Everyone knows that there is a need for math for data science. But which part of math topics should one be aware of? And where to learn math for data science. Let’s find out.
Every science field needs mathematics as they are the fundamentals of any science. All the trending technologies such as data science, machine learning, AI, and deep learning need mathematics.
There are two prerequisites to become a data scientist. One is to learn a programming language, and the other is to acquire knowledge in math for data science.
To become a successful data scientist, one must have qualify both prerequisites. Though there are several easiest programming languages to learn for data science, the math for data science remains constant.
We recommend learning Python to be suitable for learning data science.
Let’s look at the math topics for data science.
3+ Maths Topic for Data Science
You might have read somewhere to heard someone saying that one must learn statistics and mathematics for data science. You understood statistics but which part of mathematics should you know.
I’ve listed the below other required math topics for data science.
If you’re a beginner in data science, these math topics are essential for data science.
Below are the four essential math topics for data science:
- Discrete Maths
- Linear Algebra
- Probability and Statistics
If you already know these math topics and are familiar in it. Then you already meet one of the prerequisites. You can go ahead to start learning one of the programming languages for data science.
If your study or understanding of the math topic for data science remains vague, then I have one piece of advice for you.
You need a new point of view.
You don’t need to revisit your college books to learn math for data science though this can be a daunting experience from flipping pages to solving problems.
There is no need for it. You’re already through it. Hence I am emphasizing a new point of view.
Where to learn the essential math for data science?
You can sign up here to learn the bundle course of 4 from Coursera to learn the essential math for data science.
If you already an expert in one of the topics, then you can go for the individual course that was mentioned above.
Below we visit each topic and find out where to learn essential math for data science.
1. Discrete Maths
Discrete Maths is essential math for data science, but no one talks about it. Basic knowledge of discrete maths is necessary for data science.
Anyone working in Data Analysis will know the importance of discrete maths. Hence, you have a head start to learn it before you realize it.
In discrete maths, you are introduced to combinatorics topic. It is a branch of mathematics that studies how to count. Basics of this topic are critical for anyone interested in making a successful career in data science.
Once you get the hang of combinatorics, you will apply this knowledge to study probability theory. Probability is everywhere in data analysis.
Here you get to learn the tip of probability.
Where to learn Discrete Maths for data science? Sign up here.
Calculus appears in both data science and machine learning. The is the fundamental blocks of data science, and ML is calculus.
There’s two types of calculus Differential Calculus and Integral Calculus.
Differential Calculus is cutting something into small pieces. And Integral Calculus joins the little pieces together.
Calculus is used in almost every model of data science. Hence learning Calculus is essential math for data science.
Where to learn calculus for data science? Sign up here.
3. Linear Algebra
Linear Algebra is the branch of mathematics about linear equations, linear functions, and their representations through matrices and vector spaces. It is the definition taken straight from Wikipedia.
In simple words, linear algebra helps us to understand geometric terms like dimensions and planes to perform mathematical operations.
While algebra deals with the one-dimensional entity, linear algebra deals with more than two-dimensional entity.
Like calculus, the concepts of linear algebra is also essential in machine learning and data analysis. Don’t worry if this is getting complicated to understand.
Coursera has designed this course in detail where we get to learn everything from the basics.
You will learn to work with data in both vector and matrix form.
Where to learn linear algebra for data science? Sign up here.
4. Probability and Statistics
For an in-depth understanding of the data science, certain knowledge is required in probability and statistics.
To process the complex problems of the real world, data scientist and data analysts use statistics. In other words, it can be used to get meaningful insight from the data.
Probability is the chance that something will occur and calculates how likely it is for the event to occur.
We use probability every day without even knowing it, and our brain is so complex that it does millions of operations in a second.
A data scientist uses statistical inferences to analyse and predict trending data. And statistical inferences uses probability distributions of data.
Don’t worry if its getting complex to understand. You can revisit this concept by taking the course and learn from the ground level.
Where to learn probability and statistics for data science? Sign up here to learn statistics and probability.
Now that you’ve gone through each math topic be sure to revisit or learn by taking the course.
A fresh point of view on any topic is always recommended since it throws a light on concepts from a different angle. It helps you understand in a way that you never forget.
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