Mathematics is very important in the field of data science as concepts within mathematics aid in identifying patterns and assist in creating algorithms. The understanding of various notions of Statistics and Probability Theory are key for the implementation of such algorithms in data science. Notions include: Regression, Maximum Likelihood Estimation, the understanding of distributions (Binomial, Bernoulli, Gaussian (Normal)) and Bayes’ Theorem. “Mathematics in data science and machine learning is not about crunching numbers, but about what is happening, why it’s happening, and how we can play around with different things to obtain the results we want.” Some common areas of mathematics that aid in data science work are (but not limited to) statistics, linear algebra and Differential Calculus.
Now let’s take a deeper dive into these areas of mathematics and some courses to help you upskill yourself:
In data science, statistics is at the core of sophisticated machine learning algorithms, capturing and translating data patterns into actionable evidence. Data scientists use statistics to gather, review, analyze, and draw conclusions from data, as well as apply quantified mathematical models to appropriate variables. Data scientists work as programmers, researchers, business executives, and more. However, what all these areas have in common is a basis of statistics. Thus, statistics in data science is as necessary as understanding programming languages.
Statistical functions are used in data science to analyze raw data, build data models, and infer results. Below is a list of the key statistical terms: Population: the source of data to be collected; Sample: a portion of the population; Variable: any data item that can be measured or counted; Quantitative analysis (statistical): collecting and interpreting data with patterns and data visualization; Qualitative analysis (non-statistical): producing generic information from other non-data forms of media.
Here are some courses to upskill your Statistics skills:
- Advanced Statistics for Data Science – Johns Hopkins University
- Statistics for Data Science – Coursera Project Network
- Statistics for Data Science with Python – IBM Skills Network
- Mathematics for Machine Learning – Imperial College London
- Introduction to Statistics – Stanford University
Linear Algebra is a branch of mathematics that is extremely useful in data science and machine learning. Linear algebra is the most important math skill in machine learning. Most machine learning models can be expressed in matrix form. A dataset itself is often represented as a matrix. “With an understanding of Linear Algebra, you will be able to develop a better intuition for machine learning and deep learning algorithms and not treat them as black boxes. This would allow you to choose proper hyperparameters and develop a better model. You would also be able to code algorithms from scratch and make your own variations to them as well.” Here are a few topics you need to be familiar with (not limited to): Vectors, Matrices, Transpose of a matrix, Inverse of a matrix, Determinant of a matrix and Trace of a matrix.
Here are some courses to upskill your Linear Algebra skills:
- Linear Algebra for Data Science using Python – Howard University
- Essential Linear Algebra for Data Science – University of Colorado Boulder
- Linear Algebra for Machine Learning and Data Science – DeepLearning.Ai
- Mathematics for Machine Learning: Linear Algebra – Imperial College London
- Data Science Math Skills – Duke University
Calculus is an intrinsic field of mathematics and especially in many machine learning algorithms that you cannot skip over if you want to learn to learn the essence of Data Science. A machine learning algorithm (such as classification, clustering or regression) uses a training dataset to determine weight factors that can be applied to unseen data for predictive purposes. Behind every machine learning model is an optimization algorithm that relies heavily on calculus. Therefore it is important to have fundamental knowledge in calculus as this would enable a data scientist to have some understanding of the optimization algorithms used in data science and machine learning. There are two types of calculus that are worth highlights: Differential Calculus and Integral Calculus. Differential Calculus cuts something into small pieces to find how it changes. Integral Calculus joins (integrates) the small pieces together to find how much there is.
Here are some courses to upskill your Calculus skills:
- Algebra and Differential Calculus for Data Science – University of Colorado Boulder
- Applied Calculus with Python – Johns Hopkins University
- Calculus for Machine Learning and Data Science – DeepLearning.AI
- Calculus through Data & Modelling: Vector Calculus – Johns Hopkins University
- Integral Calculus and Numerical Analysis for Data Science – University of Colorado Boulder
In the field of data science, mathematics plays a crucial role in identifying patterns, creating algorithms, and developing insights. Statistics, linear algebra, and calculus are some of the key areas of mathematics that aid data science work. Upgrading skills in these areas is essential for aspiring and current data scientists. Several courses, such as those offered by Coursera can help individuals upskill themselves.
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