Basic mathematics knowledge is beneficial if you're working in machine learning, big data, or data science. And even if your technology is not based on mathematics, then having a basic understanding of it can help you to understand your data better. So why do so many people dismiss math as useless or a roadblock in their path?
We've been conditioned to think that data science is all about math. After all, you need to know some stats and fancy programming skills to get into this field. Some see it as a taboo topic or fear being seen as a math geek.
But The Question Is, Do You Really Need Math To Be A Data Scientist?
The math skills you need to be a data scientist depend on the type of data science work you want. If you want to work with large-scale datasets, you'll need to know how to do statistics and probability. However, if you work with complex algorithms, you'll need to know calculus, linear algebra, and machine learning.
One thing that all data scientists have in common is their ability to use math. Math skills are essential for working with data: the more advanced a job title is, the more math-dependent it will be. For example, a biostatistician has an incredible amount of math knowledge but only uses it in day-to-day work less than someone who works with sales or marketing data might need to do so. A statistician may need advanced math skills for their job title but will still rely on them less often than someone who works directly with customers or leads in sales departments would need these skills to perform those duties effectively.
Thus, Maths is a core part of data science, and you must have a good grasp of the basics before you move on to more complex subjects. If you have a strong interest in mathematics and computer science (or any other STEM subjects), then becoming a data scientist might be right for you! Learnbay offers rigorous data science training in Bangalore for working professionals wishing to begin their careers as data scientists.
However, it is true that if you want to be a data scientist, you will need to know some basic math. There are many ways that math plays a vital role in data science and machine learning, but it's optional to learn them all.
Linear Algebra This is a fundamental component of mathematics that every learner should know. Why? Linear algebra is a critical component of many machine-learning techniques. For instance, matrix multiplication is necessary for regression and PCA, respectively. As a Data Scientist, you'll have to deal with vast information in your profession. You'll deal with a multi-dimensional array or matrix for all your data. Many of the operations you will do on these data will necessitate familiarity with the array, vector, and matrix operations.
Calculus If you want to learn more about algorithms like Linear Regression, Stochastic Gradient Descent, etc., and understand how they function, or if you want to get into research, you'll need this. If you want to get down to the nitty-gritty of how things operate, you'll also need this (especially Gradient Descent). Derivatives, curvature, divergence, and quadratic approximations are among the topics you'll learn. Gradient descent and algorithm training are examples of applications of multivariate calculus.
Statistics: The most important is statistics, which is used for working with large amounts of data and making predictions about what will happen next. Statistics can help you understand how certain groups of people react differently than others and what factors might impact their reactions, which can be helpful for both marketing purposes and scientific research.
Statisticians are needed to deal with a lot of data, and they will need to handle, understand and display that data to do their job as Data scientists. You'll need to know how statistical methods work and how they can be applied in different contexts. Classification techniques like logistic regression, discrimination analysis, hypothesis testing, and distributions depend on this in machine learning.
Probability Knowing the probability of understanding algorithms like Logistic Regression and Decision Trees is necessary. When it comes to significant applications of deep learning, probabilities will play a big role. Hypothesis testing and distributions like the Gaussian distribution and the probability density function rely heavily on this. Self-driving automobiles are an example of the substantial use of deep learning.
Applications Of Maths In Data Science
Almost every business relies on the expertise of data scientists to carry out its day-to-day operations and achieve success. Understanding how math for data science can be used in authentic contexts might help you grasp why organizations require data scientists and how mathematics is involved.
Let's explore some of the most popular data science and machine learning applications and technologies in use today.
NLP: Topic modeling and predictive analytics both leverage linear algebra as a learning tool in NLP for word embeddings. Chatbots, language translation, speech recognition, and sentiment analysis are all examples of NLP in action.
Computer Vision: Computer vision, such as image representation and processing, also uses linear algebra. Self-driving vehicle companies, including Tesla, immediately come to mind when computer vision is mentioned in the media. The classification of diseases and the improvement of diagnostics are two other common uses of computer vision in industries like agriculture and healthcare.
Marketing and Sales: Hypothesis testing, for example, makes good use of statistics when analyzing marketing efforts' efficacy. Additionally, it's applied in approaches like causal impact analysis and survey design, as well as personalized recommendations made using predictive modeling or clustering.
Let's Cut To The Chase
I believe a sizable amount of math is needed to succeed as a data scientist—at least a basic knowledge of algebra, statistics, and probability. The data science industry abounds with stories of people who built algorithmically successful products by leveraging Big Data's gains without mathematically understanding why they work. But these are exceptions to the rule – you need a sound mathematical foundation to succeed as a data scientist.
Ultimately, if you want to be a successful data scientist, you must become a master of the fundamentals of Maths. This includes an understanding of statistics, probability, and math. If you adopt a mastery approach, performing at the highest level will be easy. The best way to learn them would be through an online course like a data science course in Bangalore offered by Learnbay. The instructor-led online class will help you become a certified statistician and data scientist.