become better and better. New tools will make obsolete certain tasks such as writing dashboards, unnecessary data wrangling, and even specific kinds of predictive modeling.
However, the need for a data scientist to extract out and communicate what's important will never be made obsolete. With increasing amounts of data and potential insights, companies will always need data scientists (or people in data science-like roles), to triage all that can be done and prioritize tasks based on impact.
The data scientist's role in the company is the serve as the ambassador between the data and the company. The success of a data scientist is measured by how well he/she can tell a story and make an impact. Every other skill is amplified by this ability.
Challenge: Tell a story with statistics. Communicate the important findings in a dataset. Make a convincing presentation that your audience cares about.
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