Statistics & Mathematics for Data Science & Data Analytics
- Description
- Curriculum
- FAQ
- Reviews

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?
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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
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This course is taught by an actual mathematician that is in the same time also working as a data scientist.
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This course is balancing both: theory & practical real-life example.
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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.
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29Intro
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30Probability Basics
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31Calculating Simple Probabilities
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32Practice: Simple Probabilities
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33Quick solution: Simple Probabilites
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34Detailed solution: Simple Probabilities
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35Rule of addition
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36Practice: Rule of addition
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37Quick solution: Rule of addition
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38Detailed solution: Rule of addition
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39Rule of multiplication
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40Practice: Rule of multiplication
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41Solution: Rule of multiplication
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42Bayes Theorem
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43Bayes Theorem - Practical example
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44Expected value
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45Practice: Expected value
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46Solution: Expected value
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47Law of Large Numbers
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48Central Limit Theorem - Theory
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49Central Limit Theorem - Intuition
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50Central Limit Theorem - Challenge
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51Central Limit Theorem - Exercise
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52Central Limit Theorem - Solution
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53Quiz: Bayes Theorem
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54Binomial distribution
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55Poisson distribtuion
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56Real life problems
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57Intro
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58What is an hypothesis?
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59Significance level and p-value
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60Type I and Type II errors
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61Confidence intervals and margin of error
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62Excursion: Calculating sample size & power
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63Performing the hypothesis test
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64Practice: Hypothesis test
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65Solution: Hypothesis test
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66t-test and t-distribution
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67Proportion testing
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68Important p-z pairs
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69Quiz: Hypothesis Testing
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70Intro
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71Linear Regression
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72Correlation coefficient
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73Practice: Correlation
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74Solution: Correlation
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75Practice: Linear Regression
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76Solution: Linear Regression
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77Residual, MSE & MAE
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78Practice: MSE & MAE
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79Solution: MSE & MAE
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80Coefficient of determination
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81Root Mean Square Error
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82Practice: RMSE
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83Solution: RMSE
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84Quiz: Regression