The Venn Diagram of Data Science – Drew Conway​

Do you always get confused about which skills to master while learning data science? Drew Conway’s venn diagram of data science will clear your confusion.

This blog covers the Drew Conway Venn diagram, how to read the Venn diagram and the important skills mentioned in the Venn diagram in detail. Along with the skills, to make your data science journey easier, we will provide you with the necessary resources to start and master your skills. Let’s get started!

What is Data Science?

Before getting started with Venn diagrams, we will try to understand what data science is. By the name itself, we can say that it is a science about data. Data science is the study of data to extract meaningful information and insights. Data science is used to provide a data-driven solution to real-world problems. It includes maths, statistics, machine learning(ML), and artificial intelligence(AI).

What is a Venn diagram?

A Venn diagram is a graphical representation of one or more elements, including the similarities and differences between them. Each element is usually represented in a circle, and the overlapping between the circles is the similarity among the elements. If an element is drawn separately without any overlapping, then it does not have any similarities with other elements.

Venn Diagram of Data Science – Drew Conway

Drew Conway’s Venn diagram of data science gives a brief overview of the data science curriculum. Josh Wills, data scientist and engineer, says that,

A Data Scientist is a person who is better at statistics than any software engineer and better at software engineering than any statistician.

According to Drew Conway’s Venn diagram of data science, you need to know three main areas:

• Mathematics and Statistics
• Hacking Skills
• Substantive Expertise
• Hacking Skills + Mathematics and Statistics + Substantial Expertise = Data Scientist
• Hacking Skills + Mathematics and Statistics = Machine Learning Engineer
• Mathematics and Statistics + Substantial Expertise = Researcher
• Hacking Skills + Substantial Expertise = Danger Zone

How to Read Drew Conway Diagram

In this section, let’s understand the Drew Conway Venn diagram of data science and the skills involved in. As a data scientist,

Hacking Skills

You need to have hacker skills in this competitive environment. It does not mean to have excellent knowledge on hacking others’ systems or protecting from being hacked. It refers to having knowledge of command line interfaces(CLI) and manipulating text files.

Mathematics and Statistics

Mathematics and statistics skills help in handling, cleaning, and manipulating data. The first thing you should do after collecting data is to clean and preprocess the data according to the algorithm and problem statement. It is done using various mathematical and statistical techniques such as linear algebra, chi-square test, and so on.

Machine learning

According to Drew Conway, hacking skills + Mathematics and Statistics will lead you to machine learning. If you are aiming to become a machine learning engineer, then it is good. If you want to become a data scientist, then you should have substantial expertise where experimenting with different algorithms for a single problem will help you in achieving more accurate results.

Research Scientist

If your aim is to become a research scientist, then you can learn Mathematics and Statistics + Substantial Expertise. This will help you in continuing your experiment with different algorithms and techniques so that you can contribute to the data science community.

Danger Zone

From the Venn diagram of data science, we can see that Hacking Skills + Substantial Expertise will lead to a danger zone. It’s because mathematics and statistics are essential skills for data science. Without any proper knowledge of mathematics and statistics, you will neither become a data scientist nor a machine learning engineer. 

Data Scientist

And at last, learning Hacking Skills + Mathematics, Statistics, + Substantial Expertise will lead you to become a Data Scientist. According to your expertise, you can start from any skill. Our advice for you would be to start from hacking skills, master mathematics and statistics, and experiment with different techniques.

Getting Started with Guvi

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Conclusion

Drew Conway’s Venn diagram of data science explains the skills and expertise you need to become a data scientist. It also mentions the cons of missing a skill and what it leads to. To summarize, hacking skills, plus mathematics and statistics, plus substantial expertise, will lead you to become a successful data scientist. While leaving any one of the skills leads you to either a successful machine learning or a danger zone. By understanding the importance of these skills and your interest, choose the skill wisely. Happy Learning!

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