Mô tả

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.

Bạn sẽ học được gì

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

Yêu cầu

  • 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

Nội dung khoá học

9 sections

Let's get started

4 lectures
Welcome!
02:05
What will you learn in this course?
05:50
How can you get the most out of it?
05:49
Download: Formula cheat sheet
00:04

Descriptive statistics

17 lectures
Intro
02:37
Mean
06:00
Quiz: Mean
1 question
Median
04:57
Quiz: Median
1 question
Mode
03:42
Quiz: Mode
1 question
Mean or Median?
07:30
Skewness
08:22
Practice: Skewness
01:19
Solution: Skewness
03:25
Range & IQR
09:40
Sample vs. Population
05:01
Variance & Standard deviation
10:39
Quiz: Variance
1 question
Impact of Scaling & Shifting
18:33
Statistical moments
05:48

Distributions

7 lectures
What is a distribution?
09:52
Normal distribution
08:53
Z-Scores
12:46
Practise: Normal distribution
03:42
Solution: Normal distribution
07:12
Normal distribution
2 questions
More distributions
00:07

Probability theory

28 lectures
Intro
00:50
Probability Basics
09:47
Calculating Simple Probabilities
05:20
Practice: Simple Probabilities
01:23
Quick solution: Simple Probabilites
00:39
Detailed solution: Simple Probabilities
06:11
Rule of addition
12:45
Practice: Rule of addition
02:20
Quick solution: Rule of addition
00:55
Detailed solution: Rule of addition
07:20
Rule of multiplication
10:50
Practice: Rule of multiplication
00:39
Solution: Rule of multiplication
03:19
Bayes Theorem
09:37
Bayes Theorem - Practical example
06:42
Expected value
10:49
Practice: Expected value
01:07
Solution: Expected value
02:44
Law of Large Numbers
07:53
Central Limit Theorem - Theory
10:07
Central Limit Theorem - Intuition
07:31
Central Limit Theorem - Challenge
10:55
Central Limit Theorem - Exercise
01:49
Central Limit Theorem - Solution
14:12
Quiz: Bayes Theorem
3 questions
Binomial distribution
15:49
Poisson distribtuion
16:37
Real life problems
15:29

Hypothesis testing

13 lectures
Intro
01:12
What is an hypothesis?
18:50
Significance level and p-value
06:12
Type I and Type II errors
05:02
Confidence intervals and margin of error
14:46
Excursion: Calculating sample size & power
10:55
Performing the hypothesis test
19:38
Practice: Hypothesis test
01:19
Solution: Hypothesis test
05:35
t-test and t-distribution
13:29
Proportion testing
10:03
Important p-z pairs
08:09
Quiz: Hypothesis Testing
3 questions

Regressions

15 lectures
Intro
02:06
Linear Regression
10:46
Correlation coefficient
10:08
Practice: Correlation
01:46
Solution: Correlation
07:32
Practice: Linear Regression
00:31
Solution: Linear Regression
06:36
Residual, MSE & MAE
07:32
Practice: MSE & MAE
00:52
Solution: MSE & MAE
03:19
Coefficient of determination
12:16
Root Mean Square Error
06:24
Practice: RMSE
01:00
Solution: RMSE
02:08
Quiz: Regression
1 question

Advanced regression & machine learning algorithms

8 lectures
Multiple Linear Regression
16:02
Overfitting
05:19
Polynomial Regression
13:05
Logistic Regression
09:27
Decision Trees
21:06
Regression Trees
14:21
Random Forests
12:38
Dealing with missing data
10:16

ANOVA (Analysis of Variance)

6 lectures
ANOVA - Basics & Assumptions
05:31
One-way ANOVA
12:25
F-Distribution
10:19
Two-way ANOVA – Sum of Squares
15:44
Two-way ANOVA – F-ratio & conclusions
11:24
Quiz: ANOVA
4 questions

Wrap up

2 lectures
Wrap up
00:40
Bonus lecture
00:54

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