English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 10 Hours | 4.70 GB
Learn the statistics & probability for data science and business analysis
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.
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
Table of Contents
Let’s get started
1 Welcome!
2 What will you learn in this course
3 How can you get the most out of it
4 Download Formula cheat sheet
Descriptive statistics
5 Intro
6 Mean
7 Median
8 Mode
9 Skewness
10 Range & IQR
11 Sample vs. Population
12 Variance & Standard deviation
13 Impact of Scaling & Shifting
14 Statistical moments
Distributions
15 What is a distribution
16 Normal distribution
17 Z-Scores
18 More distributions
Probability theory
19 Intro
20 Probability Basics
21 Calculating simple Probabilities
22 Rule of addition
23 Rule of multiplication
24 Bayes Theorem
25 Bayes Theorem – Practical example
26 Expected value
27 Law of Large Numbers
28 Central Limit Theorem – Theory
29 Central Limit Theorem – Intuition
30 Central Limit Theorem – Challenge
31 Central Limit Theorem – Exercise
32 Central Limit Theorem – Solution
33 Binomial distribution
34 Poisson distribtuion
35 Real life problems
Hypothesis testing
36 Intro
37 What is an hypothesis
38 Significance level and p-value
39 Type I and Type II errors
40 Confidence intervals and margin of error
41 Excursion Calculating sample size & power
42 Performing the hypothesis test
43 t-test and t-distribution
44 Proportion testing
45 Important p-z pairs
Regressions
46 Intro
47 Linear Regression
48 Correlation coefficient
49 Residual, MSE & MAE
50 Coefficient of determination
51 Root Mean Square Error
Advanced regeression & machine learning alogorithms
52 Multiple Linear Regression
53 Overfitting
54 Polynomial Regression
55 Logistic Regression
56 Decision Trees
57 Regression Trees
58 Random Forests
59 Dealing with missing data
ANOVA (Analysis of Variance)
60 ANOVA Basics & Assumptions
61 One-way ANOVA
62 F-Distribution
63 Two-way ANOVA – Sum of Squares
64 Two-way ANOVA – F-ratio & conclusions
Wrap up
65 Wrap up
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