5 Free MIT Math Courses You Can’t Miss

The importance of data science is evolving as the demand for data-driven decisions. As a data science professional it is important to have the mathematical skills to analyse data and take insights from it and make the right decisions or even make predictions. Understanding mathematics is important in the data science to figure out how data is represented and spot out differences about how the data is spread.

Getting the grip of math, can help you in your data science career and as well as to tackle your technical round of interviews and to get a deeper understanding of the algorithms you will use.

Here is the list of the 5 free MIT math courses you definitely can’t miss and help you master the data science journey on the following topics:

• Linear algebra

• Calculus

• Statistics

• Probability

As we can see, calculus is not as important as linear algebra for careers which require only data high school level of arithmetical understanding to be a data scientist. This Linear Algebra course by Professor Gilbert Strang is yet another delight that is considered as one of the 5 free MIT math courses to take. By doing so, one can reach the goals stated for this learning session and for the next ones, and ensure that one has grasped the contents of the problem sets and tests.

The free MIT math courses’ three main modules are arranged as follows:

There are three main subdivisions of the course, in the arrangement described below:

• Four subspaces of a matrix and proposed equation systems with a matrix are computationally significant in future research.

•  Develop a proof theoretical account of self-adjoint operators and assess the principal properties of matrix norms

• Positive definite matrices -The proposed method is applicable for working with matrices that are positive definite.

• To some degree, a vast majority of data science concepts come with mastery of calculus. You should feel confident about the chain rule; ability to difference and partial derivative most particularly, single and multivariable calculus, etc.

Another important math topic when working with Data Science is probability. Understanding of statistical analysis and inference, model mathematics and statistical modeling all call for a good understanding of probabilities.

The following subjects are covered in the excellent course Probabilistic Systems Analysis and Applied Probability:

This course focuses on the axioms of probabilities and the various types of models that are used in the probability theory:

• Bayes rule and conditioning

• Counting Independence

• In the case of probability distributions that are continuous and discrete, the random variables can be in both of the categories.

• Constant Bayes algorithm

A solid foundation in statistics is necessary to become proficient in data science. Many applied statistics ideas that are important to data science are covered in the Statistics for Applications course.

• Inference using parameters

• Moments of maximum likelihood estimation

• Testing of hypotheses

• Fit quality

• Regression

• Bayesian data analysis

• Analyzing the principal components

• linear models with generalizations

There is a possibility of synthesizing with the Linear and Calculus classes acquired through the help of The Matrix Calculus for Machine Learning and Beyond. The last course on this list might also be one of the most developed out of the others listed here. While it is not suitable for learning basic programming languages and scripting, if you have an interest in machine learning and research or if you want to take graduate level data science class, it can be highly advantageous.

• Taylors expansion and or linear approximations of any distance vector space and differentiation as a linear operation.

• Derivatives where the functions involve a matrix either as an input or output

• Matrix factorization derivatives

• Multi-level chain rule

• FA/MR differentiation between forward and reverse manual and automatic operations

The value of learning what data science is growing due to the need for data-driven solutions. It is important to have mathematical proficiency in order to analyze the data into insights, into decisions or even into forecasts. A grasp of Mathematics is considerable in data science to assess the manner in which data is written and to discern differences in the distribution of the data.