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This plan includes
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$29
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
Course Overview
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?
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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.
Pre-requisites
- 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
Target Audience
- 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
Curriculum 88 Lectures 08:49:04
-
Section 1 : Let's get Started
- 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
- 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
- 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
- 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
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Section 5 : Hypothesis Testing
- 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
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Section 6 : Regression
- 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
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Section 7 : Advance Regression and Machine Learning Algorithm
- 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
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Section 8 : ANOVA( Analysis of Variance)
- 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
Our learners work at
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Instructor
31839 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!
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