Sep 19, 2023

# 5 Free Math courses for data science: Beginners to Advanced

In the ever-evolving landscape of data-driven fields like data science and machine learning, a strong foundation in mathematics and statistics is not just beneficial; it's essential. Whether you're a beginner eager to take your first steps into the world of statistics or an intermediate learner looking to dive deeper into Bayesian statistics, there are a plethora of online courses available that cater to your specific needs and skill levels. In this article, we'll introduce you to five noteworthy courses, each designed to equip you with the mathematical and statistical prowess required for success in the realm of data science. From the basics of probability and descriptive statistics to advanced topics like Bayesian statistics and machine learning mathematics, these courses are your gateway to building a robust knowledge base.

## 1.Introduction to Statistics by Stanford on Coursera

Level: Beginner

Duration: Approx. 14 hours

Fee: Free to audit, only upgrade for certificates, financial aid available

What you’ll learn: Descriptive statistics, Probability, Binomial Distribution, regression, confidence interval, ANOVA

Description:

Stanford's "Introduction to Statistics" course helps you grasp important statistical concepts needed to make sense of data and convey your findings effectively. By completing the course, you'll develop the ability to analyze data for insights, comprehend the fundamentals of sampling, and choose the right statistical tests for various situations. These fundamental skills will set you up for exploring more advanced topics in statistics and machine learning.

## 2. Data Science Math Skills on coursera

Level: Beginner

Duration: Approx. 13 hours

Fee: Free to audit, only upgrade for certificates, financial aid available

What you’ll learn: Bayes' Theorem, Bayesian Probability, Probability, Probability Theory

Description: Data science courses require a solid foundation in math, and this course is specifically designed to teach you the essential math skills necessary for success in almost any data science math course. It's tailored for learners who have basic math knowledge but might not have studied algebra or pre-calculus.

The course covers various topics, including:

• Set theory, illustrated through Venn diagrams
• Real number line properties
• Interval notation and working with inequalities
• Summation and Sigma notation and their applications
• Cartesian (x,y) plane mathematics, including slope and distance formulas
• Graphing functions and describing their inverses on the x-y plane
• Understanding instantaneous rate of change and tangent lines to curves
• Exponents, logarithms, and the natural log function

## 3. Mathematics for Machine Learning Specialization on coursera

Level: Beginner

Duration: Approx 1 month (10 hours/week)

Fee: Free to audit, only upgrade for certificates, financial aid available

What you’ll learn: Eigenvalues And Eigenvectors, Principal Component Analysis (PCA), Multivariable Calculus, Linear Algebra

Description: This specialization helps you refresh and solidify your fundamental math skills, essential for higher-level Machine Learning and Data Science courses. It bridges the gap between your previous math education and its practical application in Computer Science. In this specialization there are 3 courses which are as follows:

• In the first course, Linear Algebra, you'll explore how linear algebra relates to data, covering vectors and matrices.
• The second course, Multivariate Calculus, builds on this by teaching optimization techniques for data fitting, starting with introductory calculus.
• The third course, Dimensionality Reduction with Principal Component Analysis, shows you how to compress high-dimensional data using the math from the first two courses. It's of intermediate difficulty and requires Python and numpy knowledge.

Then in the Applied Learning Project, you'll apply your newfound skills in Python to solve real-world problems. This includes tasks like calculating page rank for a simulated internet, training neural networks, performing non-linear least squares regression, and using principal component analysis to analyze the MNIST digits dataset.

## 4. Bayesian Statistics: From Concept to Data Analysis on coursera

Level: Intermediate

Duration: Approx. 11 hours

Fee: Free to audit, only upgrade for certificates, financial aid available

What you’ll learn: Statistics, Bayesian Statistics, Bayesian Inference, R Programming

Description: This course introduces the Bayesian approach to statistics, covering probability concepts and data analysis. It emphasizes the philosophy behind Bayesian statistics and practical implementation for various data types. You'll compare it to the more commonly taught Frequentist approach and discover the benefits of Bayesian methods, such as improved uncertainty management, intuitive results, and explicit assumptions.

This course offers a dynamic learning experience with lectures, computer demos, readings, exercises, and discussions. You can use either Microsoft Excel or the open-source R for computation, as both options have equivalent content. The lectures provide mathematical foundations, philosophical insights, and interpretation explanations.

## 5. Probability and Statistics in Data Science using Python by edx

Duration: 10 weeks (10-12 hours/week)

Fee: Free to audit, only upgrade for certificates

What you’ll learn: Mathematics for Machine learning, Understand what is confidence level and more

Description: As a data scientist, your primary task is to extract valuable insights from intricate and noisy datasets. Handling uncertainty is a crucial aspect when dealing with noisy data, and Probability and Statistics serve as the fundamental tools for addressing this uncertainty.

In this course, which is a part of the Data Science MicroMasters program, you will build a solid foundation in probability and statistics. You'll delve into both the mathematical principles and practical applications through hands-on experience using Jupyter notebooks.

Key concepts covered include random variables, dependencies, correlations, regression, PCA (Principal Component Analysis), entropy, and MDL (Minimum Description Length).

### Conclusion

From Stanford's "Introduction to Statistics" for beginners to the advanced "Probability and Statistics in Data Science using Python," there's a course for every stage of your learning journey. So, whether you're seeking to make sense of data, improve your decision-making skills, or unlock the potential of advanced statistical methods, these courses offer a wealth of knowledge and a path to proficiency.

We at Alphaa AI are on a mission to tell #1billion #datastories with their unique perspective. We are the community that is creating Citizen Data Scientists, who bring in data first approach to their work, core specialisation, and the organisation.With Saurabh Moody and Preksha Kaparwan you can start your journey as a citizen data scientist.