Statistics & Mathematics for Data Science & Data Analytics

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Description

Are you aiming for a career in Data Science or Data Analytics?

Good news, you don't need a Maths degree - this course is equipping you with the practical knowledge needed to master the necessary statistics.

It is very important if you want to become a Data Scientist or a Data Analyst to have a good knowledge in statistics & probability theory.

Sure, there is more to Data Science than only statistics. But still it plays an essential role to know these fundamentals ins statistics.

I know it is very hard to gain a strong foothold in these concepts just by yourself. Therefore I have created this course.

Why should you take this course?

This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with data

This course is taught by an actual mathematician that is in the same time also working as a data scientist.

This course is balancing both: theory & practical real-life example.

After completing this course you ll have everything you need to master the fundamentals in statistics & probability need in data science or data analysis.

What is in this course?

This course is giving you the chance to systematically master the core concepts in statistics & probability, descriptive statistics, hypothesis testing, regression analysis, analysis of variance and some advance regression / machine learning methods such as logistics regressions, polynomial regressions , decision trees and more.

In real-life examples you will learn the stats knowledge needed in a data scientist's or data analyst's career very quickly.

If you feel like this sounds good to you, then take this chance to improve your skills und advance career by enrolling in this course.

Who this course is for:

• Anybody that wants to master statistics & probabilities for data science & data analysis
• Anybody who wants to pursue a career in Data Science
• Professionals and students who want to understand the necessary statistics for data analysis