As you might have guessed from the title, Probability Fundamentals is designed to give you a working understanding of critical concepts in probability that are relevant to the everyday work of data analysis and data science.
Like all Dataquest courses, you’ll work through this course in your web browser, writing code to apply what you’re learning every step of the way.
Working through the course, you’ll use your Python programming skills and the statistics knowledge you’re learning to estimate empirical and theoretical probabilities. You’ll learn the fundamental rules of probability, and then work to solve increasingly complex probability problems.
Finally, you’ll learn about counting techniques like permutations and combinations before synthesizing all your new knowledge in a guided project building the logic for a mobile app that helps gambling addicts more accurately estimate lottery odds to help them overcome their addiction.
By the end of the course, you’ll understand the difference between theoretical and experimental probability. You’ll have experience calculating the probabilities for a variety of different events, and you’ll be able to calculate the number of permutations and combinations possible in experiment outcomes.
Although a lot of data science work is experienced as programming, almost everything that data scientists do involves working with statistics. When data scientists make predictions, they’re dealing with probabilities. The concept of probability might seem basic, but it’s the foundation for even the most advanced predictive models.
And while the actual mathematical operations are often baked into popular data science libraries for quick application, this convenience can be a double-edged sword. Just because a technique is easy to apply, after all, doesn’t mean that it’s correct to apply in every circumstance.
That’s why learning probability and statistics concepts, including those covered in this course, is so important for data scientists. When you understand the why, it becomes much easier for you to identify the correct statistical technique or calculation for the problem you’re trying to solve.
It also becomes easier to explain your analysis to others when you have a firm grasp of why you used the technique you chose.
For this project, we’ll work with a medical institute to build a mobile app that helps lottery addicts estimate their odds of winning. Using Python and probability skills, we’ll analyze Canada’s 6/49 lottery as a case study.
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