Photo by plot on Freepik
When you’re learning data science, building a good math foundation will make your learning easier and much more effective. Even if you’ve already landed your first data science role, learning the basics of data science math will only enhance your skills.
From exploratory data analysis to building machine learning models, a good foundation in mathematics such as linear algebra and statistics will give you a better understanding Why you do What you do. Even if you are a beginner, this list of courses will aid you learn:
- Basic math skills
- Calculus
- Linear algebra
- Probability and statistics
- Optimization
Sounds engaging, right? Let’s start!
A prerequisite for data science courses is knowledge of mathematics. Specifically, most courses assume that you are comfortable with high school algebra and calculus. But don’t worry if you’re not there yet.
The Mathematical skills related to data analysis The course offered by Duke University on Coursera will aid you master the basics of mathematics in the shortest possible time. Topics covered in this course include:
- Troubleshooting
- Features and charts
- Introduction to calculus
- Introduction to probability
It is recommended that you complete this course before taking other courses that cover specific math topics in more detail.
To combine: : Data Science Math Skills – Duke University on Coursera
When we talk about math in data science, calculus is definitely something you should be comfortable with. However, most students find high school calculus intimidating (I’ve been there too!). But this is partly because of the way we learn – focusing mainly on concepts, few illustrative examples, and tons of hands-on exercises.
However, you will understand and learn calculus much better if you have helpful visualizations – which aid you move from intuition to equation – by focusing on Why.
- Limits and derivatives
- Power rule, chain rule, product rule
- Hidden differentiation
- Higher Tier Derivatives
- Taylor series
- Integration
To combine: : Bill – 3Blue1Brown
As a data scientist, the datasets you work on are essentially num_samples x num_features matrices. You can therefore think of each data point as a vector in the feature space. Therefore, it is significant to understand how matrices work, common matrix operations, and matrix decomposition techniques.
If you liked 3Blue1Brown’s Calculus course, you’ll probably enjoy Grant Sanderson’s Linear Algebra course just as much, if not more. The Linear algebra 3Blue1Brown’s course will aid you learn the following:
- Basics of vectors and vector spaces
- Linear combinations, span and base
- Linear transformation and matrices
- Matrix multiplication
- 3D linear transformation
- Determinant
- Inverses, column space and null space
- Dot and cross products
- Eigenvalues and eigenvectors
- Abstract vector spaces
To combine: : Linear Algebra – 3Blue1Brown
Statistics and probability are great skills to add to your data science toolkit. But they are not at all simple to master. However, it is relatively easier to learn the basics and build on them.
The Statistics and probability Khan Academy course will aid you learn probability and statistics needed to work with data more effectively. Here is an overview of the topics covered:
- Categorical and quantitative data analysis
- Modeling data distributions
- Probability
- Counting, permutations and combinations
- Random variables
- Sample distribution
- Confidence interval
- Hypothesis testing
- Chi-square test
- ANOVA
If you want to dive deeper into statistics, also check out 5 free courses to improve your statistics in data science.
To combine: : Statistics and probability – Khan Academy
If you’ve ever trained a machine learning model, you know that the algorithm learns the optimal values for the model’s parameters. Under the hood, it runs an optimization algorithm to find the optimal value.
The Optimized for a crash course in machine learning with Machine Learning Mastery is a comprehensive resource for machine learning optimization.
This course takes a code-driven approach using Python. So, once you understand the importance of optimization, you will write Python code to see popular optimization algorithms in action. Here is an overview of the topics covered:
- The need for optimization
- Grid Search
- Optimization algorithms in SciPy
- BFGS algorithm
- Mountain climbing algorithm
- Simulated annealing
- Gradient descent
To combine: : Optimizing for a Machine Learning Crash Course – MachineLearningMastery.com
I hope you found these resources helpful. Since most of these courses are aimed at beginners, you should be able to master all the necessary math without feeling overwhelmed.
If you are looking for Python courses for data science, read 5 Free Courses to Master Python for Data Science.
Have a nice studying!
Bala Priya C is a software developer and technical writer from India. He likes working at the intersection of mathematics, programming, data analytics and content creation. Her areas of interest and specialization include DevOps, data analytics and natural language processing. She enjoys reading, writing, coding and coffee! He is currently working on learning and sharing his knowledge with the developer community by writing tutorials, guides, reviews, and more. Bala also creates engaging resource overviews and coding tutorials.