Statistics & Mathematics for Data Science & Data Analytics

What you’ll learn

• Master the fundamentals of statistics for data science & data analytics
• Master descriptive statistics & probability theory
• Machine learning methods like Decision Trees and Decision Forests
• Probability distributions such as Normal distribution, Poisson Distribution and more
• Hypothesis testing, p-value, type I & type II error
• Logistic Regressions, Multiple Linear Regression, Regression Trees
• Correlation, R-Square, RMSE, MAE, coefficient of determination and more

Requirements

• Absolutely no previous experience required. We will learn everything right from the basics and then work our way up step by step
• Eagerness and motivation to learn

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.

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