How Much Math Do You Need to Become a Data Scientist?

Data Science is a large field that requires vast expertise and being at a beginner’s level, that’s a fair question to ask “How much maths is required to become a Data Scientist?”  or “How much do you need to know in Data Science?”. The point is when you’ll be working on solving real-life problems, you’ll be required to work on a wide scale and that would certainly need to have clear concepts of Mathematics. 

The pillar of acing mathematics withholds 4 pillars that can help you to start from scratch and will definitely help you in getting a job in the field of Data Science. These 4 pillars that you’ll be learning need to be applied inside which means you need to also learn how and where to program those mathematics algorithms while working on the system.

The very first skill that you need to master in Mathematics is Linear Algebra, following which Statistics, Calculus, etc. come into play. We will be providing you with a structure of Mathematics that you need to learn to become a successful Data Scientist.

4 Mathematics Pillars that are required for Data Science

1. Linear Algebra & Matrix

• Linear Combinations
  • Vectors & Matrices
    • Quantities
    • Vectors
    • Matrices
      • Transpose Matrix
      • Inverse Matrix
      • Trace of a Matrix
      • Determinant Matrix
      • Dot Product
  • Linear Mappings
    • Functions
    • Measurements
    • Linear Mapping Composition
• Vector Spaces
  • Formal Rules
    • Algebraic structures
    • Vector subspaces of a Linear Mapping
  • Data Redundancy
    • Linear dependence
    • Basis and dimension
    • Dimension of matrix spaces
• Fundamental Theorem of Linear Algebra
  • Data Information
    • Partition of linear mapping domain and codomain
  • Data Partitioning
    • Mappings as data
    • The Singular Value Decomposition (SVD)

2. Probability & Statistics

• Probability
  • Continuous and Discrete Random variable
  • Central Limit Theorem
  • Probability distributions – Binomial, Poisson, Normal
• Statistics
  • Mean, Median and Mode
  • Standard Deviation and Variance
  • Similarity measures – Pearson, Cosine, Spearman
  • Hypothesis testing
    • T-test
    • Paired T-test
    • p-value
    • F-Test
    • z-test

3. Calculus

• Maxima and minima
• Mean value theorem
• Product and chain rule
• Taylor’s series
• Derivatives 
• Gradients of Matrices
• Backpropagation
  • Gradient Descent Algorithm
• Useful Identities for Gradient computation
• Higher-Order Derivatives
• Multivariate Taylor Series
• Fourier Transformations
• Area under the curve

4. Geometry & Graph Knowledge

• Deals with the angles, measurements, and proportions of ordinary objects
• To understand Distribution plots, Scatter plots, Boxplot (quartile, percentile)
• To visualize the graphs and ability to generate insights from them.
• Convex and Concave graphs and their properties

Future Scope of Data Science

As we’re approaching advancement, Data Science is considered one the hottest job to pick, and also it has a huge potential for the near future not in India but worldwide. Now, companies are actively looking and hiring for such professionals which resulted has spiked the growth of the Data Science Industry at a larger scale. It is predicted that this industry is likely to grow by 36 percent from 2021 to 2031, which would cross USD 700 Billion. These figures are clearly reflecting on the number of job openings and the figure is surged to above 12,000 (active openings) in India & above 254,000 (active openings) worldwide.

Taking calculative measures has become one of the most prominent reasons for companies these days to sustain in this competitive market and this is where the growth has spiked in the trend and is likely to reach more heights soon.

This is the scope of Data Science for the coming future. However, if you’re looking for an answer that how to start your journey from scratch, We recommend you refer to this article: How to Become Data Scientist – A Complete Roadmap and start your journey right away!