Learning probability and statistics isn’t the first thing most aspiring data analysts and scientists tackle. But make no mistake: understanding the math is just as critical as understanding the programming!

Math can be intimidating, particularly if you haven’t spent much time studying probability and statistics before. Today, we’re happy to announce that we’re making learning probability and statistics for data science easier by launching a new *Probability Fundamentals* course that will serve as a more approachable entry point for learning the math needed to do data science work.

This course has been added to our Data Analyst in Python and Data Scientist in Python paths as the third in our statistics course series, but you don’t need to have completed the previous courses to dive in and start learning about probability.

It does assume intermediate Python knowledge, including familiarity with the `pandas`

library. If you’re working through one of our paths, you’ll already have completed all the prerequisites you need, but if you’re taking courses a-la-carte, you can fill in those gaps with our introductory Python and pandas courses here. Like our other statistics courses, it requires at least a basic subscription.

## What You’ll Learn in *Probability Fundamentals*

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.

## Why Learn Probability and Statistics?

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.

Ready to start learning probability for data science? If you already have an account and a basic subscription, click here to get started with the course.

Otherwise, you’ll want to start by signing up for a free account and perusing our subscription options and our other data science courses.

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Charlie is a student of data science, and also a content marketer at Dataquest. In his free time, he’s learning to mountain bike and making videos about it.