8 Skills You Need to Become a Data Scientist

2 min read


Data ScientistNumbers do not scare you? There is nothing more satisfying than a beautiful excel sheet? You speak several languages, but only in code?

Then it might be time for you to explore how to become a data scientist.

Let’s dive into what a data scientist is doing, and the 8 skills they need to master!

What is a data scientist doing?

In 2012 HBR named data scientist the “sexiest job of the century”. Rightfully so, this quickly developing field has helped people to make sense of the incredible amounts of data we can now gather, making both people and machines smarter.

A data scientist is the one who dives into the facts and helps companies make better decisions – from selecting the email marketing template to use, to which part of the UX needs improvement. Executives, designers and product managers turn to data scientists to draw relevant conclusions from vast amounts of information.

Data scientists are the MVPs behind the scenes of Netflix, Spotify, and Uber. They are the ones who whisper in the form of sophisticated algorithms to the computer. Data Scientists use Machine Learning and Artificial Intelligence to automate and optimize processes.

How to become a data scientist?

Here are the top 8  essential skills you need!

1. Programming

Whether it is R, Python, SQL, Apache Spark, or Hadoop you will need to know the tools of the trade. R and Python can help you with statistical programming, SQL is for database query, Apache Spark and Hadoop are for programming big data computer frameworks.

2. Statistics

Statistics are crucial to understanding data and helping decision making. You need to dive deep with correlations, distribution, maximum likelihood estimators and so much more. Understanding statistics will help you decide what approach to adopt to data interpretation and the relevant indicators you will observe.

3. Math

Well, there is just no going around this. Math needs to be a second language to the data scientist, with special regards to multivariable calculus and linear algebra. Understanding these mathematical concepts helps greatly with writing successful algorithms.

Data Scientist4. Data Cleaning


As much as we all like nicely structured clean data, it is often not a possibility. It’s often said that data scientists spend 80% of their time cleaning their data and only 20% analyzing it. A data scientist needs to be able to make sense of the imperfections in the data. Whether this is missing information or inconsistent string formatting – you need to be able to spot and correct inaccuracies. 


5. Data Visualization

A picture says more than a thousand words: this is true with data too. Being able to visualize information will help you to present your findings in an engaging and understandable way. Make sure to put visualization tools at matplotlib, ggplot or Tableau on your data science bucket list.

6. Communication

A good old classic of communication. A data Scientist needs to be not only fluent in speaking to computers, but to people as well. You will work in teams, and often close to people who are from another business unit. As a data scientist, you will need to clearly express what you need from others, as well as explain your findings to non-techies.

7. Problem-solving

As a data scientist, you need to have a data-driven mindset, be able to select relevant information and ask the right questions. Sometimes insights do not reveal themselves instantly, and you will need to find your way through the puzzle.

8. Eagerness to learn

Data science is a growing field, with new technologies, AI, IOT and machine learning improving fast. You need to be eager to learn, and willing to constantly grow to be successful in this career in the long run.

Want to dive straight into your career and rock the world as a data scientist but unsure where to start? Here is a short guide to help you get started!


Bingran Lu With an academic and professional background in the field of Finance and IT, Bingran has a deep appreciation for data-driven decision making. He is a fan of all things data related. Currently building his dream tool to help everyday people make smart investment decisions.

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