7 Steps to Start Thinking Like a Data Scientist

Having the skills needed to perform data science work is immensely beneficial in a wide range of industries and job functions...

Having the skills needed to perform data science work is immensely beneficial in a wide range of industries and job functions. But at some point, it is also advantageous to develop a thought process that allows you to tackle problems like a data scientist. Here are 7 steps you can take to start thinking like one.

1. Understand How the Project Lifecycle Works

Every project needs to be guided through a lifecycle that goes from preparation to building and then on to finishing it. Preparation means setting goals, exploring the available data, and assessing how you’ll do the job. Building requires planning, analyzing problems, optimizing your approach, and then building viable code. Finally, finishing requires you to perform revisions, deliver the project, and wrap up loose ends. The lifecycle installs rails around the project to ensure it doesn’t suffer from mission creep.

2. Know How Time Factors into Cost-Benefit Analysis

Scraping the web for all the data you need may prove to be time-consuming, especially if the data needs to be aggressively cleaned up. On the other hand, purchasing data from a vendor can be expensive in terms of capital. There’s rarely a perfect balance between time and money so try to be receptive to which is more important on a particular project.

3. Know Why You’ve Chosen a Specific Programming Language

All programming languages have their unique strengths and weaknesses. For example, MATLAB is a very powerful language, but it often comes with licensing issues. Java handles work with a high level of precision, but it can be cumbersome. R is an excellent choice for people who need core math functions, but it can be limiting when it comes to more advanced functionality. It is essential to think about how your choice of a programming language will influence the outcome of your project.

4. Learn How to Think Outside of Your Segment of Data Science

 It’s easy to get caught in the trap of thinking certain processes are somehow more academically valid than ones aimed at the consumer market or vice versa. While something like A/B testing can feel very simple and grounded in the consumer sector, it may have applications to projects that are seemingly more technically advanced. Be open-minded in digesting information from sectors that are different from your own.

5. Appreciate Why Convincing Others is Important

Another common trap in data science is to just stay in your lane. Being a zealous advocate for your projects can make a difference in terms of getting approval and resources for them.

Develop relationships that encourage the two-way transmission of ideas and arguments. If you’re in a leadership position at a company, foster conversations with individuals who are closer to where the data gets fed into the meat grinder of analysis. Likewise, those down the ladder should be confident in presenting their ideas to people further up the chain. A good project deserves a representative who’ll advocate for it.

6. Demand Clean Data at Every Stage of a Project

Especially when there’s pressure to deliver work products, cleaning up data can sometimes feel like a secondary concern. Oftentimes, data scientists get their inputs and outputs cleaned up to a condition of “good enough” to avoid additional mundane cleaning tasks.

Data sets rarely just go away when a job is done, and that’s simply good practice for the sake of retention, auditing, and reuse. But, that also means someone else may get stuck swimming through a data swamp when they were expecting a data lake. Leave every bit of data you encounter looking cleaner than you found it.

7. Know When to Apply Critical Thinking

Data science should never be a machine that continually goes through the motions and automatically spits out results. A slew of problems can emerge when a project is too results-oriented without an eye toward critical thinking. You should always be thinking about issues like:

  • Overfitting
  • Correlation vs. causation
  • Bayesian inference
  • Getting fooled by noise
  • Independent replication of results

Welcome criticism and be prepared to ask others to show how they’ve applied critical thinking to their efforts. Doing so could very well save a project from a massive misstep.

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