Video description
If you aim for a career in data science or data analytics, this course will equip you with the practical knowledge needed to master basic statistics. You need good statistics and probability theory knowledge to become a data scientist or analyst.
The course begins with an introduction to descriptive statistics and explains the basics, including the mean, median, mode, and skewness. You will then learn more about ranges, interquartile range (IQR), samples and populations, variance, and standard deviation. The following section will explain distributions in detail, including normal distribution and Zscores. Then, you will explore probability in detail, go over the Bayes theorem, the Central Limit theorem, the law of large numbers, and finally, Poisson’s distribution. Next, you will comprehensively explore linear regression and the coefficients of regression, mean square error, mean absolute error, and root mean square error.
You will also explore hypothesis testing and type I and II errors in more detail and then learn comprehensively about the analysis of variance (ANOVA).
After completing this course, you will comprehensively acquire knowledge about statistical fundamentals, data analysis methods, decisionmaking processes, and machine learning concepts with examples.
What You Will Learn
 Master basic statistics, descriptive statistics, and probability theory
 Explore ML methods, including decision trees and decision forests
 Learn probability distributions normal and Poisson distributions
 Explore hypothesis testing, pvalues, types I and II error handling
 Master logistic regression, linear regression, and regression trees
 Learn correlation, RSquare, RMSE, MAE, and coefficient of determination
Audience
This beginnerlevel course has been niched to cater to an individual looking to master statistics and probability for data science and analysis, an individual looking to pursue a career in data science, or professionals and students wanting to understand statistics for data analysis. The prerequisites for this course include absolutely no previous experience required and an eagerness and motivation to learn.
About The Author
Nikolai Schuler: Nikolai Schuler, as a data scientist and BI consultant, believes that the data world benefits from new tools and technologies, but it is extremely difficult to get trained in the field as practical courses with quality content are rare or are structured incompatible with a busy working life.
Nikolai’s courses offer precious content and have an easytofollow structure. He aims to help anyone wishing to pursue their desired career by upgrading their data analysis skills. His courses have already found their audience in over 170 countries with numerous positive feedback and will equip you with the skillsets to master data science and analytics! If you are looking for qualitatively approachable training, then jump on board!
Table of contents
 Chapter 1 : Let's Get Started
 Chapter 2 : Descriptive Statistics
 Chapter 3 : Distributions

Chapter 4 : Probability Theory
 Introduction
 Probability Basics
 Calculating Simple Probabilities
 Practice: Simple Probabilities
 Quick Solution: Simple Probabilities
 Detailed Solution: Simple Probabilities
 Rule of Addition
 Practice: Rule of Addition
 Quick Solution: Rule of Addition
 Detailed Solution: Rule of Addition
 Rule of Multiplication
 Practice: Rule of Multiplication
 Solution: Rule of Multiplication
 Bayes Theorem
 Bayes Theorem  Practical Example
 Expected Value
 Practice: Expected Value
 Solution: Expected Value
 Law of Large Numbers
 Central Limit Theorem  Theory
 Central Limit Theorem  Intuition
 Central Limit Theorem  Challenge
 Central Limit Theorem  Exercise
 Central Limit Theorem  Solution
 Binomial Distribution
 Poisson Distribution
 RealLife Problems

Chapter 5 : Hypothesis Testing
 Introduction
 What Is a Hypothesis?
 Significance Level and PValue
 Type I and Type II Errors
 Confidence Intervals and Margin of Error
 Excursion: Calculating Sample Size and Power
 Performing the Hypothesis Test
 Practice: Hypothesis Test
 Solution: Hypothesis Test
 ttest and tdistribution
 Proportion Testing
 Important pz Pairs

Chapter 6 : Regressions
 Introduction
 Linear Regression
 Correlation Coefficient
 Practice: Correlation
 Solution: Correlation
 Practice: Linear Regression
 Solution: Linear Regression
 Residual, MSE, and MAE
 Practice: MSE and MAE
 Solution: MSE and MAE
 Coefficient of Determination
 Root Mean Square Error
 Practice: RMSE
 Solution: RMSE
 Chapter 7 : Advanced Regression and Machine Learning Algorithms
 Chapter 8 : ANOVA (Analysis of Variance)
 Chapter 9 : Wrap Up
Product information
 Title: Statistics and Mathematics for Data Science and Data Analytics
 Author(s):
 Release date: January 2023
 Publisher(s): Packt Publishing
 ISBN: 9781837632336
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