Important Math Topics and Skills Needed for Data Science
Do you want to be a data scientist?
If you came here and reading this article, YES!
I have shared all the skills required to become a data scientist.
Mathematics is one of them, very important and vital. Without mathematics, you can not be a data scientist.
I’m not good at mathematics; can I become a data scientist?
This is the most common question asked by every alternate Data Science aspirants.
In this article, I’m sharing math needed for data science along with my own experience and learning.
Going through this complete article, you will also learn how you should not be afraid of mathematics and why mathematics should not hold you back to become future Data Scientist.
Let’s deep dive.
Why mathematics is important in Data Science?
Machines understand binaries o’s and 1’s. It deals with numbers very well than any other data format.
Moving to machine learning, it is all about math.
Almost all the data you used for data analytics will be in numeric format. To parse, to filter, to analyze and to find the pattern you have to dwell into numerous data numerical features.
Math is Important!
If I’m don’t do the math, I’m not a Data Scientist.
If you are an aspiring data scientist, Now you might have lots of questions in your mind.
How much math knowledge Data Scientist should know?
You don’t require a masters or bachelors degree in mathematics.
I have mastered in Computer Science.
To bring your interest in mathematics, it would be great if you have basics knowledge of mathematics.
If you are a mathematics student in your college and have a good understanding and interests in math topics, things are not difficult for you.
To learn mathematics, two things are required.
- Knowing the importance of mathematics
- Interest in learning mathematics.
Without knowing the importance, you cannot bring interest.
If you hold these two things, you are the Right Person to start learning Data Science.
Import Skills and Topics from Math needed for Data Science
What are the essentials that I need to get myself into Data Science and Machine Learning?
Here are my secret sauce and important mathematic skills you required to become a data scientist.
Generally, mathematics falls into two major areas- linear algebra and geometry.
Forget about geometry, for data science you have to deal with linear algebra.
Linear Algebra is one of the most important topics from the math you need to learn.
For every data manipulation work, you need a data structure to organize your data and arithmetic operation to analyze your data.
- Sets, Vectors, Matrices, Arrays are important data structures to organize your data.
- Arithmetic Operations you perform on row data is called Data Wrangling. It is also called as Data Munging.
For analyzing the data, you have to perform many statistical and probability operations.
Some important topics form Statistics and Probability:
- Descriptive Stats
- Inferential Stats
- Hypothesis Testing
- Different Statistical Tests
(Chi-square/ t-test/ Z test/ ANOVA/Regression/Sampling/BootStapping/Bagging/Cross Validation)
For machine learning, along with the above topics, you have to explore some of the major math topics like linear and nonlinear algebra, calculus and limits.
Calculus is an advanced topic in mathematics.
Important Tips for Data Science Aspirants
- If you are new to these mathematics topics, don’t hold yourself back. Rather than start learning data science with whatever mathematics knowledge you have. While going through the data science project, explore mathematics skills in parallel.
- There are many Python libraries in data science. You can use those libraries for performing mathematical operations, rather than dealing with mathematics with bare hands.
- Data science is highly dynamic and changing every day, so the mathematics. You have to keep eyes on everything going on.
- To become an expert data scientist, you need to be good in these mathematical topics and skills for sure. The more you know mathematics, the better you become in your data science job.
Do you have any doubt or thoughts to share? Write in the comment.
Keep learning Data Science!