What statistics should I learn for data science?

According to Elite Data Science, a data science educational platform, data scientists need to understand the fundamental concepts of descriptive statistics and probability theory, which include the key concepts of probability distribution, statistical significance, hypothesis testing and regression.

Should I learn statistics before data science?

Both tasks require statistical knowledge so it is a must-have skill for data scientists. Data science is an interdisciplinary field. Statistics is an integral part and an absolute requirement for data scientists. Without a decent level of statistical knowledge, we can only be a tool expert.

How long does it take to learn statistics for data science?

On average, it takes approximately 6 to 7 months for an individual to become moderately proficient in the field of data science. However, by having a well-structured and thought through plan, and by committing yourself to it, you can considerably expedite this learning process and timeline.

How do I get started in data science?

How to launch your data science career

1. Step 0: Figure out what you need to learn.
2. Step 1: Get comfortable with Python.
3. Step 2: Learn data analysis, manipulation, and visualization with pandas.
4. Step 3: Learn machine learning with scikit-learn.
5. Step 4: Understand machine learning in more depth.

How do I start learning statistics to become a data scientist?

If you’re interested in learning statistics specifically to become a data scientist, I would recommend seeking as many coding applications as you can. Specifically, I’d recommend the following three books, all available online for free and containing applications in Python or R.

Is head first statistics a good book to learn statistics?

Statistics is a mandatory tool for every data scientist — no arguing there. At the same time, your formal education on statistics might have sucked, or it never existed. That doesn’t mean you can’t learn the topic, it will just require more manual work. And that’s where books like Head First Statistics come in handy.

Is it possible to learn statistics by yourself?

If you know how to program, then you can use that skill to teach yourself statistics. We’ve found this approach to be very effective, even for those with formal math backgrounds. One of the philosophical debates in statistics is between Bayesians and frequentists . The Bayesian side is more relevant when learning statistics for data science.

Do you need a math background to be a data scientist?

If you do have a formal math background, this approach will help you translate theory into practice and give you some fun programming challenges. Here are the 3 steps to learning the statistics and probability required for data science: Descriptive statistics, distributions, hypothesis testing, and regression.