How to think like a Data Scientist (Secret to a data-driven mindset)
7 Steps to master your thoughts to become a data-driven person.
Photo by Mika Brandt on UnsplashIf you have read my previous article “Becoming a Data Scientist (Zero to Hero)”, Consider this as the second part of it. (Click below to read it in case if you have missed)
Becoming a Data Scientist (Zero to hero checklist)
10 Steps for people who want to pursue a career in Data Science on Jayasekara.blog by Dilan jayasekara
In addition to the concrete steps I listed in my first article 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.
1. Satisfy your curiosity through data
Photo by Michael Aleo on Unsplash
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.
Here are two great examples:
Challenge: Think of a problem or topic you’re interested in and answer it with data!
For instance, I was using Google BigQuery for a data warehousing project a few months back and I was curious about the process of whole data migration thing and I researched for days and finally wrote this article:
What happens to Data on the fly when migrating from SQL to BigQuery using StitchData
If you happen to come across my previous article about creating a data warehouse in BigQuery, you came to know about..jayasekara.blog by Dilan Jayasekara…
2. Read news with a suspicious eye
Photo by Paul M on Unsplash
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 necessary 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.
Easier: You Love Your iPhone. Literally.
Harder: Who predicted Russia’s military intervention?
Challenge: Do this every day when you encounter a news article. Comment on the article and point out the flaws.
Here is one time I got suspicious about Google’s Nested Data Structure and Columnar format in Google Big Query and what I have found regarding it:
How to create Tables inside Tables using Nested Data Structure and Columnar Format in BigQuery
In this article, I’ll guide you through the steps of creating a table inside table using Columnar Storage feature with…jayasekara.blog by Dilan Jayasekara
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!
For instance, I was looking for a way to create a Dashboard using python to show the annual sales change between teo periods and the trends of products (Positive/ Negative) So I came with the idea of combining both R,R Shiny and Python to create a dashboard (With some cool graphs with the use of plotly package), If you’re interested click on the link below and read it
Creating interactive dashboards in R Shiny using Python scripts as the backend.
Why is it always R v Python? Why can’t we admit that both are unique in their own way and we should know how to handle…jayasekara.blog by Dilan Jayasekara
4. Think like a Bayesian
Photo by Sharon McCutcheon on Unsplash
To think like a Bayesian, avoid the Base rate fallacy. 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
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.
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
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