You don’t need to know much math for data science says Josh Ebner of Sharp Sight Labs.

Truth be told, practical data science doesn’t require you to learn too much math. However, if you’re stressing to learn it to begin your journey in data science then perhaps studying certain topics in math would suffice.

To bring into practice, you just need to master certain skills to use a particular tool or technology. Therefore, you don’t need to get in-depth with the subject. Not to mention, it is also important to understand the difference between theory and practical applications underpinning data science. This is where the difference lies.

Learning math as a beginner could be a challenge. However, we can still follow the below guide to begin the data science career.

### 1. Explore a new approach toward learning math

Most people in academics already have a knack for their subjects. Such people love to binge-practice probability while binge-watching shows for weeks together. But what about those who are still new to the field?

It is tough to jump from one topic to another when you’re unable to understand the basic fundamentals. Well, this is where you’re doing it all wrong. Instead of jumping from one topic to another, it is advisable that the candidate try to focus on one topic at a time. If needed, use more than one resource to understand the concept. You need to repeatedly do this until you completely understand the core concept.

Well, math can only be understood if you practice what you learn. Therefore, start solving problems. Apply what you learn to solve mathematical sums and equations.

### 2. Binge-study/Binge-practice

There’s no better way to ace mathematics unless you binge-practicing mathematical concepts. So, start doing it.

• Below are major mathematical concepts for you to get started with:

• Bayes’ Theorem – Math is fun

• Permutations and combinations – Eddie Woo

• Probability density function – Michael

• Cumulative Distribution Function – Michael

• Discrete random variable – Eddie Woo

• Discrete probability distributions – Jason Gibson

• Conditional probability, Bayes’ Theorem – Investopedia

### 3. Statistics and Linear Regression

You can binge-watch a video by Josh Starmer and start learning statistics fundamentals. Josh has an amazing technique of explaining the concepts in an easy manner. He does not waste time and gets straight to the point. And he is able to explain every detail without needing to code. Josh is all about clarity and fundamentals. Aspiring data science professionals might find these topics relevant when looking to select what to learn first in mathematics for data science.

### 4. Linear Algebra

Another topic of interest in data science is Linear Algebra. Instead of going the traditional way, it is better to start picking a book with this topic and start spending months together on it. Perhaps, not every day, but once a week will suffice. Below are a few places you can start with today:

• Linear algebra – Dive into Deep Learning

• Linear algebra – Ritchie Ng

• Linear algebra – Deep Learning Book

• Linear algebra – Pablo Caceres

### How can you fight the fear of math?

The word ‘math’ itself instills fear in most learners. Maybe this is one of the reasons why most of them fail even before trying.

At times, it is the fear that does not let us understand topics we’re willing to learn because we all feel we don’t the brains for it. Perhaps, we’re wrong. There’s a huge difference between becoming a genius in mathematics and using math as a tool to solve complex problems in data science. Well, the former is a gift while the latter is totally a skill-set.

Not all of us are Harvard graduates and neither was born a genius in mathematics. So, the best strategy to follow is to start believing in ourselves and start working toward our goal.

### Videos from The Math Sorcerer – List A

• Why Do Some People Learn Math So Fast

• 6 Little Known Reasons

• Three Tips For Learning Math on Your Own

• How to Overcome Failure in Math

### List B

• Anyone Can Be a Math Person – Po-Shen Loh

• What does it take to learn math? To live life? – Miroslav Lovric

• How you can be good at math, and other surprising facts about learning – Jo Boaler

• The interesting story of our educational system – Adhitya Iyer

### List C

The last thing for you to do is to pick your favorite math topic and start reading it. Start practicing and work through the sums and exercises. Starting a data science career is indeed not a cakewalk without knowing what topics to pick in mathematics. Perhaps the given information will help you get started in the field. Doing so will end half of the fear you have in math.

### Don’t forget what you’ve learned

The best way to do this is by reading, watching, and starting to solve problems on a daily basis. However, as a data scientist, it is not always possible to use everything you’ve learned. This is why you need to use the Feynman technique next.

### Some of the best advantages of using the Feynman technique includes:

• Saves a lot of time

• You need to learn only what you need to

• Your focus remains on the real-work

• Explaining practical application gets easier

Hopefully, the above steps can help you combat the fear you have toward learning mathematics.