How To Become a Data Scientist Without Knowledge of Statistics

The data science boom has attracted a lot of likely and unlikely aspirants. As a data science upskiller, GreyAtom receives a lot of inquiries for becoming a data scientist. A large chunk of these inquiries is made by folks with relatively scant background in engineering/technical sciences. The bottom line question is the same: How to switch to career in data science?

This article will describe some quick hacks and pointers for folks who want to transition into data science, but lack sufficient knowledge of statistics.

Our database of over 5000 aspirants shows that most candidates from a non-technical background lack knowledge of statistics. What our academic counsellors look for is the willingness to build sufficient groundwork in statistics in order to successfully switch to data science.

We have a rigorous, data-backed screening process in place that evaluates willingness to learn and aptitude amongst others. We have noticed that those who are ready to learn foundational subjects like statistics are likelier to succeed in their overall career transformation than those who are not. Owing to this, we are explicit to our aspirants about setting expectations.

A. Get Started

It’s time to pull up high school statistics lessons and review them. Most people learn statistics formally only up to high school. It’s expected that your knowledge of statistics will wear away over time as you pick up a different career trajectory. 

Starting all over again and building those eroded basics should be your first step. You may resort to either textbooks or free Youtube videos that are more interactive and improve retention.

Alternatively, if you need something more structured, you may resort to paid online courses (MOOCs).

B. Learn the Essentials

  • Ground Work

Brush up on every single element of statistics that is covered in high school – grades 11 and 12. If you are a visual learner, check out this Khan’s Academy playlist on Youtube for extremely effective content. An alternate option to gain in-depth understanding of the advanced areas is by subscribing a more advanced learning Youtube channel – Mathematical Monk.

  • 3Ps

The key to coming up to speed is patience, perseverance, and persistence. You won’t retain much of the lessons on your first go. But going over the lessons multiple times periodically will certainly improve retention and understanding. 

  • Practice leads to Perfection

We also realized that working on statistical problems helps in understanding and retention. There are several free repositories of problems in statistics that one can get hold of using Google search. We found this resource really helpful.

  • Grand Goal

We don’t encourage people to learn in a rote manner. While it’s important to know the methods and tools in statistics, it’s even more important to know how and where they fit in the bigger picture of applications.

C. Skill-Up

  1. Analytical skills

    Data science involves finding trends and insights in large volumes of data. This is a blend of hard skills and art. Hard skills help you process and present the data and the artistic element comes in when you are trying to fish for relevant patterns.

  2. Communication Skills 

    A data scientist needs to work on his communication skills to articulate his findings and conclusions. Chances are that he will be interfacing with stakeholders and CXOs, who might not necessarily have a hard science background. He will need to communicate in a manner that is comprehensible and cogent.  

  3. Troubleshooting 

    Often the data set will contain missing values and/or proxy values. This will have a definitive effect on the model and the conclusions. The data scientist will need to trouble shoot the model accordingly and make the necessary adjustments.

  4. Math Skills 

    While you are not required to perform mental math, you certainly are required to demonstrate your A game at math.

So there you have it, some quick hacks to work yourself into data science without an extensive statistics background. You don’t have to be a genius to become a data scientist, but you certainly have to be persistent, patient, and correctly mentored to get your foot in the door.

To read outstanding success stories of how people with no background in hard sciences pivoted to data sciences, watch this short video.

To know more about what we offer at GreyAtom, click here.

 

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