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Data science is the future, everyone wants to learn this budding technology. Is everyone able to learn? The answer is “No”. Do you know the reason, it is none other than “*mathematics*”. What….did I say *mathematics*? Yes, Mathematics or simply math. While reading this, people who know what is data science or has worked in this field would be confused and would be asking how maths is responsible. Let me, rephrase my answer, “ **Fear to Mathematics**”.

Starting from our elementary education to our higher education we see students scared of mathematics or we can say students have a math phobia. Not sure who has created this buzz that data science requires a long list of math topics as a prerequisite.

It is not completely correct, elementary math is required but, as a beginner, you don’t need that much math for data science. Also, there is another side to data science and that is the practical side. For practical data science, a great deal of math is not required. Practical data science only requires skills to select the right tools. Being said that let’s understand how theoretical and practical data science differs.

**Difference between Theory and Practice**

When we talk about data science, it is important to set our goal. What we want to achieve from learning data science? It is for academic learning or for practical purpose to build career on.

Why this goal setting is important because priorities and deliverables are different in both theoretical and practical data science. Learning data science for academic purposes is more for publishing research papers and push the field forward. While practical use of data science is to generate reports, build models and system software.

**Skills required for foundational Data science**

As a beginner in data science, one will primarily work on foundational/fundamental skills of data science. These skills are required in each and every data science project.

What are these skills?

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- Data Visualization
- Data analysis, also know as EDA (Exploratory Data Analysis)

It is a known fact that in any data science project 75% of effort and time is spent doing these fundamental steps. These are the core skills required for success of any data science project. If any of the above step is missed out or is not carried out with utmost precision, final model might not be as good as it should be.

Coming back to the question, how much math is required for these core skills? – Very little.

So, by now you must be pretty much clear and convinced that math required for data science is not scary at all.

**What concepts of math are required for these foundation skills of data science?**

You would be wondering that still some math is required , so what all topics are there.

Let me break the good news to you, you just require lower-level

algebra and simple statistics.Don’t be astonished, it is a fact. Feeling delighted !!

Let me explain it in a bit more detail.

- For getting the data, cleaning it and understanding it, doesn’t require any math. If new variable creation is required by deriving it’s value from already present variable. Then , it requires elementary math of addition, subtraction, multiplication and division. It may be required to manipulate the data by calculating mean, median or mode. It could be seen that none of the calculations here require complex maths.

For 95% of cases above method holds true but exceptions will always be there. Very rare cases would require complex computation. Again it is very **rare.**

- One major tasks in data science is data visualization. In this step graphs and plots are created to check patterns in data. This is like a subset of exploratory data analysis step. So, where math is required in this? One should be aware of which plot to create and which tool to use. So, problem solved, no math is required in this step as well.

One should know how to read plots and graphs whether it be scatter plot, histogram, line graph, point plot, etc. This is the only demand from this step.

- By now 75% work of any data science project is done. Now comes model creation using machine learning. Here again concept of theory and practical applicability of machine learning comes into picture. We have to create models for our business purpose and not to dig deep in model theoretically and publish a research paper. Conceptual understanding of machine learning models again don’t require math.

One should learn which model to used in which situation. How to interpret the model. How to check assumptions in model. How to make predictions from model. Have I mentioned math anywhere, no. So, in short no advance math is require to become a data scientist.

**Basic skills required to become a foundational data scientist**

- Selection of right tool
- Understanding the syntax
- Basic graphs and charts
- Conceptual understanding of different machine learning models
- Basic Algebra
- Basic Statistics

This is mostly all you need to get started learning data science. Motivation and will to learn is utmost important.

But always remember saying by Galileo Galilei:

“

Mathematicsis the language with which God has written the universe.”

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Netali Agrawal is a part of the AIM Writers Programme. She is a Business Analyst who loves to explore new ideas in different industries through machine learning and artificial intelligence. She holds a bachelors degree in engineering along with post-graduation certification in business analytics and business intelligence. She is working with an MNC as a business analyst and leading a project for machine learning and artificial intelligence. Netali loves to write about analytics, machine learning and artificial intelligence. She loves to explore data and mould it in the best possible shape to get all possible insights from the data. She resides in Hyderabad, India. Linkedin Bio: www.linkedin.com/in/netali-agrawal-31192a71