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There are many misconceptions about data science. Many people think that data scientists are required to have a PhD in math or data science to be successful. Many also think that data scientists are only used for solving hard math problems. But the truth is that data scientists are not required to have an advanced degree in math or data science.
You need a foundational knowledge of some mathematical concepts in order to pursue a career in data science. You need to be proficient in discrete math, linear algebra, statistics, and calculus, as well as computational mathematics.
How Much Math Do Data Scientists Use?
Mathematics is the base of most contemporary disciplines in the field of science. Nearly all the techniques used in data science, even machine learning, are deeply rooted in math.
To function as a good data scientist, you must develop different skills, and almost all of them are associated with math.
For example, some common tasks assigned to data scientists are to build machine-learning algorithms and create prediction-based models. For these, you will need to at least know how to properly graph functions. This is covered in any basic algebra class.
If you are asked to understand which type of ads to run for a particular brand, you’d need to multiply matrices. You will learn about matrices and how to manipulate them in your advanced algebra course.
Besides the hardcore math-related skills, you will also need to possess at least some basic programming abilities and a basic understanding of economic concepts. Again, these are rooted in mathematics.
So, the short answer to the question, “How much math do data scientists use?” is a lot.
What Parts Of Math Do You Need For Data Science?
Besides arithmetic, you will need to know about concepts from higher math subjects. You will apply them in all the aspects of a data scientist’s job.
Why do you need to pay attention to your math lessons in high school? The math concepts that you will learn during this period will help you understand the dynamics of binary searches and analyze time series.
This area of math covers the concepts of equations of a line to the binomial theorem. Some topics in this area that you should pay special attention to are logarithms, exponential and polynomial functions, and rational numbers.
Graphing and plotting coordinates on a Cartesian plane is also a must-learn.
You will also need a working knowledge of series, sums, and inequalities. Some basic theorems in geometry and trigonometry will also come in handy when you start working as a data scientist.
In any analytical project, you will use what you know about data structures and algorithms.
So, you need to pay attention to your lessons in topics such as sets and subsets, counting functions and combinatorics, and concepts like trees, hash tables, arrays, queues, and stacks.
To be able to make good decisions, you will apply your lessons in logic. So, pay attention to your lessons in proof techniques. You must have a strong understanding of the basics of deductive and inductive reasoning as well.
Additionally, you must be able to graph properties. This entails knowing about connected components, maximum flow and minimum cuts, and degree.
For choosing algorithms, you must be able to understand how space and running time requirements grow given a specific data size. This skill is also rooted in what you learn in your discrete math classes.
Data scientists deal with machine-learning algorithms and how they work. You must be able to analyze the stream of data you acquire to create useful insights. Thus, you must have a good foundation in linear algebra.
From identifying which song to recommend on music apps to converting your selfies into cartoon portraits, all these require working with matrix algebra. You need to focus on understanding the basic matrix and vector properties.
You need to be able to apply the multiplication rule and get inner and outer products.
An understanding of special matrices like the square matrix unit vectors, triangular matrix, Hermitian, and skew-Hermitian matrixes will certainly give you an advantage.
Plus, you will need to apply the techniques that you learned from your linear algebra class to process learning operations and network structures in neural network algorithms.
We cannot emphasize enough how having a solid foundation in the concepts of statistics and probability is a must for anybody seeking to start a career in data science.
This is because, as many practitioners believe, classical machinal learning is exactly the same as statistical learning.
Statistics is a vast subject area and knowing which parts to focus on is critical for anyone trying to break into data science.
You need to know and understand descriptive statistics and data summaries. You have to be able to get the variance, central tendency, and correlation when given a set of data.
You will also need to know how to interpret basic probabilities and apply Bayes’ theorem. This is also related to identifying expectations and looking at conditional probabilities.
An understanding of concepts on probability distribution functions such as the use of chi-square, central limit theorem, and Student’s t-distribution will also be extremely helpful.
Applying your knowledge on hypothesis testing, t-test, ANOVA will also come up at work from time to time.
Concepts in calculus are an integral part of machine learning, therefore, it is something that you must understand to be a successful data scientist.
What you learn in your college calculus class will be the backbone of your ability to create analytical solutions for any least squares problem in linear regression.
You will also use this knowledge when you make your neural network understand a new pattern.
Do you still remember your lessons on functions of a single variable? Can you apply what you know about mean value theorems or indeterminate forms? These are just a few of the topics that you have to know about.
You will also use your knowledge of product and chain rule, be able to evaluate improper and definite integrals, recall the basics of partial differential equations, and so on.
So, besides algebra, you will use the knowledge and skills that you will develop from studying calculus to get your algorithms to deliver whatever objectives you assign them in a timely manner.
Remember that you don’t have to be a calculus expert. Just being able to understand the core concepts and apply them to your tasks should be enough.
Where to learn math for data science?
Most of the math concepts that you will need to become a successful data scientist will already get covered in your high school and college math courses. However, if you are not happy with your current understanding of them, or if you want to brush up on them, there are many resources that you can find online.
Khan Academy is an extremely helpful resource if you want to review your basic math concepts. It offers videos, texts, and quizzes for subjects such as Algebra 1 and Calculus 1.
EdX is also a great resource for pre-collegiate math. You can find good materials there on introduction to algebraic concepts, pre-university level calculus, and even statistics and probability using Python. That last one is specifically made for data scientists.
Coursera is another immensely popular course provider that you can explore.
It offers various classes that you can take if you want to review anything on mathematical thinking, discrete mathematics, statistics, and linear algebra.
You can even find classes there that focus on business statistics, math for machine learning, and math skills targeted for data scientists.
Finally, there’s Udemy.
This platform connects you with instructors that either hold online classes or give you access to videos and texts to help you learn and review concepts on various subjects.
You will be able to find great courses there on discrete mathematics, sets, functions, statistics, and so much more.
If you are interested in pursuing a career in the field of data science, you have to work on making your math foundation solid. This article has already provided you with a list of math skills that you need to have and master.
There are also online resources that you can use to learn on your own time.
You don’t have to be a math genius to make it in the industry, but you do need a working knowledge of a lot of math concepts to be able to work successfully as a data scientist.