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Is data analytics math heavy

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Are you one of those people who have a twisted relationship with maths? While data analysts have to be good with numbers, a foundational knowledge of math and statistics is helpful. Data science occupations demand mathematical studies since machine learning algorithms, running analysis, and gaining insights from data all require arithmetic. Although math is not the only requirement for a data science study and job, it is typically one of the most crucial. So, to answer the question, yes, data analytics is a little math-heavy, but worry not. We will tell you all things necessary to keep the complicated stuff at bay.   

Do you need math to become a data analyst?

Yes, but in practice, while mathematics is used in many aspects of data science, you may not need to learn or relearn as much as you might think. Most data scientists are only concerned with understanding the principles of mathematics, and how those principles may affect their models. Learning the abilities needed to become a data analyst is not difficult. You must have sufficient domain expertise and technical skills to query and extract insights from data; this is a constant learning process.

The good news is that for the majority of your data analytics employment, you only need to be an expert in statistics. The possibility of having to re-learn mathematics is a major barrier to becoming a data analyst for many people who had terrible experiences with it in high school or college.

 

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Why is mathematics so important in data science?

All contemporary scientific disciplines are built on mathematical principles. As a result, practically all data science methodologies and ideas, including machine learning (ML) and artificial intelligence (AI), are deeply rooted in mathematics. To become a great data analyst, one obviously needs to possess a wide range of information, including programming skills, business knowledge, and the inquisitive mind necessary for tackling complex analytical problems. However, it’s always preferable to be a knowledgeable driver who understands the workings of the car than to be a careless driver who doesn’t.

For understanding math in data analytics, we need knowledge of the three most important pillars of this field. They are:

  1. Linear algebra

As a student or a math enthusiast, you may be aware of linear algebra by now. This branch of mathematics deals with the solution of linear equations with unknown values. More importantly, it serves as a major foundation for the development and implementation of ML algorithms. Some of the concepts of linear algebra that you should be aware of are

  • Matrices

  • Transpose and Trace a matrix

  • Determinant and inverse of a matrix

  • Dot products

  • Vectors

  • Eigenvalues and Eigenvectors

 

  1. Probability and statistics

Probability and statistics together constitute the foundation of data science, even though these are two separate branches of mathematics. The probability theory is crucial for estimating and creating predictions, which may then be used for more in-depth study using statistical techniques. Finding the likelihood of an event occurring is the focus of probability, which enables data analysts to make inferences and decisions in the face of uncertainties. As a result, you should be aware of the following sub-topics to become a successful data analyst.

  • Random variables and distributions

  • Central limit theorem 

  • Conditional probability and Bayes theorem 

  • Conditional distributions and expected values

  • Markov chains

 

Statistics, on the other hand, is at the heart of complex AI and ML algorithms, gathering and turning data trends into persuasive evidence. Inferential statistics allow data analysts to make predictions about a population based on sample data, whereas descriptive statistics assist us in better understanding our data by recognizing underlying patterns and characteristics of data sets. As a result, firms may be able to predict whether a given product would be well-liked by a particular market or not. 

The fundamental statistical tools required for data analysis include the following:

  • Descriptive statistics and visualization techniques 

  • Measures of central tendency and asymmetry 

  • Variance and Expectations 

  • Linear and logistic regressions

  • Rank tests

  • Principal Components Analysis

 

  1. Other Mathematical tools

Several newbies who disliked mathematics in high school will be in for an unpleasant revelation because mathematics is a crucial component of ML and AI algorithms. The good news is that you don’t need to delve too deeply into these concepts because most analytical positions just require a basic comprehension of mathematics.

 

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Mathematics is essentially the study of ongoing change. Along with linear algebra, it helps develop and refine algorithms over time. It is critical to realize that every ML model is supported by an optimization method that significantly draws on fundamental mathematics ideas. A linear regression estimator can be created using the Gradient Descent Algorithm (GDA), which is employed in many of these models. For example, by examining housing data sets, GDA may estimate future home prices by locating the minima of a function using various calculus methods. As a result, it is advised to have a solid grasp of the following subjects:

  • Continuity and limits

  • Differentiation and its theorems 

  • Product and Chain rule

  • Taylor’s series and infinite series summation

  • Fundamental and mean value theorems of integral calculus

  • Gamma and Beta functions 

  • Multi-variable functions

In a nutshell, it can be concluded that data analytics is indeed math-heavy, but you don’t have to worry about it if you have brushed up your knowledge on the above-mentioned concepts. In addition to these three main concepts, which serve as the fundamental pillars of data science, some other mathematical tools are used to power the analytical engine. 

One of these is discrete mathematics, which, despite receiving little attention in the data science community, is at the core of all computer systems. Data structures, set theory, fundamentals of logic, and methods of proof are the main topics covered in discrete mathematics. 

Given that computer systems are composed of discrete packets of memory bits and that discrete mathematics deals with numbers with finite accuracy, computer systems rely heavily on this field of math. So, you do need to brush up your mathematical skills, and take up training courses if needed, before you decide to get into data analytics and make a name for yourself in it.

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