Learn about the importance of statistics in data science, about probability, distribution and other principles of a statistical data analysis

Understand the fundamentals of statistics

Understand the probability in data analysis

Learn how to work with different types of data

Different types of distributions

Apply statistical methods and hypothesis testing to business problems

You have probably encountered statistics at least once in your life. By definition, statistics refer to the processes of collecting, arranging, analyzing, and visualizing data. Usually, statistics include large amounts of data that can be used for various purposes from making predictions in the market to revealing the current tendencies. In this course on statistics for data science, you will learn that statistics are highly relevant for any data scientist.

Statistics usually include a great deal of calculations of numbers and use mathematical formulas. However, these calculations typically have a purpose of answering relevant questions.

For instance, data scientists can take advantage of a large collection of data to figure out the correlations between tendencies. One of the main parts of statistical data analysis is to interpret data. Therefore, data is then applied for answering questions or solving issues. Proper and efficient data analysis often leads to successful results.

This course on statistics for data science is beginner-friendly since it provides the fundamentals of statistics that you will need to know for the other lessons. In order to become a data scientist, you need to learn statistics first.

Data scientists are specialists that have skills and knowledge from various spheres. There is even a simple recipe for creating a data scientist that uses statistics for data science. Combine a mathematician, a tech-savvy scientist, and an overall curious person. Mix these ingredients, and you get a data scientist. Another popular question is what data scientists do in their job. You can say that they perform statistical data analysis, but let’s take a closer look at their typical tasks.

Data scientists collect large amounts of raw data and attempt to present it in understandable formats. In many cases, data scientists also have some programming skills (at least with Python). Additionally, they aim to keep track of the newest machine learning and deep learning strategies to simplify their statistical data analysis.

My course on statistics for data science begins with an intro to statistics. Without understanding this concept, you won't be able to proceed with the following lessons that will go deeper into the data science sphere.

I will also explain the principle of probability. In every intro to statistics class, you will learn that probability is a number representing the likelihood of something happening. In this course, I will go into detail when explaining this principle, starting from the basics and ending with Bayes’ Theorem.

In my course, I also go over the various types of probability distribution: uniform, binomial, and poisson. I will also explain the Central limit theorem, which is also a part of the probability theory. As you might have understood, the probability is one of the main principles for people who want to learn statistics. Next, I will turn to hypothesis testing that refers to the attempts to prove a specific prediction through statistical data analysis.

Finally, after completing this statistics for data science course, you will know the basics of statistics and the concepts that are common in statistical modeling. So, join me on this journey of learning about the impact of professional statistical analysis!

Is the online course "Statistics for Data Science: Intro to Statistics & Data Analysis" free?

Is "Statistics for Data Science: Intro to Statistics & Data Analysis" an online course worth taking?