6
Lessons
50+
Videos
Beginner
Skill Level
15h
Duration
English
Language
Overview
This self-paced, fully online course will take you from 0 knowledge to analyzing datasets in no time, without sacrificing understanding!
The course is compact, practical, and straight to the point.
I’ve carefully curated the content and refined my approach over many years, drawing from my university lectures, and (if I can say so without sounding pretentious) I really believe in this material.
The material will include videos, podcast style conversations, slides, code notebooks in R and Python, exercises, and references to books, blogs, and other courses to help you go further, when appropriate.
Completing this course will help you:
- Understand different data types
- Choose impactful visualizations for the data
- Choose suitable statistical models and tests to make clear statistical inferences
- Analyze real-world datasets in a meaningful way
- Go beyond cookbook recipes and rote applications of statistics, by understanding deeper concepts related to inference, causality, and the nature of evidence.
Who is the course for?
This course assumes no background knowledge and requires only basic high-school level mathematics.
It is designed for science and industry, focusing on clarity, rigour, and practical aspects of statistical analyses.
About the instructor
I’ve been teaching statistics in 2 world-class universities for many years, where I consistently receive exceptional student evaluations : students value my patience, clear style of exposition, empathy, and passion for the subject.
I’ve also been a consultant in projects all over the world, in business and academia.
This unique blend of pedagogy, diverse experience, and hands-on work will permeate all throughout the course.
I want you to learn statistics the right way, how I wish it was taught to me!
Learn Statistics The Right WAY
- Give you tools to start working independently right from the start
- Draw on my wealth on real-world experience to give you examples and insights you can't find in any traditional curriculum
- Avoid unnecessary complications, yet don't shy away from teaching things in a way that you will truly understand
- Point you, the learner, towards more resources so you meet more advanced material when you're ready!
My Misson?
To elevate statistics education and practice.
This means, no dumbing down of content : I will elevate you, the learner, to meet the material at the right time for you to master it!
Ready to become a statistics expert?
Learning Path
- Causality, AI, Data Science, and Statistics
- The role of the (bio)statistician in 2025
- My Teaching Philosophy and Pedagogical Approach
- Data types (quantitative, qualitative)
- Mean, median
- Variance, standard-deviation
- Quantiles and percentiles
- Tufte’s principles for effective visualization
- Histograms, boxplots, advanced plots, and combining plots for maximum information
- Probability density functions (pdf) and cumulative density functions (cdf)
- Poisson and exponential distributions
- Normal distribution, Central Limit Theorem (CLT), and Student-t distribution
- Gamma and log-normal distributions
- Uniform and beta distributions
- Binomial, multinomial, and ordinal distributions
- Khi-square and Fisher distributions
- Theoretical vs. Empirical probability distributions
- Testing Hypotheses: The Logic of Statistical Inference
- Causality and Hypothesis Testing
- Expected values, Estimators, and Standard-errors
- Hypothesis testing examples and advanced concepts
- Joint distribution, conditional distribution, and Bayes Theorem
- Covariance, Correlation, and Variance
- Line-of-best-fit
- Linear Regression with Categorical Variables
- Linear Regression, ANOVA, and ANCOVA
- Logistic Regression vs. Linear Regression
- Logistic Regression
- Generalized Linear Models (GLMs) and Conditional Expectation
- Estimation, Inference, and Prediction
- Capstone Project
- Looking Ahead: mixed effects, study design and protocol writing, multiplicity problem, missing data, quantile regression, smoothing regression, soft skills, etc.
- Causal Inference,
- Communities, Job Placements
What people are saying
Are You READY TO LEARN STATISTICS THE RIGHT WAY?
Frequently Asked Questions
- Beginners with no prior knowledge of statistics.
- Anyone with a basic high-school level math background.
- Science professionals and industry practitioners looking to improve their data analysis skills.
- Understanding different types of data.
- Selecting impactful visualizations for data.
- Applying statistical models and tests for clear inferences.
- Analyzing real-world datasets in a meaningful way.
- Compact and straight to the point, without sacrificing understanding.
- Goes beyond typical Stats 101 courses to explore deeper ideas related to inference and experimentation, causality, and the nature of evidence.
- Emphasizes practical applications with real-world datasets.
- Includes curated resources like videos, slides, code notebooks, and exercises.
- Taught by an experienced instructor with a blend of academic and industry expertise.
- No prior knowledge of statistics or programming is required.
- Basic understanding of high-school level mathematics.
- Video lectures.
- Podcast-style conversations between Justin Belair and Aleksander Molak on deep ideas
- Slide decks.
- Code notebooks and datasets for hands-on learning.
- Exercises to practice your skills.
- References to books, blogs, and other advanced resources.
The course consists of 10 lessons:
-
- Introduction
- Describing Data
- Visualizing Data
- Probability Distributions
- Statistical Hypothesis Testing
- Modelling Relationships Between Variables
- Capstone Project, Discussing Next Steps, and Career Prospects
- The course is self-paced, so you can learn at your own speed.
- Most learners complete the course within a few weeks.
- Access to R and RStudio and/or Python and Jupyter notebooks is recommended (free and easy to install, and you can use a Cloud web version at no cost).
- No prior coding experience is required—R and Python code will be explained step-by-step.
- While this is a self-paced course, you will engage with:
- Exercises for practice
- Hands-on code notebooks
- Real-world datasets for analysis
- Forums to exchange with instructors and students
- Yes, you will receive a certificate upon completing the course.
- The course includes dedicated support channels where you can ask questions.
- Additional community resources will be available for discussion and collaboration.
- Special discounted pricing for pre-ordering the course before its official launch.
- Unfortunately, no free trial is available, but I do provide a no questions-asked 30-day money-back guarantee!