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Statistics Bootcamp (with Python): Zero to Mastery

20h 50m 51s
English
Paid

Statistics Bootcamp (with Python): Zero to Mastery is a 226-lesson 20 hours 50 minutes self-paced course by Zero To Mastery. Learn Statistics from an industry expert (and even have fun).

Course facts

Lessons
226
Duration
20 hours 50 minutes
Level
All levels
Language
English
Updated
Instructor
Zero To Mastery
Price
Premium

Learn Statistics from an industry expert (and even have fun). You'll learn by building 6 statistics-based projects and solidify your skills with 18 quizzes, practice tests, and challenges. Plus you'll learn to utilize ChatGPT to work with statistics and conduct data analysis efficiently.

We guarantee you this is the most up-to-date, comprehensive, and FUN way to learn Statistics with Python. This Statistics course is the key building block to launch your career in statistics-heavy fields like Data Analytics, Data Science, and A.I. Machine Learning.

Who teaches Statistics Bootcamp (with Python): Zero to Mastery? Zero To Mastery

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Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

What lessons are included in Statistics Bootcamp (with Python): Zero to Mastery?

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#1: Learn Statistics with Diogo Resende
All Course Lessons (226)
#Lesson TitleDurationAccess
1
Learn Statistics with Diogo Resende Demo
01:14
2
Course Outline
02:50
3
Why Statistics Matter - The Challenger Space Shuttle Disaster
03:31
4
Creating a Google Account
05:24
5
Setting Up the Course Materials
03:24
6
Game Plan for Python Essentials
01:54
7
Print Function
05:48
8
Python - Print Function
10:28
9
Input Function
04:20
10
Python - Input Function
08:09
11
CHALLENGE - Your Superhero Name
06:51
12
Variable Types
03:02
13
Python - Variable Types
06:27
14
Arithmetic Operators
06:04
15
Python - Arithmetic Operators
06:48
16
Comparison Operators
03:41
17
Python - Comparison Operators
04:57
18
CHALLENGE - Split Bill Calculator
11:29
19
The if-else Condition
06:22
20
Python - if-else Conditions
06:02
21
EXERCISE - Can You Vote?
02:29
22
EXERCISE - Grading Papers
05:22
23
CHALLENGE - Berghain Club Bouncer
11:55
24
CHALLENGE - Your Monthly Savings Plan
19:44
25
Wrap Up - Python Essentials
02:22
26
Game Plan for Intermediate Python for Statistics
03:08
27
While Loop
02:27
28
Python - While Loops
06:45
29
EXERCISE - Countdown Times
05:03
30
Python Lists
07:01
31
Python - Lists
10:31
32
EXERCISE - Monthly Expense Report
06:43
33
EXERCISE - Fibonacci Sequence
06:07
34
Randomization
04:36
35
Python - Randomization
05:25
36
EXERCISE - Movie Picker
06:22
37
CHALLENGE - Read my Mind
09:01
38
Dictionaries
04:03
39
Python - Dictionaries
06:31
40
EXERCISE - Magical Pet Sounds
07:36
41
CHALLENGE - Budget Mastermind
16:20
42
For Loops
04:42
43
Python - For Loops
04:19
44
EXERCISE - Sum of Numbers
04:27
45
EXERCISE - Counting the Number of Characters
07:59
46
CHALLENGE - Treasure Hunter
14:09
47
Functions
06:49
48
Python - Functions
06:19
49
EXERCISE - Function That Adds Numbers
02:21
50
EXERCISE - Function That Counts Vowels
04:36
51
EXERCISE - Function That Transforms Fahrenheit to Celsius
06:35
52
CHALLENGE - Recipe Converter
24:41
53
Wrap Up - Python Intermediate Skills
03:24
54
Project Presentation - Virtual Escape Game
05:52
55
Python - Plan the Solution
08:16
56
Python - Check User's Answer Function
08:48
57
Python - Prepare Game
18:12
58
Python - Solving with ChatGPT
06:55
59
Game Plan for Descriptive Statistics
01:51
60
Variable Types in Statistics
02:56
61
Population vs. Sample
03:29
62
CASE STUDY Briefing - Moneyball
02:17
63
Python - Setting Up
05:52
64
Measures of Central Tendency
03:14
65
(Arithmetic) Mean
03:39
66
Python - Mean
04:45
67
EXERCISE - Python
02:36
68
Median
02:17
69
Python - Median
01:52
70
EXERCISE - Median
01:09
71
Mode
01:28
72
Python - Mode
02:35
73
EXERCISE - Mode
02:37
74
Standard Deviation and Variance
04:57
75
Python - Standard Deviation and Variance
05:27
76
EXERCISE - Standard Deviation and Variance
02:38
77
Coefficient of Variation
04:32
78
Python - Coefficient of Variation
03:26
79
EXERCISE - Coefficient of Variation
01:04
80
Covariance
04:15
81
Python - Covariance
03:48
82
EXERCISE - Covariance
01:52
83
Correlation
06:55
84
Python - Correlation
05:37
85
EXERCISE - Correlation
02:01
86
Normal Distribution
04:09
87
Python - Normal Distribution
06:48
88
EXERCISE - Normal Distribution
03:12
89
CASE STUDY - Moneyball
04:15
90
Wrap Up - Descriptive Statistics
01:56
91
Game Plan for Confidence Intervals
01:05
92
CASE STUDY Briefing - Dioguinis Pizza
01:30
93
Standard Error of the Sample Mean
02:18
94
Python - Libraries and Data
04:40
95
Python - Standard Error of the Sample Mean
02:48
96
Z-Score and Standardization
03:13
97
Python - Z-Score and Standardization
09:58
98
Confidence Level
04:50
99
Python - Confidence Level
10:31
100
Confidence Intervals for Large Samples
06:18
101
Python - Confidence Interval for Large Samples
06:13
102
EXERCISE - Confidence Interval Function with ChatGPT
06:56
103
CASE STUDY - Guinness Beer and t-distribution
02:36
104
Confidence Interval with Small Samples
03:27
105
Degrees of Freedom
07:14
106
Python - Confidence Interval with Small Samples
08:15
107
EXERCISE - Confidence Interval Function with ChatGPT
05:12
108
Confidence Intervals Wrap Up
04:28
109
Project Presentation - Lights, Camera, Statistics
02:41
110
Python - Data Preparation and Cleaning
20:53
111
Python - Exploratory Data Analysis
16:56
112
Python - Estimating Average Ratings
11:58
113
Python - Conclusions
05:44
114
Exercise: Imposter Syndrome
02:57
115
Game Plan for Hypothesis Testing
03:08
116
What is Hypothesis Testing?
04:55
117
P-Value
04:37
118
Type I and Type II Errors
04:06
119
CASE STUDY - Publication Bias in Statistics
02:59
120
How to Test Your Hypothesis (Known Population Variance).
06:51
121
CASE STUDY Briefing - Tesla Production
01:49
122
Python - Setting Up and Libraries
02:53
123
Python - How to Test Your Hypothesis (Known Population Variance)
12:01
124
Python - Build a Function to Test Your Known Variance Hypothesis
05:23
125
Hypothesis Testing with Unknown Population Variance
02:55
126
Python - How to Test Your Hypothesis (Unknown Population Variance) - Part 1
11:03
127
Python - How to Test Your Hypothesis (Unknown Population Variance) - Part 2
06:39
128
Paired T-Test
03:55
129
Python - Paired T-Test - Part 1
10:40
130
Python - Paired T-Test - Part 2
03:30
131
Two Sample T-Test
05:31
132
Python - Levene's Test
08:05
133
Python - Welch's T-Test
03:45
134
Python - Two-Sample T-Test
02:13
135
Exercise - Two-Sample Test Function
04:42
136
One-Tailed Test vs. Two-Tailed Test
05:51
137
Python - One-Tailed Test with Known Variance
07:28
138
Python - One-Tailed Test with Unknown Variance
05:40
139
Python - One-Tailed Paired T-Test
05:55
140
Python - One-Tailed Two-Sample T-Test
05:30
141
Chi-Square Test
03:09
142
Python - Chi-Square Test
11:00
143
Is Your Distribution Normal? - The Shapiro-Wilks Test
02:44
144
Python - Shapiro-Wilks Test
05:35
145
Hypothesis Testing Wrap Up
02:44
146
Powerposing and P-Hacking
03:40
147
Python Solutions - Data
14:42
148
Capstone Project with ChatGPT - Yelp me!
02:44
149
Python Solutions - Hypothesis 1
11:20
150
Python Solutions - Hypothesis 2
09:08
151
Python Solutions - Hypothesis 3
08:42
152
Game Plan for Multilinear Regression
01:23
153
CASE STUDY Briefing - Pricing Diamonds
01:54
154
Linear Regression
05:13
155
Python - Libraries and Data
04:21
156
Python - Exploratory Data Analysis
04:58
157
Python - Linear Regression
03:10
158
Regression Statistics
04:24
159
Python - Linear Regression Output
02:30
160
Python - Plotting Regression Curve
02:57
161
Dummy Variable (Trap)
04:01
162
Python - Linear Regression with Dummy Variables
06:53
163
EXERCISE - Create Function that Reads the Regression Coefficients with ChatGPT
07:11
164
CASE STUDY - Linearity Bias - We Will All Be Obese! Wait What?
04:02
165
Multilinear Regression
01:48
166
Python - Categorical Variables
05:48
167
Python - Multilinear Regression Preparation
02:11
168
Under and Overfitting
03:28
169
Training and Test Set
02:35
170
Python - Training and Test Split
03:15
171
Python - Multilinear Regression
08:43
172
Assessing Regression Models
06:08
173
Python - Assessing Regression Model
06:20
174
CASE STUDY - Dangers of Regression Analysis
02:52
175
Multilinear Regression Wrap Up
02:03
176
Capstone Project - Understanding Sales Drivers
01:08
177
Python - Solutions - Step 1
07:14
178
Python - Solutions - Steps 2-4
05:38
179
Python - Solutions - Steps 5-6
07:16
180
Game Plan for Logistic Regression
01:40
181
CASE STUDY Briefing - Spam Emails
01:26
182
Logistic Regression
03:29
183
Python - Preparing Script and Loading Data
03:32
184
Python - Summary Statistics
03:45
185
Python - Histograms and Outlier Detection
05:37
186
Python - Correlation Matrix
03:27
187
Python - Logistic Regression Preparation
04:00
188
How to Read Logistic Regression Coefficients
02:12
189
Python - Logistic Regression
02:18
190
Python - Build a Coefficient Function with ChatGPT
09:07
191
Python - Predictions
03:20
192
Confusion Matrix and Model Assessment
06:25
193
Python - Confusion Matrix and Classification Report
05:35
194
Python - Assessing Classification Models with ChatGPT
05:31
195
Section Wrap Up - Logistic Regression
03:16
196
Capstone Project - Surviving Titanic
01:34
197
Python - Libraries and Data with ChatGPT
08:13
198
Python - Removing Outliers and EDA with ChatGPT
12:35
199
Python - Logistic Regression Model and Assessment with ChatGPT
23:29
200
Game Plan for Cox Proportional Hazard Regression
02:15
201
Introduction to Survival Analysis
07:48
202
CASE STUDY - Briefing
01:48
203
Python - Libraries and Data
05:10
204
Kaplan-Meier Estimator
04:36
205
Python - Kaplan Meier Estimator
04:23
206
Python - Calculating for a Specific Event
02:47
207
Python - Plotting Kaplan-Meier and Cumulated Curves
03:52
208
Censoring
03:46
209
Log Rank Test
02:56
210
Python - Kaplan-Meier Estimator per Gender and Visualization
05:51
211
Python - Log Rank Test
06:34
212
Cox Proportional Hazard Regression
04:52
213
Python - Prepare Data for CPH Model
03:12
214
Python - Cox Proportional Hazard Regression
09:37
215
Python - Visualize Results
02:13
216
Assessing Cox Proportional Hazard Models
05:19
217
Python - Assessing the CPH Model
08:38
218
Python - Predicting Specific Instances
03:39
219
Cox Proportional Hazard Regression Wrap Up
03:15
220
Capstone Project - Will Your App Make it?
01:24
221
Python - Libraries and Data
07:11
222
Python - Data Cleaning
19:11
223
Python - Dependent Variable
08:27
224
Python - Kaplan-Meier Estimator
04:31
225
Python - Cox Model
10:01
226
Thank You!
01:18
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Frequently asked questions

What prerequisites do I need before enrolling in this course?
The course is designed for beginners, so no prior knowledge of statistics is required. However, it covers basic Python programming skills, which will be essential as you progress. Early lessons include Python essentials like the print and input functions, variable types, and arithmetic operators, ensuring you have the necessary programming foundation to tackle statistical concepts.
What projects will I build during this course?
Throughout the course, you will build six statistics-based projects. One highlighted project involves creating a Virtual Escape Game, where you apply Python programming and statistics to plan the solution, check user answers, and prepare the game environment. These projects are designed to reinforce your learning and provide practical applications of statistical concepts.
Who is the target audience for this course?
The course is tailored for beginners who are interested in learning statistics with a practical approach using Python programming. It is suitable for those who have little to no background in statistics or programming but wish to gain foundational skills in statistical analysis and data manipulation.
What specific tools or platforms will I learn to use?
In addition to learning Python programming, the course teaches you to utilize ChatGPT for working with statistics and conducting data analysis more efficiently. This integration of AI tools with statistics provides a modern approach to data handling and analysis.
What important topics are NOT covered in this course?
While the course provides a foundational understanding of statistics and Python, it does not cover advanced statistical methods or data science techniques such as machine learning, predictive modeling, or data visualization tools beyond basic Python capabilities.
How much time should I expect to commit to complete this course?
The course consists of 226 lessons, so the time commitment will depend on your pace of learning. It is structured to include quizzes, practice tests, and challenges that reinforce learning, so dedicating regular study time each week will help you progress steadily through the material.
How will skills from this course carry over to other areas or careers?
The skills gained from this course, such as statistical analysis and Python programming, are applicable to a variety of fields, including data analysis, finance, research, and more. Understanding statistics is a valuable skill that enhances decision-making processes across industries, and Python is a widely used programming language in data science and analytics.