If you are intimidated by math, you are like millions of others in this world. If you are passionate about becoming a data scientist, and still intimidated by math, you are bound to have second thoughts in your mind about following your passion. Are you worried about data science being too heavy in math? If yes, you have come to the right place. Here, we will throw light on some of the basic mathematical skills you need to become a data scientist, and clear some misconceptions that people have about data science being too dependent on math.
Answer in short
Yes, you do need to know some math concepts to become a data scientist. However, the amount of math needed in the practical data science world is far lesser than what you thought might have needed. You do need to some basic concepts of calculus, linear algebra and statistics to become a successful data scientist. However, you will be thrilled to know that out of these three, the most useful concept is statistics, which is fairly easy to learn, even if you don’t have any prior knowledge in this field.
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Math is important for data science
Even if you don’t like math, or haven’t learned it in high school, bachelor’s or master’s education, data science can still be a good career choice for you, provided you have an open mind to learn the mathematical concepts required for data science. The truth is that mathematics will always be the basis of data science. It is up to you to update your knowledge on certain concepts of this subject, based on the industry that you plan to work in.
Use of math in data science
As a data scientist, you will be required to do an in-depth analysis of data, draw insights from them by creating algorithms, using programming languages to understand the patterns, create analytical & predictive models to take critical, data-driven decisions, and add as much value as possible to the data after structuring it. All of these involve math, especially three important fields of mathematics – linear algebra, calculus and statistics & probability.
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Here is a brief overview of how these three concepts are used in data science jobs on a day-to-day basis.
- Linear Algebra & Data Science
Linear Algebra, in itself, is a vast subject. However, the good news for you is that you don’t need to master the entire subject to pursue a career in data science. According to experts in data science, it is a good idea to brush up your skills on certain important chapters of linear algebra before choosing a data science course, as these chapters are the ones that are used theoretically and practically in your job. These chapters include vectors & matrices, quantities, linear mappings, data redundancy, fundamental theorem of linear algebra, data information and data partitioning.
A data scientist needs linear algebra concepts to make analytical calculations while creating algorithms. Even if you have multiple dimensions of data in front of you, you can use linear algebra concepts to make accurate representations of them while presenting the data to the management.
- Statistics & Data Science
Of all the math concepts you need to know to become a data scientist, statistics is the most important. This is because the basic concepts of statistics form the core of your job profile. While studying statistics as a subject in your bachelor’s or master’s degree would make learning data science an easy process for you, you don’t have to worry if you are new to this subject. It will take you only up to a couple of months to learn the most important chapters of statistics needed for data science. These include probability distribution, mean, median & mode, hypothesis testing, similarity measures and regression analysis.
As a data scientist, you will definitely use these statistical concepts when creating forecast & analytical models. You can use techniques like regression analysis to forecast sales & profit margins. Statistics are also used when you have to analyze the trends of data and make predictions or decisions based on them.
- Calculus & Data Science
Calculus can be quite intimidating even for math students. However, the truth is that calculus is quite interesting, and if you have an open mind to learn new things, you can learn calculus easily. You don’t have to learn the entire subject, but it is important to touch upon some basic chapters like maxima & minima, single & multi-variable functions, product & chain rule, Taylor’s series, infinite series summation, mean value theorem and derivatives.
Calculus is widely used by data scientists while dealing with neural networks. It is also used for creating algorithms, and the chain rule of calculus is used for many analytical data science processes.
How can you get good at math to become a data scientist?
If you thought that learning all these mathematical concepts from scratch would be too difficult, you are mistaken. Many data scientists have been able to scale great heights in their careers today, even though they didn’t have any background education in math. This is because they approached the right method to learn math for data science. You will be surprised to know that the fundamental concepts of each of the three subjects we mentioned here (linear algebra, statistics and calculus) can be studied easily if you spend one hour on each of them.
Many people believe that knowing probability distribution alone will help them become good data scientists. However, this is not the case in the real world. To pursue a career in data science, you should first learn coding and programming languages, and work on projects related to these. Once you have got a hold of these computing techniques, you can slowly shift to enrolling yourself in short-term training courses on math concepts for data science.
This way, you will first get to understand the practical applications of math before learning the theoretical reasoning behind its use in data science. This will reduce your inhibition, and prepare you well to keep an open mind to accept new thoughts.
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