Data science is currently a popular topic, mainly because it’s such an in-demand field. It combines several branches, including computer sciences, mathematics, and business intelligence, to receive conclusions from data. So, if this is a profession that sounds appealing to you, you’re probably interested in maths for data science. But, does it have to be your favourite subject at school? Do you have to be the best mathematician to learn about data? Or should you find yourself a perfect math tutor Sydney tutoring company offers?
Below you’ll learn if you need mathematical skills to work with data and which skills you should focus on. You’ll also find out why maths is an integral part of analysing, calculating, and getting insights from data.
What Role Does Maths Play in Data Science?
According to a recent LinkedIn report, the world is facing a shortage of data scientists. The demand is high, but there aren’t enough skilled people to respond to it. Therefore, all students thinking of prospective careers and willing to go deeper into data should consider this profession. But what happens if mathematics isn’t your strong suit?
Having basic maths skills is essential if you’re interested in the science of data. You don’t have to be a maths genius; however, having knowledge in several areas is necessary. Although this science uses bits and pieces of coding, mathematics, and even artificial intelligence, maths is one of the most important parts of understanding data.
These days, computer science has replaced humans in several parts of working with data, but that doesn’t mean that a data science pro should lack maths skills. In this case, mathematics is present in all parts of the job, starting with algorithms, metrics, functions, hypotheses, etc. Although this is a subject most kids find intimidating, getting better at mathematics and putting in the effort to learn can help you build a successful career as a data scientist.
What Maths Do You Need to Become a Data Scientist?
There are numerous areas these scientists can specialise in; however, they all need to learn the basics in three maths branches: calculus, algebra, and statistics. Once they decide which part they’ll specialise in, they will upgrade their knowledge and skills in that area.
Calculus is a maths branch that explores how a function changes at a particular time, the rate of change, and the factors that determine it. It’s divided into two parts: differential calculus and integral calculus. It’s an old science with beginnings that date back to the 17th century.
Everyone interested in mathematics for data science should know the basics of calculus. Although this isn’t the favourite school subject of many students, it’s essential to understand it for data science and machine learning.
Beginners need to be familiar with the basics of calculus to understand the meaning of gradient descent better. Then, it will help them find the local minima of differentiable functions. The next thing potential scientists of data should know is multivariable calculus. It’s pretty useful if you’re interested in machine learning because it takes application in algorithms.
2. Linear Algebra
Algebra is another branch of mathematics that explores linear equations. In other words, you have several familiar quantities and also unknown ones that you should figure out by using this maths branch. Learning linear algebra will help potential scientists understand algorithms much better. It’s one of the most crucial parts of maths for data science because data and algorithms are tightly intertwined.
Many parts of algebra will be helpful for students who want to succeed in this profession. Analysing data is much more than just algorithms. Students should also focus on learning about vectors, matrix algebra, linear regression, covariance matrices, and much more. Once again, there is no need to be a pro in algebra, but you still have to gain at least basic knowledge before you start working with data.
Before computers and artificial intelligence did all the work, a science named statistics helped people calculate information and summaries of important data. Doing statistics by hand was a go-to method for people when they only analysed a limited amount of data. However, as the number of information grows, big data analytics requires big data mathematics done by computers and special tools.
Descriptive statistics is one of the most essential parts of this science for students that want to pursue this profession. Although you don’t have to excel in statistics, you should focus on learning more about descriptive principles such as logistic regression and the testing of hypotheses. Other areas of statistics students should concentrate on include conditionals, Bayes theorem, variance, and expectation, etc. Even though there are numerous tools and programs that do statistics, you have to learn the basics of these concepts to understand how analysing data works.
Other Types of Maths You’ll Need to Solve a Data Science Problem
What type of maths do scientists use to analyse data aside from the three most important mathematical concepts we mentioned above? In most cases, students, interested in working with data, should also have knowledge in sciences such as discrete mathematics, probability, and information theory. Knowing more about the principles of these branches will help you become better much faster and find a speciality that you love the most.
1. Discrete Maths
This branch of mathematics explores discrete numbers. It’s not interested in continuous numbers; that’s why it works with finite precision. Instead, one of the main focuses of discrete maths is integers or numbers that can be either positive or negative but never contain a decimal.
Why is this science important for people who work with data? The moment you start calculating, analysing data, you’re using and seeing discrete numbers. The more you know about this part of mathematics, the more you’ll be prepared to solve problems. Most algorithms will only use discrete numbers, so if you’re ready to learn maths, this is something you have to add to your list.
Probability doesn’t sound like science that data analytics would be interested in; however, you’ll be surprised to hear that it’s a beneficial one. Guessing what the final result or end product will be is important because numerous happenings can’t be predicted with certainty.
One of the most crucial parts of probability students interested in data should learn about is random variables. Although this is a challenging section to tackle and learning it can cause a lot of maths anxiety; with the proper help, everyone can get familiar with the basics.
3. Information Theory
Anyone who wants to work with data will get in touch with information theory quite often. This science is all about information, starting with storing, maximising, optimising, quantifying, and much more. Therefore, any scientist in this area should tap into information theory because digital information and dealing with it are part of the future.
Is it essential to excel in maths for data science? The short answer is no! There is no need for students to be highly passionate about mathematics, but they should have basic knowledge in several maths branches. The three crucial parts that you should focus on should be statistics, algebra, and calculus. Although it sounds like a lot, working with data requires knowing maths principles, most of which fall into the three branches we mentioned above.
Other maths parts you should consider getting familiar with are discrete mathematics, probability, and information theory. Once you feel more confident with your knowledge in these areas, you can start working with data and even expand into Python programming.