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Many people wonder if they need to be math experts before entering data science. This is slightly true. 

Since data science is all about numbers, if you're going to be a data scientist, you must know how to use mathematics and its formulas for performing analysis. 

Yes, Math can seem scary at first, but it's actually fun when you see how it works. However, knowledge of Maths is essential to data science. At the end of the day, it's all about taking lots and lots of sample data, performing some operations on it, and making inferences from it. 

In this blog, I will help you understand what math you need for data science in general and which ones will help you most specifically when working with machine learning and gray matter calculations.

Let's head to the basics first. 

Data Science - Overview 

Data science is a branch of computer science dealing with the analysis and manipulation of data. It refers to studying data collection, analysis, and interpretation from different resources. It is utilized in many industries, including healthcare, finance, retail, marketing, etc. Also, there are many data science course online, which can help you in acquiring the knowledge. 

To know if your programming skills are up to scratch, you must understand the mathematics behind each algorithm you use. As an expert data scientist, you must be adept at applying mathematics. And when it comes to math, the trick is not just being able to work out formulas; more importantly, you'll also need to understand how formulas can help answer your questions.

Why is Math essential to learn for data science?

Please note that if it is possible to learn how to program without understanding the deeper principles behind computer programming, it is also possible to learn data science without understanding mathematical concepts. However, in both cases understanding these concepts will help you become better at your job and allow you to solve more complex problems. Let's discuss a few cases to know why math is essential for data science and machine learning.

• First and foremost, since data is all about numbers, it's obvious that math will play a key role. 

• Math helps understand the distinction between the algorithms and which tool can be suitable for that particular case. 

• It helps ensure the insights of the analysis are accurate.

• Diagnose the problem and debug models that are not convergent.

• It assists in making most of the ML libraries like Sklearn.

• You can create and customize the prediction and cost functions to fit the algorithm to the problem you're trying to solve. Want to master math concepts to solve ML problems? Head to the IBM-accredited data science courses today. Expert faculty offers this training course, helping you gain hands-on experience. 

Essential Math for data science 

Now, here's a quick rundown of what essential mathematics you need to know:

1. Linear algebra

Linear algebra is the foundation of computer science and data science. In fact, several machine learning concepts are tied to linear algebra. It lets you add, subtract, multiply and divide numbers in your head without a calculator. It also lets you solve equations like 2x + 3 = 7 using basic rules like "the sum equals the difference between the two sides."

Some of the primary topics of linear algebra are: 

• The basic properties of matrices and vectors include scalar multiplication, linear transformation, transpose, and determinant. 

• Advanced matrices include square matrix, Identity matrix, Symmetric matrix, sparse and dense matrix, 

• Eigenvalues, eigenvectors, diagonalization, singular value decomposition (SVD)

• Matrix factorization concepts, LU decomposition, Gaussian/Gauss Jordan elimination, Solving ax=b linear education system. 

2. Calculus 

No matter how much you hated calculus during college, the truth is that it is commonly used in data science and ML, and you can't ignore it. Calculus is highly useful for solving mathematics problems involving taking functions' derivatives. 

• Calculus is used in many fields, including physics engineering, and it is beneficial when dealing with optimization problems. (For example, gradient descent)

• Calculus also has many applications in data science because data sets tend to have multiple components and variables—and these can be difficult to model using simpler methods like linear regression or logistic regression. 

• Calculus allows you to take a more complex approach to model your data by breaking down how each variable contributes towards the final result.

Basic Concepts of calculus:

• Fundamental and Mean value theorems of integral calculus, evaluation of definite and improper integrals 

• Derivatives and Gradients

• Product and chain rule

• Beta and Gamma functions

• Maxima and minima

• Functions of multiple variables, including limit, continuity, and partial derivatives

• Basics of ordinary and partial differential equations

• Infinite series summation/ integration concepts 

3. Statistics

You've probably heard about statistics before, but what does it actually mean? How does it help us understand our data?

Statistics are the core of data analysis, laying the groundwork for understanding what data sets tell us. Statistics are important in machine learning when working with classifications like logistic regression, discrimination analysis, and hypothesis testing.

In order to visualize features, convert features, impute data, reduce dimensionality, engineer features, evaluate models, etc., statistics and probability are required.

This is where statistical inference comes in: You use statistics to measure how likely something is based on certain variables (like age). Statistics can tell you whether people get divorced if they're married or single if they're not.

Statistics are of two major types:

1. Descriptive statistics

This type of statistics refers to the characteristics of a population. 

For example, calculating the mean age of people signing up for website newsletters

2. Inferential statistics 

This statistic makes predictions about the population based on the sample data. 

For instance, hypothesis testing.  

Some of the statistics concepts you should know are: 

• Descriptive statistics, central tendency, variance and covariance, correlation

• Sampling, measurement, random number generation

• Hypothesis testing, A/B Testing, p-values

• Linear regression, regularization 

4) Probability

Probability is the study of the likelihood that something will occur, and it is crucial for arriving at findings that can help make decisions in uncertain circumstances.

While probability and statistics are linked, and people tend to study them together, they are used to identify different conclusions. 

Some of the common probability concepts are:

• Probability distribution

• Bayes Theorem 

• Classical Probability

• Relative Frequency

5) Discrete Mathematics

Although this topic is less commonly included in data science, discrete math lies at the core of all computer systems used in modern data science.

The following discrete math principles will be reviewed, as they are essential for using algorithms and data structures in analytics projects regularly:

• Basics of Sets, subsets, power sets

• Counting functions, combinatorics, countability

• Basic data structures: stacks, queues, graphs, arrays,

• Basic proof techniques: induction, proof by contradiction

• Recurrence relations and equations

• Growth of functions and O(n) notation concept

• Graph properties: connected components, degree, maximum flow/minimum cut concepts, graph coloring

Ready to get started in data science?

Data science is one of the most in-demand jobs right now. It's a very lucrative and auspicious career if you're looking to build a future with exciting possibilities. That being said, if you want to become a successful data scientist, it helps to have a solid mathematical foundation. Maths isn't just valuable for helping you solve problems; it's also vital in helping you design algorithms and set up quantitative systems that run well. Even if stats and sound programming are way more interesting than algebra and calculus, don't let your math phobia stop you from pursuing this career.

What's the point of having such an incredible job if you don't take advantage of it? Thus, whether you're already looking to become a data scientist, it's always handy to know some math. So get started with the best data science courses in India, co-developed with IBM. Get a chance to work on multiple real-world data science and ML projects and level up your portfolio.