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Math in Data analytics

Credit Author: Vaish

Digital data is growing at a very rapid rate, and changing the way we live. In a world where huge amounts of data are released and collected every day, it has become essential to be able to give meaning to data sets and extract value from them. This process of categorizing and analysing data to study its trends and patterns is called Data Analytics.

Data is analysed with the help of various mathematical algorithms and techniques, namely:

  • Statistics and Probability
  • Algebra and Geometry
  • Calculus
  • Mathematical thinking
  • Neural networks

Statistics and Probability

The importance of statistics and probability just cannot be stressed enough. Statistics and probability are the heart and soul of machine learning, artificial intelligence, and economics. Statistics is used for collecting and sampling useful data, while probability theory is ideal for dealing with uncertainties and making predictions.

Algebra and Geometry

Algebra and geometry go hand in hand to produce graphs and analyse inequalities. They are heavily used in economics to model the behaviour and the trends of the stock market. They are also used for analysing the growth of a business, so that plans can be made to slope up their profits, both literally and figuratively.


Calculus is called the mathematics of change. Calculus is important for studying how a parameter changes over a period of time. It is extensively used to find optimal solutions in engineering, biology, chemistry, economics, technology, real estate, or any other field one can imagine.

Mathematical thinking

Mathematical thinking is used to understand data and derive conclusions based on it. It involves problem-solving, reasoning and proof, communication, and making connections. It is a way of analysing things and deconstructing data. This is the most interesting field, because it does not involve doing the actual math. Rather, it is about exploring what assumptions are necessary to make the math work.

Neural networks

Neural networks algorithms that are designed to recognize patterns. Modelled based on human brain, this technique is used to read sensory data like handwriting, voice input, fingerprints, images, etc., and store them in a digital form for further use. This information is especially useful for applocks, DNA testing, fitness tracking, and much more.

Therefore, it is pretty clear that math plays quite an important role in data analytics. Math can be really interesting when we notice how various concepts are so intricately woven into our day to day life.

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