CF

Become a Probability & Statistics Master

11h 29m 40s
English
Paid

Master the essential concepts of Probability and Statistics with our comprehensive course featuring 163 lessons, complete with video and text explanations. Test your knowledge with 45 quizzes, complete with solutions, and dive deeper with 8 additional workbooks full of practice problems. "Become a Probability & Statistics Master" is systematically organized into the following sections:

Course Content Overview

  • Visualizing Data

    Learn to create and interpret bar graphs, pie charts, Venn diagrams, histograms, and dot plots for better data visualization.

  • Analyzing Data

    Understand central tendency measures such as mean, median, and mode, along with range, interquartile range (IQR), and box-and-whisker plots.

  • Data Distributions

    Dive into data distributions including mean, variance, and standard deviation, as well as exploring normal distributions and z-scores.

  • Probability Concepts

    Explore probability topics such as union vs. intersection, independent and dependent events, and delve into Bayes' theorem.

  • Discrete Random Variables

    Gain knowledge on binomial, Bernoulli, Poisson, and geometric random variables.

  • Sampling Techniques

    Understand different study types, biases, and the sampling distribution of the sample mean or proportion, including confidence intervals.

  • Hypothesis Testing

    Master inferential statistics, significance levels, type I and II errors, test statistics, and the interpretation of p-values.

  • Regression Analysis

    Learn about scatterplots, correlation coefficients, residuals, the coefficient of determination, RMSE, and chi-square tests for regression.

About the Authors

Krista King

Krista King thumbnail

Krista King is a US math educator behind Krista King Math — one of the largest independent math-teaching brands on YouTube, focused on the foundational math curriculum (algebra, calculus, statistics, linear algebra) that anchors STEM and engineering work. Her teaching style is unusually patient and accessible for the math-teaching market.

Her CourseFlix listing carries two Krista King courses: Master the Fundamentals of Math (the foundational arithmetic-through-pre-calculus curriculum) and Become a Probability & Statistics Master (the statistics curriculum aimed at developers and analysts who didn't get a rigorous math education through formal schooling).

Material is paid and aimed at adult learners filling foundational math gaps. For broader content, see CourseFlix's Math & Statistics category page.

Udemy

Udemy thumbnail

Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

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#1: Hi! START HERE: Course overview
All Course Lessons (55)
#Lesson TitleDurationAccess
1
Hi! START HERE: Course overview Demo
01:28
2
Introduction to visualizing data
00:46
3
One-way data
09:18
4
Bar graphs and pie charts
19:39
5
Line graphs and ogives
13:59
6
Two-way data
12:56
7
Venn diagrams
14:03
8
Relative frequency tables
11:15
9
Joint distributions
10:37
10
Frequency tables and dot plots
03:43
11
Histograms and stem-and-leaf plots
13:02
12
Introduction to analyzing data
01:03
13
Central tendency: mean, median and mode
13:40
14
Spread: range and IQR
11:27
15
Changing the data, and outliers
16:08
16
Box-and-whisker plots
06:20
17
Introduction to data distributions
00:56
18
Mean, variance, and standard deviation
15:17
19
Frequency histograms and polygons, and density curves
10:53
20
Symmetric and skewed distributions and outliers
14:06
21
Normal distributions and z-scores
20:58
22
Introduction to probability
00:47
23
Simple probability
17:13
24
The addition rule, and union vs. intersection
20:31
25
Independent and dependent events and conditional probability
17:41
26
Bayes' theorem
17:08
27
Introduction to discrete random variables
01:01
28
Discrete probability
13:10
29
Transforming random variables
07:35
30
Combinations of random variables
15:34
31
Permutations and combinations
10:12
32
Binomial random variables
21:18
33
Poisson distributions
16:37
34
"At least" and "at most," and mean, variance, and standard deviation
13:53
35
Bernoulli random variables
10:38
36
Geometric random variables
18:22
37
Introduction to sampling
01:19
38
Types of studies
15:57
39
Sampling and bias
15:41
40
Sampling distribution of the sample mean
22:14
41
Sampling distribution of the sample proportion
19:03
42
Confidence interval for a population mean
22:33
43
Confidence interval for a population proportion
17:13
44
Introduction to hypothesis testing
01:02
45
Inferential statistics and hypotheses
15:26
46
Significance level and type I and II errors
13:25
47
Test statistics for one- and two-tailed tests
19:51
48
The p-value and rejecting the null
12:47
49
Hypothesis testing for the population proportion
09:53
50
Introduction to regression
01:08
51
Scatterplots and regression
15:52
52
Correlation coefficient and the residual
23:11
53
Coefficient of determination and root-mean-square error
17:01
54
Chi-square tests
22:25
55
Wrap-up
00:25
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Frequently asked questions

What prerequisites are needed for this course?
The course is designed for individuals with a basic understanding of mathematics. Familiarity with algebraic concepts will be beneficial, but the course starts with fundamental topics like visualizing and analyzing data, making it accessible to beginners.
What types of projects or exercises are included in the course?
The course includes 45 quizzes with solutions and 8 additional workbooks filled with practice problems. These exercises help reinforce topics like visualizing data with bar graphs and pie charts, understanding probability concepts, and conducting hypothesis testing.
Who is the target audience for this course?
This course is suitable for students, professionals, or anyone interested in gaining a solid understanding of probability and statistics. It caters to those who want to enhance their data analysis skills, whether for academic purposes or in fields like data science, business analytics, or research.
How does the course depth compare to other statistics courses?
The course offers a systematic exploration of essential topics, from central tendency measures to complex concepts like Bayes' theorem and chi-square tests. It provides a comprehensive foundation in probability and statistics, making it comparable to introductory college-level courses.
What specific tools or platforms are covered in this course?
The course delves into various methods for visualizing and analyzing data, such as creating bar graphs, pie charts, and scatterplots. It does not focus on specific software tools but emphasizes understanding the underlying statistical concepts and techniques.
What topics are not covered in this course?
The course does not cover advanced statistical software or programming languages like R or Python. It focuses on the foundational theories and concepts of probability and statistics without delving into computational statistics or machine learning applications.
What is the estimated time commitment for completing the course?
The course consists of 163 lessons with video and text explanations, complemented by quizzes and workbooks. Depending on the participant's prior knowledge and pace, it may take several weeks to complete, with an estimated commitment of a few hours per week.