5 Free Courses to Mastering Math for Data Science

Data science is a powerful field that unlocks valuable insights from data. You don’t need a hefty budget to master these essential skills! Here’s a look at 5 free courses that will equip you with the mathematical knowledge to excel in data science.

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About the Data Science

Data science is an interdisciplinary field that utilizes algorithms, procedures, and processes to analyze large amounts of data, uncover hidden patterns, generate insights, and guide decision-making. It involves the extraction of knowledge and insights from structured and unstructured data using statistical, computational, and machine-learning methods. Data scientists construct questions around specific data sets, use data analytics and advanced analytics to create predictive models and develop insights that inform business decision-making. Data science is a vital discipline that integrates statistics, scientific computing, scientific methods, algorithms, and systems to extract actionable insights from data for various applications across industries.

Eligibility Criteria

Any College Students with any Stream.

Here Are Free Courses to Mastering Math for Data Science

1. Data Science Math Skills

Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one at a time.

Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material. Topics include ~Set theory, including Venn diagrams ~Properties of the real number line ~Interval notation and algebra with inequalities ~Uses for summation and Sigma notation ~Math on the Cartesian (x,y) plane, slope and distance formulas ~Graphing and describing functions and their inverses on the x-y plane, ~The concept of instantaneous rate of change and tangent lines to a curve ~Exponents, logarithms, and the natural log function. ~Probability theory, including Bayes’ theorem. While this course is intended as a general introduction to the math skills needed for data science, it can be considered a prerequisite for learners interested in the course, “Mastering Data Analysis in Excel,” which is part of the Excel to MySQL Data Science Specialization. Learners who master Data Science Math Skills will be fully prepared for success with the more advanced math concepts introduced in “Mastering Data Analysis in Excel.”

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2. Calculus – 3Blue1Brown 

You should surely be comfortable with calculus when we discuss math for data science. However, most students find calculus in high school daunting (I know, I have!). However, this is partially due to the way we learn, which primarily focuses on concepts, a limited number of examples, and a ton of practice problems.

However, if there are useful visuals to assist you move from intuition to equation—focusing on the why—you’ll grasp and learn calculus far more effectively.

We are all in dire need of Grant Sanderson of 3Blue1Brown’s Calculus course! This course will teach you the following and more through a sequence of lectures with incredibly useful visualizations—moving from geometry to formula whenever possible:

Derivatives and Limits

Chain rule, product rule, and power rule

Unspoken distinction

Higher order derivatives

Taylor series

Integration

Course Link Click Here

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3. Linear Algebra – 3Blue1Brown

The datasets you work with as a data scientist are simply matrices with dimensions of num_samples x num_features. Each data point may therefore be thought of as a vector in the feature space. Thus, it is crucial to comprehend matrix decomposition techniques, common operations on matrices, and matrix operation principles.

If you like 3Blue1Brown’s calculus course, you’ll probably enjoy Grant Sanderson’s linear algebra course even more. You can learn the following with the aid of 3Blue1Brown’s Linear Algebra course:

The basics of vector spaces and vectors

The basis, span, and linear combinations

Mappings and linear transformations

Multiplication of matrices

three-dimensional linear transformation

Decisive

Null space, column space, and inverses

Products with dots and crosses

Eigenvectors and eigenvalues

Vector spaces that are abstract

Course Link Click Here

4. Probability and Statistics

Statistics and probability are great skills to add to your data science toolbox. But they are by no means easy to master. However, it’s relatively easier to get your fundamentals down and build on them. 

The Statistics and Probability course from Khan Academy will help you learn the probability and statistics you need to start working with data more effectively. Here is an overview of the topics covered:

Analyzing categorical and quantitative data 

Modelling data distributions

Probability 

Counting, permutations, and combinations 

Random variables

Sampling distribution 

Confidence interval 

Hypothesis testing 

Chi-square test 

ANOVA

Course Link Click Here

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5. Optimization for Machine Learning

If you’ve ever trained a machine learning model, you know that the algorithm learns the optimal values of the parameters of the model. Under the hood, it runs an optimization algorithm to find the optimal value.

The Optimization for Machine Learning Crash Course from Machine Learning Mastery is a comprehensive resource to learn optimization for machine learning.

This course takes a code-first approach using Python. So after understanding the importance of optimization, you’ll write Python code to see popular optimization algorithms in action. Here’s an overview of the topics covered:

The need for optimization

Grid search

Optimization algorithms in SciPy

BFGS algorithm

Hill climbing algorithm

Simulated annealing

Gradient descent

Course Link Click Here