Statistics & Probability for Data Science & Machine Learning

Learn the statistics & probability for data science and business analysis

Created by : Rated :4.6 21129 views

Last Updated: 2022-05-04 04:05:57 English Course Type: Self Learning

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

Are you aiming for a career in Data Science or Data Analytics?

Good news, you dFon'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.

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

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

Section 1 : Let's get Started 4 Lectures 00:11:39
Lecture 1 :
Welcome Preview
Lecture 2 :
What will you learn in this course
Lecture 3 :
How to get the most out of this course
Lecture 4 :
Resources
Section 2 : Descriptive Statistics 13 Lectures 00:48:23
Lecture 1 :
Descriptive Statistics intro
Lecture 2 :
Mean
Lecture 3 :
Median
Lecture 4 :
Mode
Lecture 5 :
Mean or Median?
Lecture 6 :
Skewness
Lecture 7 :
Practice: Skewness
Lecture 8 :
Solution: Skewness
Lecture 9 :
Range & IQR
Lecture 10 :
Sample vs. Population
Lecture 11 :
Variance & Standard deviation
Lecture 12 :
Impact of Scaling & Shifting
Lecture 13 :
Statistical Moments (+Screenshot attached)
Section 3 : Distribution 5 Lectures 00:42:25
Lecture 1 :
Practise: Normal distribution
Lecture 2 :
Z-Scores
Lecture 3 :
Normal distribution
Lecture 4 :
What is a distribution?
Lecture 5 :
Solution: Normal distribution
Section 4 : Probability Theory 27 Lectures 03:13:39
Lecture 1 :
Introduction
Lecture 2 :
Probability Basics
Lecture 3 :
Calculating simple Probabilities
Lecture 4 :
Practice: Simple Probabilities
Lecture 5 :
Quick solution: Simple Probabilites
Lecture 6 :
Detailed solution: Simple Probabilities
Lecture 7 :
Rule of addition
Lecture 8 :
Practice: Rule of addition
Lecture 9 :
Quick solution: Rule of addition
Lecture 10 :
Detailed solution: Rule of addition
Lecture 11 :
Rule of multiplication
Lecture 12 :
Practice: Rule of multiplication
Lecture 13 :
Solution: Rule of multiplication
Lecture 14 :
Bayes Theorem
Lecture 15 :
Bayes Theorem - Practical example
Lecture 16 :
Expected value
Lecture 17 :
Practice: Expected value
Lecture 18 :
Solution: Expected value
Lecture 19 :
Law of Large Numbers
Lecture 20 :
Central Limit Theorem - Theory
Lecture 21 :
Central Limit Theorem - Intuition
Lecture 22 :
Central Limit Theorem - Challenge
Lecture 23 :
Central Limit Theorem - Exercise
Lecture 24 :
Central Limit Theorem - Solution
Lecture 25 :
Binomial distribution
Lecture 26 :
Poisson distribtuion
Lecture 27 :
Real life problems
Section 5 : Hypothesis Testing 12 Lectures 01:16:44
Lecture 1 :
Hypothesis introduction
Lecture 2 :
What is an hypothesis?
Lecture 3 :
Significance level and p-value
Lecture 4 :
Type I and Type II errors
Lecture 5 :
Confidence intervals and margin of error
Lecture 6 :
Excursion: Calculating sample size & power
Lecture 7 :
Performing the hypothesis test
Lecture 8 :
Practice: Hypothesis test
Lecture 9 :
Solution: Hypothesis test
Lecture 10 :
t-test and t-distribution
Lecture 11 :
Proportion testing
Lecture 12 :
Important p-z pairs
Section 6 : Regression 14 Lectures 01:00:40
Lecture 1 :
Regression Introduction
Lecture 2 :
Linear Regression
Lecture 3 :
Correlation coefficient
Lecture 4 :
Practice: Correlation
Lecture 5 :
Solution: Correlation
Lecture 6 :
Practice: Linear Regression
Lecture 7 :
Solution: Linear Regression
Lecture 8 :
Residual, MSE & MAE
Lecture 9 :
Practice: MSE & MAE
Lecture 10 :
Solution: MSE & MAE
Lecture 11 :
Coefficient of determination
Lecture 12 :
Root Mean Square Error
Lecture 13 :
Practice: RMSE
Lecture 14 :
Solution: RMSE
Section 7 : Advance Regression and Machine Learning Algorithm 8 Lectures 01:01:26
Lecture 1 :
Multiple Linear Regression
Lecture 2 :
Overfitting
Lecture 3 :
Polynomial Regression
Lecture 4 :
Logistic Regression
Lecture 5 :
Decision Trees
Lecture 6 :
Regression Trees
Lecture 7 :
Random Forests
Lecture 8 :
Dealing with missing data
Section 8 : ANOVA( Analysis of Variance) 5 Lectures 00:34:08
Lecture 1 :
ANOVA - Basics & Assumptions
Lecture 2 :
One-way ANOVA
Lecture 3 :
F-Distribution
Lecture 4 :
Two-way ANOVA – Sum of Squares
Lecture 5 :
Two-way ANOVA – F-ratio & conclusions

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Nikolai Schuler ,

33918 Course Views

2 Courses

I am a mathematician working as a data scientist and BI consultant. I am helping people all over the world with the tools and skills of data science and business intelligence such as Power BI and Statistics. I found it not that easy to find courses that are high quality and easy to understand in the same time. Therefore I decided to create these courses that are high quality on the one hand and easy to learn with on the other hand. These courses now have reached people in more than 100 countries and I am really glad to help spreading these IT skills and enable people to pursue the career they are after. As I am a data specialist on the one hand but also introducing ordinary users to Power BI and other tools on the other hand, I understand the common hurdels most people have. Data and teaching are my passions and therefore I am really looking forward to level up your IT skills as well!