AI News, William Chen

William Chen

Here are some amazing and completely free resources online that you can use to teach yourself data science.

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.

Attend an interesting talk, learn about data science live, and meet data scientists and other aspirational data scientists.

Complete Harvard's Data Science Course As of Fall 2015, the course is currently in its third year and strives to be as applicable and helpful as possible for students who are interested in becoming data scientists.

I'd recommend doing the labs and lectures from 2015 and the homeworks from 2013 (2015 homeworks are not available to the public, and the 2014 homeworks are written under a different instructor than the original instructors).

Next, play around some more and check out the tutorials for Titanic: Machine Learning from Disaster for a binary classification task (with categorical variables, missing values, etc.) Afterwards, try some multi-class classification with Forest Cover Type Prediction.

Think like a Data Scientist In addition to the concrete steps I listed above to develop the skill set 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.

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.

(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.

(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.

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.).

This means to form new beliefs you must incorporate both newly observed information AND prior information formed through intuition and experience.

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.

(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.

(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.

New tools will make obsolete certain tasks such as writing dashboards, unnecessary data wrangling, and even specific kinds of predictive modeling.

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.

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