Hey,

It’s already Monday and I’m bringing a piece of my data journey right into your pocket!

I want to talk about math for data science. I think it’s very important to understand why some people will tell you not to learn it while others will tell you to learn it.

I used to hate math. Actually, I nearly failed a year at high school because of it. I can’t say I’m a big fan today, but it’s enabled me to do beautiful things with it, both in physics and the world in data.

So, how much math do you need exactly?

If you’re after the data analyst role, you won’t need much math. It’d be good to have a basic understanding of statistics. Data engineering roles require even less math, and I’ll dare say none.

If you’re making a move towards data science, however, it’d be really good if you could have the fundamental understanding of probability theory, statistics, linear algebra and Calculus.

I had the “luck” (or curse) of learning linear algebra and calculus at uni when I was studying physics. We relied on heavy Russian books we didn’t understand but went very deep into the material.

Don’t worry, you won’t have to be an expert at calculus, unless you want to get into academia and do a heavy research on AI or any other STEM-related research.

That said, if you really want to get into the heavy math, I recommend this Coursera course that explores linear algebra and differential calculus which you can use for data science.

So, let’s get started

Khan Academy

If you’re looking for a solid foundation in math without feeling overwhelmed, Khan Academy is a great place to start. They have free, beginner-friendly lessons on statistics, probability, linear algebra, and calculus, all explained in a way that actually makes sense.

But here’s the thing—you don’t need to learn everything at once. Instead, focus on what’s immediately useful for your data science journey:

• 🔹 Statistics & Probability – Essential for understanding distributions, hypothesis testing, and A/B testing in analytics.
• 🔹 Linear Algebra – Helps with vectors, matrices, and transformations, which are the backbone of machine learning models.
• 🔹 Calculus (Basics) – If you want to understand optimization techniques like gradient descent, some calculus knowledge will go a long way.

Introduction to Statistical Learning (ISLR)

An Introduction to Statistical Learning (ISLR) is one of the best books out there If you're looking for a solid introduction to statistical learning with applications in Python and R, This book covers essential concepts in machine learning, including:

• Linear regression and classification
• Resampling methods like cross-validation and bootstrapping
• Model selection and regularization (Ridge, Lasso)
• Tree-based methods (Decision Trees, Random Forests, Boosting)
• Support Vector Machines and Unsupervised learning
• 
  

The Python and R editions provide hands-on code examples, making it easy to apply what you learn. If you’re serious about understanding the math behind machine learning while getting practical experience, ISLR is a must-read.

You can find the free PDF version here if you want to dive in. Let me know if you need recommendations on how to approach the book or which topics to focus on first.

Probability & Statistics for Machine Learning & Data Science Deeplearning.ai

Understanding probability and statistics is key to building better machine learning models. DeepLearning.AI’s course breaks down these concepts with a hands-on approach, making them easy to apply in real-world scenarios.

Here’s what you’ll learn:

• Descriptive statistics – How to summarize and visualize data effectively
• Probability distributions – Normal, binomial, and Poisson distributions explained
• Bayesian thinking – Learn how prior, likelihood, and posterior probabilities work
• Statistical inference – Confidence intervals, hypothesis testing, and p-values
• Regression & correlation – Identifying relationships in data
• ML applications – Using probability in Naïve Bayes classifiers and probabilistic modeling

This course is perfect for anyone looking to strengthen their statistical foundation and apply it to data science and AI. Find it here.

Statistics Bootcamp (with Python): Zero to Mastery

Zerotomastery Academy is one of my favorite resources for learning AI-related skills. I used it for Tensorflow, PyTorch and even HuggingFace projects. They also have a great course on Statistics.

This bootcamp takes you from the basics to advanced concepts, all while applying them in Python.

What you’ll learn:

• Descriptive Statistics – Mean, median, variance, and standard deviation
• Probability Distributions – Normal, binomial, and Poisson distributions
• Inferential Statistics – Hypothesis testing, confidence intervals, and p-values
• Regression Analysis – Linear and logistic regression with real-world examples
• Bayesian Statistics – Understanding probabilistic modeling
• Python Applications – Hands-on coding exercises using NumPy, Pandas, SciPy, and Statsmodels

This course is designed for beginners and professionals looking to apply statistics in data-driven decision-making and machine learning. You can access it here

These are the resources I used to learn math for data science, and so far, it was extremely helpful.

The Ultimate Data Science Roadmap

Given my journey with math in data science was very turbulent, which it is for many people getting started with data science, I added several chapters and sections of how to study probability and statistics, math and other important units including how to apply them in Python.

Try it out!

It’s packed with value and knowledge every aspiring data scientist should acquire when getting into data. Check the limited offer out and have the best week!

Yours sincerely, Danica