Learn the statistics & probability for data science and business analysis 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.