Statistics and probability control your life. I don’t just mean What YouTube’s algorithm recommends you to watch next, and I don’t just mean the chance of meeting your future significant other in class or at a bar. Human behavior, single-cell organisms, Earthquakes, the stock market, whether it will snow in the first week of December, and countless other phenomena are probabilistic and statistical. Even the very nature of the most fundamental deep structure of the universe is governed by probability and statistics.
You need to understand statistics.
Nearly all areas of human civilization are incorporating code and numerical computations. This means that many jobs and areas of study are based on applications of statistical and machine-learning techniques in programming languages like Python and MATLAB. This is often called ‘data science’ and is an increasingly important topic. Statistics and machine learning are also fundamental to artificial intelligence (AI) and business intelligence.
If you want to make yourself a future-proof employee, employer, data scientist, or researcher in any technical field — ranging from data scientist to engineering to research scientist to deep learning modeler — you’ll need to know statistics and machine-learning. And you’ll need to know how to implement concepts like probability theory and confidence intervals, k-means clustering and PCA, Spearman correlation and logistic regression, in computer languages like Python or MATLAB.
There are six reasons why you should take this course:
- This course covers everything you need to understand the fundamentals of statistics, machine learning, and data science, from bar plots to ANOVAs, regression to k-means, t-test to non-parametric permutation testing.
- After completing this course, you will be able to understand a wide range of statistical and machine-learning analyses, even specific advanced methods that aren’t taught here. That’s because you will learn the foundations upon which advanced methods are build.
- This course balances mathematical rigor with intuitive explanations, and hands-on explorations in code.
- Enrolling in the course gives you access to the Q&A, in which I actively participate every day.
- I’ve been studying, developing, and teaching statistics for over 20 years, and I think math is, like, really cool.
What you need to know before taking this course:
- High-school level maths. This is an applications-oriented course, so I don’t go into a lot of detail about proofs, derivations, or calculus.
- Basic coding skills in Python or MATLAB. This is necessary only if you want to follow along with the code. You can successfully complete this course without writing a single line of code! But participating in the coding exercises will help you learn the material. The MATLAB code relies on the Statistics and Machine Learning toolbox (you can use Octave if you don’t have MATLAB or the statistics toolbox). Python code is written in Jupyter notebooks.
- I recommend taking my free course called “Statistics literacy for non-statisticians”. It’s 90 minutes long and will give you a bird’s-eye-view of the main topics in statistics that I go into much much much more detail about here in this course. Note that the free short course is not required for this course, but complements this course nicely. And you can get through the whole thing in less than an hour if you watch if on 1.5x speed!
- You do not need any previous experience with statistics, machine learning, deep learning, or data science. That’s why you’re here!
