Top 8 Recommended Statistics Self-Study Materials! [December 2023]

Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist?

And you want to acquire the quantitative skills needed for the job?

Well then, you’ve come to the right place!   

Statistics for Data Science and Business Analysis is here for you! (with TEMPLATES in Excel included)   

This is where you start. And it is the perfect beginning!  

In no time, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. We have created a course that is:   

• Easy to understand
  
   

• Comprehensive
  
   

• Practical
  
   

• To the point
  
   

• Packed with plenty of exercises and resources   

• Data-driven
  
   

• Introduces you to the statistical scientific lingo
  
   

• Teaches you about data visualization
  
   

• Shows you the main pillars of quant research
  
   

It is no secret that a lot of these topics have been explained online. Thousands of times. However, it is next to impossible to find a structured program that gives you an understanding of why certain statistical tests are being used so often. Modern software packages and programming languages are automating most of these activities, but this course gives you something more valuable – critical thinking abilities. Computers and programming languages are like ships at sea. They are fine vessels that will carry you to the desired destination, but it is up to you, the aspiring data scientist or BI analyst, to navigate and point them in the right direction.   

Teaching is our passion

 

We worked full-time for several months to create the best possible Statistics course, which would deliver the most value to you. We want you to succeed, which is why the course aims to be as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts and course notes, as well as a glossary with all new terms you will learn, are just some of the perks you will get by subscribing.   

What makes this course different from the rest of the Statistics courses out there?

 

• High-quality production – HD video and animations (This isn’t a collection of boring lectures!)   

• Knowledgeable instructor (An adept mathematician and statistician who has competed at an international level)   

• Complete training – we will cover all major statistical topics and skills you need to become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist  

• Extensive Case Studies that will help you reinforce everything you’ve learned  

• Excellent support - if you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day  

• Dynamic - we don’t want to waste your time! The instructor sets a very good pace throughout the whole course

Why do you need these skills?

 

1. Salary/Income – careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow    

2. Promotions – If you understand Statistics well, you will be able to back up your business ideas with quantitative evidence, which is an easy path to career growth  

3. Secure Future – as we said, the demand for people who understand numbers and data, and can interpret it, is growing exponentially; you’ve probably heard of the number of jobs that will be automated soon, right? Well, data science careers are the ones doing the automating, not getting automated

4. Growth - this isn’t a boring job. Every day, you will face different challenges that will test your existing skills and require you to learn something new   

Please bear in mind that the course comes with Udemy’s 30-day unconditional money-back guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you.

 

Click 'Buy now' and let's start learning together today!

 

1. Introduction
2. What does the course cover?
3. Download all resources
4. Sample or population data?
5. Understanding the difference between a population and a sample
6. Population vs sample
7. The fundamentals of descriptive statistics
8. The various types of data we can work with
9. Types of data
10. Levels of measurement
11. Levels of measurement
12. Categorical variables. Visualization techniques for categorical variables
13. Categorical variables. Visualization Techniques
14. Categorical variables. Visualization techniques. Exercise
15. Numerical variables. Using a frequency distribution table
16. Numerical variables. Using a frequency distribution table
17. Numerical variables. Using a frequency distribution table. Exercise
18. Histogram charts
19. Histogram charts
20. Histogram charts. Exercise
21. Cross tables and scatter plots
22. Cross Tables and Scatter Plots
23. Cross tables and scatter plots. Exercise
24. Measures of central tendency, asymmetry, and variability
25. The main measures of central tendency: mean, median and mode
26. Mean, median and mode. Exercise
27. Measuring skewness
28. Skewness
29. Skewness. Exercise
30. Measuring how data is spread out: calculating variance
31. Variance. Exercise
32. Standard deviation and coefficient of variation
33. Standard deviation
34. Standard deviation and coefficient of variation. Exercise
35. Calculating and understanding covariance
36. Covariance. Exercise
37. The correlation coefficient
38. Correlation
39. Correlation coefficient
40. Practical example: descriptive statistics
41. Practical example
42. Practical example: descriptive statistics
43. Distributions
44. Introduction to inferential statistics
45. What is a distribution?
46. What is a distribution
47. The Normal distribution
48. The Normal distribution
49. The standard normal distribution
50. The standard normal distribution
51. Standard Normal Distribution. Exercise
52. Understanding the central limit theorem
53. The central limit theorem
54. Standard error
55. Standard error
56. Estimators and estimates
57. Working with estimators and estimates
58. Estimators and estimates
59. Confidence intervals - an invaluable tool for decision making
60. Confidence intervals
61. Calculating confidence intervals within a population with a known variance
62. Confidence intervals. Population variance known. Exercise
63. Confidence interval clarifications
64. Student's T distribution
65. Student's T distribution
66. Calculating confidence intervals within a population with an unknown variance
67. Population variance unknown. T-score. Exercise
68. What is a margin of error and why is it important in Statistics?
69. Margin of error
70. Confidence intervals: advanced topics
71. Calculating confidence intervals for two means with dependent samples
72. Confidence intervals. Two means. Dependent samples. Exercise
73. Calculating confidence intervals for two means with independent samples (part 1)
74. Confidence intervals. Two means. Independent samples (Part 1). Exercise
75. Calculating confidence intervals for two means with independent samples (part 2)
76. Confidence intervals. Two means. Independent samples (Part 2). Exercise
77. Calculating confidence intervals for two means with independent samples (part 3)
78. Practical example: inferential statistics
79. Practical example: inferential statistics
80. Practical example: inferential statistics
81. Hypothesis testing: Introduction
82. The null and the alternative hypothesis
83. Further reading on null and alternative hypotheses
84. Null vs alternative
85. Establishing a rejection region and a significance level
86. Rejection region and significance level
87. Type I error vs Type II error
88. Type I error vs type II error
89. Hypothesis testing: Let's start testing!
90. Test for the mean. Population variance known
91. Test for the mean. Population variance known. Exercise
92. What is the p-value and why is it one of the most useful tools for statisticians
93. p-value
94. Test for the mean. Population variance unknown
95. Test for the mean. Population variance unknown. Exercise
96. Test for the mean. Dependent samples
97. Test for the mean. Dependent samples. Exercise
98. Test for the mean. Independent samples (Part 1)
99. Test for the mean. Independent samples (Part 1)
100. Test for the mean. Independent samples (Part 2)
101. Test for the mean. Independent samples (Part 2)
102. Test for the mean. Independent samples (Part 2). Exercise
103. Practical example: hypothesis testing
104. Practical example: hypothesis testing
105. Practical example: hypothesis testing
106. The fundamentals of regression analysis
107. Introduction to regression analysis
108. Introduction
109. Correlation and causation
110. Correlation and causation
111. The linear regression model made easy
112. The linear regression model
113. What is the difference between correlation and regression?
114. Correlation vs regression
115. A geometrical representation of the linear regression model
116. A geometrical representation of the linear regression model
117. A practical example - Reinforced learning
118. Subtleties of regression analysis
119. Decomposing the linear regression model - understanding its nuts and bolts
120. Decomposition
121. What is R-squared and how does it help us?
122. R-squared
123. The ordinary least squares setting and its practical applications
124. The ordinary least squares setting and its practical applications
125. Studying regression tables
126. Studying regression tables
127. Regression tables. Exercise
128. The multiple linear regression model
129. The multiple linear regression model
130. The adjusted R-squared
131. The adjusted R-squared
132. What does the F-statistic show us and why do we need to understand it?
133. Assumptions for linear regression analysis
134. OLS assumptions
135. OLS assumptions
136. A1. Linearity
137. A1. Linearity
138. A2. No endogeneity
139. A2. No endogeneity
140. A3. Normality and homoscedasticity
141. A3. Normality and homoscedasticity
142. A4. No autocorrelation
143. A4. No autocorrelation
144. A5. No multicollinearity
145. A5. No multicollinearity
146. Dealing with categorical data
147. Dummy variables
148. Practical example: regression analysis
149. Practical example: regression analysis
150. Bonus lecture
151. Bonus lecture: Next steps

This is a hands-on, project-based course designed to help you learn and apply essential statistics concepts for data analysis & business intelligence. Our goal is to simplify and demystify the world of statistics using familiar tools like Microsoft Excel, and empower everyday people to understand and apply these tools and techniques – even if you have absolutely no background in math or stats!

We'll start by discussing the role of statistics in business intelligence, the difference between sample and population data, and the importance of using statistical techniques to make smart predictions and data-driven decisions.

Next we'll explore our data using descriptive statistics and probability distributions, introduce the normal distribution and empirical rule, and learn how to apply the central limit theorem to make inferences about populations of any type.

From there we'll practice making estimates with confidence intervals, and using hypothesis tests to evaluate assumptions about unknown population parameters. We'll introduce the basic hypothesis testing framework, then dive into concepts like null and alternative hypotheses, t-scores, p-values, type I vs. type II errors, and more.

Last but not least, we'll introduce the fundamentals of regression analysis, explore the difference between correlation and causation, and practice using basic linear regression models to make predictions using Excel's Analysis Toolpak.

Throughout the course, you'll play the role of a Recruitment Analyst for Maven Business School. Your goal is to use the statistical techniques you've learned to explore student data, predict the performance of future classes, and propose changes to help improve graduate outcomes.

You'll also practice applying your skills to 5 real-world BONUS PROJECTS, and use statistics to explore data from restaurants, medical centers, pharmaceutical companys, safety teams, airlines, and more.

COURSE OUTLINE:

• Why Statistics?

  • Discuss the role of statistics in the context of business intelligence and decision-making, and introduce the statistics workflow

  
  

• Understanding Data with Descriptive Statistics

  • Understand data using descriptive statistics, including frequency distributions and measures of central tendency & variability

  • PROJECT #1: Maven Pizza Parlor

  
  

• Modeling Data with Probability Distributions

  • Model data with probability distributions, and use the normal distribution to calculate probabilities and make value estimates

  • PROJECT #2: Maven Medical Center

  
  

• The Central Limit Theorem

  • Introduce the Central Limit Theorem, which leverages the normal distribution to make inferences on populations with any distribution

  
  

• Making Estimates with Confidence Intervals

  • Make estimates with confidence intervals, which use sample statistics to define a range where an unknown population parameter likely lies

  • PROJECT #3: Maven Pharma

  
  

• Drawing Conclusions with Hypothesis Tests

  • Draw conclusions with hypothesis tests, which let you evaluate assumptions about population parameters using sample statistics

  • PROJECT #4: Maven Safety Council

  
  

• Making Predictions with Regression Analysis

  • Make predictions with regression analysis, and estimate the values of a dependent variable via its relationship with independent variables

  • PROJECT #5: Maven Airlines

Join today and get immediate, lifetime access to the following:

• 7.5 hours of high-quality video

• Statistics for Data Analysis PDF ebook (150+ pages)

• Downloadable Excel project files & solutions

• Expert support and Q&A forum

• 30-day Udemy satisfaction guarantee

If you're an analyst, data scientist, business intelligence professional, or anyone looking to use statistics to make smart, data-driven decisions, this course is for you!

Happy learning!

-Enrique Ruiz (Lead Statistics & Excel Instructor, Maven Analytics)

1. Getting Started
2. Course Structure & Outline
3. READ ME: Important Notes for New Students
4. DOWNLOAD: Course Resources
5. Setting Expectations
6. The Course Project
7. Helpful Resources
8. Why Statistics?
9. Section Intro
10. Why Statistics?
11. Populations & Samples
12. The Statistics Workflow
13. QUIZ: Why Statistics?
14. Understanding Data with Descriptive Statistics
15. Section Intro
16. Descriptive Statistics Basics
17. Types of Variables
18. Types of Descriptive Statistics
19. Categorical Frequency Distributions
20. Numerical Frequency Distributions
21. Histograms
22. ASSIGNMENT: Frequency Distributions
23. KNOWLEDGE CHECK: Frequency Distributions
24. SOLUTION: Frequency Distributions
25. Mean, Median, and Mode
26. Left & Right Skew
27. ASSIGNMENT: Measures of Central Tendency
28. KNOWLEDGE CHECK: Measures of Central Tendency
29. SOLUTION: Measures of Central Tendency
30. Min, Max & Range
31. Interquartile Range
32. Box & Whisker Plots
33. Variance & Standard Deviation
34. PRO TIP: Coefficient of Variation
35. ASSIGNMENT: Measures of Variability
36. KNOWLEDGE CHECK: Measures of Variability
37. SOLUTION: Measures of Variability
38. Key Takeaways
39. QUIZ: Descriptive Statistics
40. PROJECT #1: Maven Pizza Parlor
41. PROJECT BRIEF: Maven Pizza Parlor
42. SOLUTION: Maven Pizza Parlor
43. Modeling Data with Probability Distributions
44. Section Intro
45. Probability Distribution Basics
46. Types of Probability Distributions
47. The Normal Distribution
48. Z Scores
49. The Empirical Rule
50. ASSIGNMENT: Normal Distributions
51. KNOWLEDGE CHECK: Normal Distributions
52. SOLUTION: Normal Distributions
53. Excel's Normal Distribution Functions
54. Calculating Probabilities with the Normal Distribution
55. The NORM.DIST Function
56. The NORM.S.DIST Function
57. ASSIGNMENT: Calculating Probabilities
58. KNOWLEDGE CHECK: Calculating Probabilities
59. SOLUTION: Calculating Probabilities
60. PRO TIP: Plotting the Normal Curve
61. Estimating X or Z Values with the Normal Distribution
62. The NORM.INV Function
63. The NORM.S.INV Function
64. ASSIGNMENT: Estimating Values
65. KNOWLEDGE CHECK: Estimating Values
66. SOLUTION: Estimating Values
67. Key Takeaways
68. QUIZ: Probability Distributions
69. PROJECT #2: Maven Medical Center
70. PROJECT BRIEF: Maven Medical Center
71. SOLUTION: Maven Medical Center
72. The Central Limit Theorem
73. Section Intro
74. The Central Limit Theorem
75. DEMO: Proving the Central Limit Theorem
76. Standard Error
77. Implications of the Central Limit Theorem
78. Applications of the Central Limit Theorem
79. Key Takeaways
80. QUIZ: The Central Limit Theorem
81. Making Estimates with Confidence Intervals
82. Section Intro
83. Confidence Intervals Basics
84. Confidence Level
85. Margin of Error
86. DEMO: Calculating Confidence Intervals
87. The CONFIDENCE.NORM Function
88. ASSIGNMENT: Confidence Intervals
89. KNOWLEDGE CHECK: Confidence Intervals
90. SOLUTION: Confidence Intervals
91. Types of Confidence Intervals
92. T Distribution
93. Excel's T Distribution Functions
94. Confidence Intervals with the T Distribution
95. ASSIGNMENT: Confidence Intervals (T Distribution)
96. KNOWLEDGE CHECK: Confidence Intervals (T Distribution)
97. SOLUTION: Confidence Intervals (T Distribution)
98. Confidence Intervals for Proportions
99. ASSIGNMENT: Confidence Intervals (Proportions)
100. KNOWLEDGE CHECK: Confidence Intervals (Proportions)
101. SOLUTION: Confidence Intervals (Proportions)
102. Confidence Intervals for Two Populations
103. Dependent Samples
104. ASSIGNMENT: Confidence Intervals (Dependent Samples)
105. KNOWLEDGE CHECK: Confidence Intervals (Dependent Samples)
106. SOLUTION: Confidence Intervals (Dependent Samples)
107. Independent Samples
108. ASSIGNMENT: Confidence Intervals (Independent Samples)
109. KNOWLEDGE CHECK: Confidence Intervals (Independent Samples)
110. SOLUTION: Confidence Intervals (Independent Samples)
111. PRO TIP: Difference Between Proportions
112. Key Takeaways
113. QUIZ: Confidence Intervals
114. PROJECT #3: Maven Pharma
115. PROJECT BRIEF: Maven Pharma
116. SOLUTION: Maven Pharma
117. Drawing Conclusions with Hypothesis Tests
118. Section Intro
119. Hypothesis Testing Basics
120. Null & Alternative Hypothesis
121. Significance Level
122. Test Statistic (T-score)
123. P-Value
124. Drawing Conclusions from Hypothesis Tests
125. ASSIGNMENT: Hypothesis Tests
126. KNOWLEDGE CHECK: Hypothesis Tests
127. SOLUTION: Hypothesis Tests
128. Relationship between Confidence Intervals & Hypothesis Tests
129. Type I & Type II Errors
130. One Tail & Two Tail Hypothesis Tests
131. DEMO: One Tail Hypothesis Test
132. Hypothesis Tests for Proportions
133. ASSIGNMENT: Hypothesis Tests (Proportions)
134. KNOWLEDGE CHECK: Hypothesis Tests (Proportions)
135. SOLUTION: Hypothesis Tests (Proportions)
136. Hypothesis Tests for Dependent Samples
137. ASSIGNMENT: Hypothesis Tests (Dependent Samples)
138. KNOWLEDGE CHECK: Hypothesis Tests (Dependent Samples)
139. SOLUTION: Hypothesis Tests (Dependent Samples)
140. Hypothesis Tests for Independent Samples
141. ASSIGNMENT: Hypothesis Tests (Independent Samples)
142. KNOWLEDGE CHECK: Hypothesis Tests (Independent Samples)
143. SOLUTION: Hypothesis Tests (Independent Samples)
144. Key Takeaways
145. QUIZ: Hypothesis Tests
146. PROJECT #4: Maven Safety Council
147. PROJECT BRIEF: Maven Safety Council
148. SOLUTION: Maven Safety Council
149. Making Predictions with Regression Analysis
150. Section Intro
151. Linear Relationships
152. Correlation (R)
153. ASSIGNMENT: Linear Relationships
154. KNOWLEDGE CHECK: Linear Relationships
155. SOLUTION: Linear Relationships
156. Linear Regression & Least Squared Error
157. Excel's Linear Regression Functions
158. ASSIGNMENT: Simple Linear Regression
159. KNOWLEDGE CHECK: Simple Linear Regression
160. SOLUTION: Simple Linear Regression
161. Determination (R-Squared)
162. Standard Error
163. Homoskedasticity & Heteroskedasticity
164. Hypothesis Testing with Regression
165. ASSIGNMENT: Model Evaluation
166. KNOWLEDGE CHECK: Model Evaluation
167. SOLUTION: Model Evaluation
168. Excel's Regression Tool (Analysis ToolPak)
169. PRO TIP: Multiple Linear Regression
170. Key Takeaways
171. QUIZ: Regression Analysis
172. PROJECT #5: Maven Airlines
173. PROJECT BRIEF: Maven Airlines
174. SOLUTION: Maven Airlines
175. BONUS LESSON
176. BONUS LESSON

HOW BECOME A PROBABILITY & STATISTICS MASTER IS SET UP TO MAKE COMPLICATED MATH EASY:

This 163-lesson course includes video and text explanations of everything from Probability and Statistics, and it includes 45 quizzes (with solutions!) and an additional 8 workbooks with extra practice problems, to help you test your understanding along the way. Become a Probability & Statistics Master is organized into the following sections:

• Visualizing data, including bar graphs, pie charts, Venn diagrams, histograms, and dot plots

• Analyzing data, including mean, median, and mode, plus range and IQR and box-and-whisker plots

• Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores

• Probability, including union vs. intersection and independent and dependent events and Bayes' theorem

• Discrete random variables, including binomial, Bernoulli, Poisson, and geometric random variables

• Sampling, including types of studies, bias, and sampling distribution of the sample mean or sample proportion, and confidence intervals

• Hypothesis testing, including inferential statistics, significance levels, type I and II errors, test statistics, and p-values

• Regression, including scatterplots, correlation coefficient, the residual, coefficient of determination, RMSE, and chi-square

AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:

Videos: Watch over my shoulder as I solve problems for every single math issue you’ll encounter in class. We start from the beginning... I explain the problem setup and why I set it up that way, the steps I take and why I take them, how to work through the yucky, fuzzy middle parts, and how to simplify the answer when you get it.

Notes: The notes section of each lesson is where you find the most important things to remember. It’s like Cliff Notes for books, but for math. Everything you need to know to pass your class and nothing you don’t.

Quizzes: When you think you’ve got a good grasp on a topic within a course, you can test your knowledge by taking one of the quizzes. If you pass, great! If not, you can review the videos and notes again or ask for help in the Q&A section.

Workbooks: Want even more practice? When you've finished the section, you can review everything you've learned by working through the bonus workbook. The workbooks include tons of extra practice problems, so they're a great way to solidify what you just learned in that section.

HERE'S WHAT SOME STUDENTS OF BECOME A PROBABILITY & STATISTICS MASTER HAVE TOLD ME:

• “Krista is an experienced teacher who offers Udemy students complete subject matter coverage and efficient and effective lessons/learning experiences. She not only understands the course material, but also selects/uses excellent application examples for her students and presents them clearly and skillfully using visual teaching aids/tools.” - John

• “Really good, thorough, well explained lessons.” - Scott F.

• “This is my second course (algebra previously) from Ms. King's offerings. I enjoyed this course and learned a lot! Each video explains a concept, followed by the working of several examples. I learned the most by listening to Ms King's teaching of the concept, stopping the video, and then attempting to work the example problems. After working the problems, then watching her complete the examples, I found that I really retained the concepts. A great instructor!” - Charles M.

YOU'LL ALSO GET:

• Lifetime access to Become a Probability & Statistics Master

• Friendly support in the Q&A section

• Udemy Certificate of Completion available for download

• 30-day money back guarantee

Enroll today!

I can't wait for you to get started on mastering probability and statistics.

- Krista :)

1. Getting started
2. What we'll learn in this course
3. How to get the most out of this course
4. Download the formula sheet
5. The EVERYTHING download
6. Visualizing data
7. Introduction to visualizing data
8. RESOURCE: Quiz solutions for this section
9. One-way tables
10. One-way tables
11. One-way tables
12. Bar graphs and pie charts
13. Bar graphs and pie charts
14. Bar graphs and pie charts
15. Line graphs and ogives
16. Line graphs and ogives
17. Line graphs and ogives
18. Two-way tables
19. Two-way tables
20. Two-way tables
21. Venn diagrams
22. Venn diagrams
23. Venn diagrams
24. Frequency tables and dot plots
25. Frequency tables and dot plots
26. Frequency tables and dot plots
27. Relative frequency tables
28. Relative frequency tables
29. Relative frequency tables
30. Joint distributions
31. Joint distributions
32. Joint distributions
33. Histograms and stem-and-leaf plots
34. Histograms and stem-and-leaf plots
35. Histograms and stem-and-leaf plots
36. Building histograms from data sets
37. Building histograms from data sets
38. Building histograms from data sets
39. BONUS! Extra practice problems. :)
40. Analyzing data
41. Introduction to analyzing data
42. RESOURCE: Quiz solutions for this section
43. Measures of central tendency
44. Measures of central tendency
45. Measures of central tendency
46. Measures of spread
47. Measures of spread
48. Measures of spread
49. Changing the data, and outliers
50. Changing the data, and outliers
51. Changing the data, and outliers
52. Box-and-whisker plots
53. Box-and-whisker plots
54. Box-and-whisker plots
55. BONUS! Extra practice problems. :)
56. Data distributions
57. Introduction to data distributions
58. RESOURCE: Quiz solutions for this section
59. Mean, variance, and standard deviation
60. Mean, variance, and standard deviation
61. Mean, variance, and standard deviation
62. Frequency histograms and polygons, and density curves
63. Frequency histograms and polygons, and density curves
64. Frequency histograms and polygons, and density curves
65. Symmetric and skewed distributions and outliers
66. Symmetric and skewed distributions and outliers
67. Symmetric and skewed distributions and outliers
68. Normal distributions and z-scores
69. Normal distributions and z-scores
70. Normal distributions and z-scores
71. Chebyshev's Theorem
72. Chebyshev's Theorem
73. Chebyshev's Theorem
74. Covariance
75. Covariance
76. Covariance
77. Correlation coefficient
78. Correlation coefficient
79. Correlation coefficient
80. Weighted means and grouped data
81. Weighted means and grouped data
82. Weighted means and grouped data
83. BONUS! Extra practice problems. :)
84. Probability
85. Introduction to probability
86. RESOURCE: Quiz solutions for this section
87. Simple probability
88. Simple probability
89. Simple probability
90. The addition rule, and union vs. intersection
91. The addition rule, and union vs. intersection
92. The addition rule, and union vs. intersection
93. Independent and dependent events and conditional probability
94. Independent and dependent events and conditional probability
95. Independent and dependent events and conditional probability
96. Bayes' Theorem
97. Bayes' Theorem
98. Bayes' Theorem
99. BONUS! Extra practice problems. :)
100. Discrete random variables
101. Introduction to discrete random variables
102. RESOURCE: Quiz solutions for this section
103. Discrete probability
104. Discrete probability
105. Discrete probability
106. Transforming random variables
107. Transforming random variables
108. Transforming random variables
109. Combinations of random variables
110. Combinations of random variables
111. Combinations of random variables
112. Permutations and combinations
113. Permutations and combinations
114. Permutations and combinations
115. Binomial random variables
116. Binomial random variables
117. Binomial random variables
118. Poisson distributions
119. Poisson distributions
120. Poisson distributions
121. "At least" and "at most," and mean, variance, and standard deviation
122. "At least" and "at most," and mean, variance, and standard deviation
123. "At least" and "at most," and mean, variance, and standard deviation
124. Bernoulli random variables
125. Bernoulli random variables
126. Bernoulli random variables
127. Geometric random variables
128. Geometric random variables
129. Geometric random variables
130. BONUS! Extra practice problems. :)
131. Sampling
132. Introduction to sampling
133. RESOURCE: Quiz solutions for this section
134. Types of studies
135. Types of studies
136. Types of studies
137. Sampling and bias
138. Sampling and bias
139. Sampling and bias
140. Sampling distribution of the sample mean
141. Sampling distribution of the sample mean
142. Sampling distribution of the sample mean
143. Conditions for inference with the SDSM
144. Conditions for inference with the SDSM
145. Conditions for inference with the SDSM
146. Sampling distribution of the sample proportion
147. Sampling distribution of the sample proportion
148. Sampling distribution of the sample proportion
149. Conditions for inference with the SDSP
150. Conditions for inference with the SDSP
151. Conditions for inference with the SDSP
152. The student's t-distribution
153. The student's t-distribution
154. The student's t-distribution
155. Confidence interval for the mean
156. Confidence interval for the mean
157. Confidence interval for the mean
158. Confidence interval for the proportion
159. Confidence interval for the proportion
160. Confidence interval for the proportion
161. BONUS! Extra practice problems. :)
162. Hypothesis testing
163. Introduction to hypothesis testing
164. RESOURCE: Quiz solutions for this section
165. Inferential statistics and hypotheses
166. Inferential statistics and hypotheses
167. Inferential statistics and hypotheses
168. Significance level and type I and II errors
169. Significance level and type I and II errors
170. Significance level and type I and II errors
171. Test statistics for one- and two-tailed tests
172. Test statistics for one- and two-tailed tests
173. Test statistics for one- and two-tailed tests
174. The p-value and rejecting the null
175. The p-value and rejecting the null
176. The p-value and rejecting the null
177. Hypothesis testing for the population proportion
178. Hypothesis testing for the population proportion
179. Hypothesis testing for the population proportion
180. Confidence interval for the difference of means
181. Confidence interval for the difference of means
182. Confidence interval for the difference of means
183. Hypothesis testing for the difference of means
184. Hypothesis testing for the difference of means
185. Hypothesis testing for the difference of means
186. Matched-pair hypothesis testing
187. Matched-pair hypothesis testing
188. Matched-pair hypothesis testing
189. Confidence interval for the difference of proportions
190. Confidence interval for the difference of proportions
191. Confidence interval for the difference of proportions
192. Hypothesis testing for the difference of proportions
193. Hypothesis testing for the difference of proportions
194. Hypothesis testing for the difference of proportions
195. BONUS! Extra practice problems. :)
196. Regression
197. Introduction to regression
198. RESOURCE: Quiz solutions for this section
199. Scatterplots and regression
200. Scatterplots and regression

Statistics and probability control your life. I don't just mean What YouTube's algorithm recommends you to watch next, and I don't just mean the chance of meeting your future significant other in class or at a bar. Human behavior, single-cell organisms, Earthquakes, the stock market, whether it will snow in the first week of December, and countless other phenomena are probabilistic and statistical. Even the very nature of the most fundamental deep structure of the universe is governed by probability and statistics.

You need to understand statistics.

Nearly all areas of human civilization are incorporating code and numerical computations. This means that many jobs and areas of study are based on applications of statistical and machine-learning techniques in programming languages like Python and MATLAB. This is often called 'data science' and is an increasingly important topic. Statistics and machine learning are also fundamental to artificial intelligence (AI) and business intelligence.

If you want to make yourself a future-proof employee, employer, data scientist, or researcher in any technical field -- ranging from data scientist to engineering to research scientist to deep learning modeler -- you'll need to know statistics and machine-learning. And you'll need to know how to implement concepts like probability theory and confidence intervals, k-means clustering and PCA, Spearman correlation and logistic regression, in computer languages like Python or MATLAB.

There are six reasons why you should take this course:

• This course covers everything you need to understand the fundamentals of statistics, machine learning, and data science, from bar plots to ANOVAs, regression to k-means, t-test to non-parametric permutation testing.

• After completing this course, you will be able to understand a wide range of statistical and machine-learning analyses, even specific advanced methods that aren't taught here. That's because you will learn the foundations upon which advanced methods are build.

• This course balances mathematical rigor with intuitive explanations, and hands-on explorations in code.

• Enrolling in the course gives you access to the Q&A, in which I actively participate every day.

• I've been studying, developing, and teaching statistics for over 20 years, and I think math is, like, really cool.

What you need to know before taking this course:

• High-school level maths. This is an applications-oriented course, so I don't go into a lot of detail about proofs, derivations, or calculus.

• Basic coding skills in Python or MATLAB. This is necessary only if you want to follow along with the code. You can successfully complete this course without writing a single line of code! But participating in the coding exercises will help you learn the material. The MATLAB code relies on the Statistics and Machine Learning toolbox (you can use Octave if you don't have MATLAB or the statistics toolbox). Python code is written in Jupyter notebooks.

• I recommend taking my free course called "Statistics literacy for non-statisticians". It's 90 minutes long and will give you a bird's-eye-view of the main topics in statistics that I go into much much much more detail about here in this course. Note that the free short course is not required for this course, but complements this course nicely. And you can get through the whole thing in less than an hour if you watch if on 1.5x speed!

• You do not need any previous experience with statistics, machine learning, deep learning, or data science. That's why you're here!

Is this course up to date?

Yes, I maintain all of my courses regularly. I add new lectures to keep the course "alive," and I add new lectures (or sometimes re-film existing lectures) to explain maths concepts better if students find a topic confusing or if I made a mistake in the lecture (rare, but it happens!).

You can check the "Last updated" text at the top of this page to see when I last worked on improving this course!

What if you have questions about the material?

This course has a Q&A (question and answer) section where you can post your questions about the course material (about the maths, statistics, coding, or machine learning aspects). I try to answer all questions within a day. You can also see all other questions and answers, which really improves how much you can learn! And you can contribute to the Q&A by posting to ongoing discussions.

And, you can also post your code for feedback or just to show off -- I love it when students actually write better code than me! (Ahem, doesn't happen so often.)

What should you do now?

First of all, congrats on reading this far; that means you are seriously interested in learning statistics and machine learning. Watch the preview videos, check out the reviews, and, when you're ready, invest in your brain by learning from this course!

1. Introductions
2. [Important] Getting the most out of this course
3. About using MATLAB or Python
4. Statistics guessing game!
5. Using the Q&A forum
6. (optional) Entering time-stamped notes in the Udemy video player
7. Math prerequisites
8. Should you memorize statistical formulas?
9. Arithmetic and exponents
10. Scientific notation
11. Summation notation
12. Absolute value
13. Natural exponent and logarithm
14. The logistic function
15. Rank and tied-rank
16. IMPORTANT: Download course materials
17. Download materials for the entire course!
18. What are (is?) data?
19. Is "data" singular or plural?!?!!?!
20. Where do data come from and what do they mean?
21. Types of data: categorical, numerical, etc
22. Code: representing types of data on computers
23. Sample vs. population data
24. Samples, case reports, and anecdotes
25. The ethics of making up data
26. Visualizing data
27. Bar plots
28. Code: bar plots
29. Box-and-whisker plots
30. Code: box plots
31. "Unsupervised learning": Boxplots of normal and uniform noise
32. Histograms
33. Code: histograms
34. "Unsupervised learning": Histogram proportion
35. Pie charts
36. Code: pie charts
37. When to use lines instead of bars
38. Linear vs. logarithmic axis scaling
39. Code: line plots
40. "Unsupervised learning": log-scaled plots
41. Descriptive statistics
42. Descriptive vs. inferential statistics
43. Accuracy, precision, resolution
44. Data distributions
45. Code: data from different distributions
46. "Unsupervised learning": histograms of distributions
47. The beauty and simplicity of Normal
48. Measures of central tendency (mean)
49. Measures of central tendency (median, mode)
50. Code: computing central tendency
51. "Unsupervised learning": central tendencies with outliers
52. Measures of dispersion (variance, standard deviation)
53. Code: Computing dispersion
54. Interquartile range (IQR)
55. Code: IQR
56. QQ plots
57. Code: QQ plots
58. Statistical "moments"
59. Histograms part 2: Number of bins
60. Code: Histogram bins
61. Violin plots
62. Code: violin plots
63. "Unsupervised learning": asymmetric violin plots
64. Shannon entropy
65. Code: entropy
66. "Unsupervised learning": entropy and number of bins
67. Data normalizations and outliers
68. Garbage in, garbage out (GIGO)
69. Z-score standardization
70. Code: z-score
71. Min-max scaling
72. Code: min-max scaling
73. "Unsupervised learning": Invert the min-max scaling
74. What are outliers and why are they dangerous?
75. Removing outliers: z-score method
76. The modified z-score method
77. Code: z-score for outlier removal
78. "Unsupervised learning": z vs. modified-z
79. Multivariate outlier detection
80. Code: Euclidean distance for outlier removal
81. Removing outliers by data trimming
82. Code: Data trimming to remove outliers
83. Non-parametric solutions to outliers
84. Nonlinear data transformations
85. An outlier lecture on personal accountability
86. Probability theory
87. What is probability?
88. Probability vs. proportion
89. Computing probabilities
90. Code: compute probabilities
91. Probability and odds
92. "Unsupervised learning": probabilities of odds-space
93. Probability mass vs. density
94. Code: compute probability mass functions
95. Cumulative distribution functions
96. Code: cdfs and pdfs
97. "Unsupervised learning": cdf's for various distributions
98. Creating sample estimate distributions
99. Monte Carlo sampling
100. Sampling variability, noise, and other annoyances
101. Code: sampling variability
102. Expected value
103. Conditional probability
104. Code: conditional probabilities
105. Tree diagrams for conditional probabilities
106. The Law of Large Numbers
107. Code: Law of Large Numbers in action
108. The Central Limit Theorem
109. Code: the CLT in action
110. "Unsupervised learning": Averaging pairs of numbers
111. Hypothesis testing
112. IVs, DVs, models, and other stats lingo
113. What is an hypothesis and how do you specify one?
114. Sample distributions under null and alternative hypotheses
115. P-values: definition, tails, and misinterpretations
116. P-z combinations that you should memorize
117. Degrees of freedom
118. Type 1 and Type 2 errors
119. Parametric vs. non-parametric tests
120. Multiple comparisons and Bonferroni correction
121. Statistical vs. theoretical vs. clinical significance
122. Cross-validation
123. Statistical significance vs. classification accuracy
124. The t-test family
125. Purpose and interpretation of the t-test
126. One-sample t-test
127. Code: One-sample t-test
128. "Unsupervised learning": The role of variance
129. Two-samples t-test
130. Code: Two-samples t-test
131. "Unsupervised learning": Importance of N for t-test
132. Wilcoxon signed-rank (nonparametric t-test)
133. Code: Signed-rank test
134. Mann-Whitney U test (nonparametric t-test)
135. Code: Mann-Whitney U test
136. Permutation testing for t-test significance
137. Code: permutation testing
138. "Unsupervised learning": How many permutations?
139. Confidence intervals on parameters
140. What are confidence intervals and why do we need them?
141. Computing confidence intervals via formula
142. Code: compute confidence intervals by formula
143. Confidence intervals via bootstrapping (resampling)
144. Code: bootstrapping confidence intervals
145. "Unsupervised learning:" Confidence intervals for variance
146. Misconceptions about confidence intervals
147. Correlation
148. Motivation and description of correlation
149. Covariance and correlation: formulas
150. Code: correlation coefficient
151. Code: Simulate data with specified correlation
152. Correlation matrix
153. Code: correlation matrix
154. "Unsupervised learning": average correlation matrices
155. "Unsupervised learning": correlation to covariance matrix
156. Partial correlation
157. Code: partial correlation
158. The problem with Pearson
159. Nonparametric correlation: Spearman rank
160. Fisher-Z transformation for correlations
161. Code: Spearman correlation and Fisher-Z
162. "Unsupervised learning": Spearman correlation
163. "Unsupervised learning": confidence interval on correlation
164. Kendall's correlation for ordinal data
165. Code: Kendall correlation
166. "Unsupervised learning": Does Kendall vs. Pearson matter?
167. The subgroups correlation paradox
168. Cosine similarity
169. Code: Cosine similarity vs. Pearson correlation
170. Analysis of Variance (ANOVA)
171. ANOVA intro, part1
172. ANOVA intro, part 2
173. Sum of squares
174. The F-test and the ANOVA table
175. The omnibus F-test and post-hoc comparisons
176. The two-way ANOVA
177. One-way ANOVA example
178. Code: One-way ANOVA (independent samples)
179. Code: One-way repeated-measures ANOVA
180. Two-way ANOVA example
181. Code: Two-way mixed ANOVA
182. Regression
183. Introduction to GLM / regression
184. Least-squares solution to the GLM
185. Evaluating regression models: R2 and F
186. Simple regression
187. Code: simple regression
188. "Unsupervised learning": Compute R2 and F
189. Multiple regression
190. Standardizing regression coefficients
191. Code: Multiple regression
192. Polynomial regression models
193. Code: polynomial modeling
194. "Unsupervised learning": Polynomial design matrix
195. Logistic regression
196. Code: Logistic regression
197. Under- and over-fitting
198. "Unsupervised learning": Overfit data
199. Comparing "nested" models
200. What to do about missing data

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.

1. Let's get started
2. Welcome!
3. What will you learn in this course?
4. How can you get the most out of it?
5. Download: Formula cheat sheet
6. Descriptive statistics
7. Intro
8. Mean
9. Quiz: Mean
10. Median
11. Quiz: Median
12. Mode
13. Quiz: Mode
14. Mean or Median?
15. Skewness
16. Practice: Skewness
17. Solution: Skewness
18. Range & IQR
19. Sample vs. Population
20. Variance & Standard deviation
21. Quiz: Variance
22. Impact of Scaling & Shifting
23. Statistical moments
24. Distributions
25. What is a distribution?
26. Normal distribution
27. Z-Scores
28. Practise: Normal distribution
29. Solution: Normal distribution
30. Normal distribution
31. More distributions
32. Probability theory
33. Intro
34. Probability Basics
35. Calculating Simple Probabilities
36. Practice: Simple Probabilities
37. Quick solution: Simple Probabilites
38. Detailed solution: Simple Probabilities
39. Rule of addition
40. Practice: Rule of addition
41. Quick solution: Rule of addition
42. Detailed solution: Rule of addition
43. Rule of multiplication
44. Practice: Rule of multiplication
45. Solution: Rule of multiplication
46. Bayes Theorem
47. Bayes Theorem - Practical example
48. Expected value
49. Practice: Expected value
50. Solution: Expected value
51. Law of Large Numbers
52. Central Limit Theorem - Theory
53. Central Limit Theorem - Intuition
54. Central Limit Theorem - Challenge
55. Central Limit Theorem - Exercise
56. Central Limit Theorem - Solution
57. Quiz: Bayes Theorem
58. Binomial distribution
59. Poisson distribtuion
60. Real life problems
61. Hypothesis testing
62. Intro
63. What is an hypothesis?
64. Significance level and p-value
65. Type I and Type II errors
66. Confidence intervals and margin of error
67. Excursion: Calculating sample size & power
68. Performing the hypothesis test
69. Practice: Hypothesis test
70. Solution: Hypothesis test
71. t-test and t-distribution
72. Proportion testing
73. Important p-z pairs
74. Quiz: Hypothesis Testing
75. Regressions
76. Intro
77. Linear Regression
78. Correlation coefficient
79. Practice: Correlation
80. Solution: Correlation
81. Practice: Linear Regression
82. Solution: Linear Regression
83. Residual, MSE & MAE
84. Practice: MSE & MAE
85. Solution: MSE & MAE
86. Coefficient of determination
87. Root Mean Square Error
88. Practice: RMSE
89. Solution: RMSE
90. Quiz: Regression
91. Advanced regression & machine learning algorithms
92. Multiple Linear Regression
93. Overfitting
94. Polynomial Regression
95. Logistic Regression
96. Decision Trees
97. Regression Trees
98. Random Forests
99. Dealing with missing data
100. ANOVA (Analysis of Variance)
101. ANOVA - Basics & Assumptions
102. One-way ANOVA
103. F-Distribution
104. Two-way ANOVA – Sum of Squares
105. Two-way ANOVA – F-ratio & conclusions
106. Quiz: ANOVA
107. Wrap up
108. Wrap up
109. Bonus lecture