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When you are learning data science, building a good foundation in mathematics will make your learning journey easier and much more effective. Even if you've already landed your first role in data, learning the mathematical foundations for data science will only improve your skills.
From exploratory data analysis to building machine learning models, having a good foundation in mathematical topics such as linear algebra and statistics will give you a better understanding because you do that you do. So even if you are a beginner, this list of courses will help you learn:
- Basic math skills
- Calculation
- Linear algebra
- Odds and statistics
- Improvement
Sounds interesting, doesn't it? Let us begin!
Data science courses require you to be comfortable with mathematics as a prerequisite. To be specific, most courses assume that you are comfortable with high school algebra and calculus. But don't worry if you're not there yet.
He Data Science Math Skills The course, offered by Duke University on Coursera, will help you start learning the fundamentals of mathematics in the shortest time possible. Topics covered in this course include:
- Problem resolution
- Functions and graphics
- Introduction to calculus
- Introduction to probability
It is recommended that you complete this course before beginning other courses that explore specific mathematical topics in greater depth.
Link: Mathematical Skills in Data Science – Duke University on Coursera
When we talk about mathematics for data science, calculus is definitely something you should be comfortable with. But most students find high school calculus intimidating (I've been there too!). However, this is partly due to the way we learn: focusing mainly on concepts, a small number of illustrative examples and lots of practice exercises.
But you will understand and learn calculus much better if you have useful visualizations (to help you go from intuition to equation) focusing on the because.
He Calculation Grant Sanderson's course from 3Blue1Brown is exactly what we all need! Through a series of lessons with super useful visualizations, going from geometry to formula whenever possible, this course will help you learn the following and more:
- Limits and derivatives
- Power rule, chain rule, product rule.
- Implicit differentiation
- Higher order derivatives
- taylor series
- Integration
Link: Calculation – 3Blue1Brown
As a data scientist, the data sets you work with are essentially arrays of dimensions num_samples x num_features. Therefore, he can think of each data point as a vector in the feature space. Therefore, it is important to understand how matrices work, common matrix operations, and matrix decomposition techniques.
If you loved 3Blue1Brown's calculus course, you'll probably enjoy Grant Sanderson's linear algebra course as much or more. He Linear algebra The 3Blue1Brown course will help you learn the following:
- Fundamentals of vectors and vector spaces.
- Linear combinations, section and base.
- Linear transformation and matrices.
- Matrix multiplication
- 3D linear transformation
- Determinant
- Inverses, column space and null space
- Stitch and cross products
- Eigenvalues and eigenvectors
- Abstract vector spaces
Link: Linear algebra – 3Blue1Brown
Statistics and probability are great skills to add to your data science toolbox. But they are not at all easy to master. However, it is relatively easier to master the fundamentals and develop them.
He Statistic and probability The Khan Academy course 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:
- Analysis of categorical and quantitative data.
- Modeling data distributions
- Probability
- Counting, permutations and combinations.
- Random variables
- Sampling distribution
- Confidence interval
- Hypothesis evaluation
- chi-square test
- ANOVA
If you're interested in diving deeper into statistics, also check out 5 Free Courses to Master Statistics for Data Science.
Link: Statistics and Probability – Khan Academy
If you've ever trained a machine learning model, you know that the algorithm learns the optimal values of the model parameters. Under the hood, it runs an optimization algorithm to find the optimal value.
He Optimization Crash Course for Machine Learning from Machine Learning Mastery is a comprehensive resource for learning how to optimize machine learning.
This course takes a code-based approach using Python. So after understanding 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
- Climbing algorithm
- Simulated annealing
- gradient descent
Link: Optimization for Machine Learning Crash Course – MachineLearningMastery.com
I hope you found these resources useful. Because most of these courses are designed for beginners, you should be able to learn all the essential math without feeling overwhelmed.
If you are looking for courses to learn Python for data science, read 5 free courses to master Python for data science.
Happy learning!
twitter.com/balawc27″ rel=”noopener”>Bala Priya C. is a developer and technical writer from India. He enjoys working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include DevOps, data science, and natural language processing. He likes to read, write, code and drink coffee! Currently, he is working to learn and share his knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource descriptions and coding tutorials.
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