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Introduction to Inferential Statistics

9h 25m 55s
English
Paid

Introduction to Inferential Statistics is a 112-lesson 9 hours 25 minutes self-paced course by Zero To Mastery. This course gives you a clear and practical start in inferential statistics.

Course facts

Lessons
112
Duration
9 hours 25 minutes
Level
All levels
Language
English
Updated
Instructor
Zero To Mastery
Price
Premium

This course gives you a clear and practical start in inferential statistics. You move step by step and learn how to use stats to make sound decisions from data.

What You Learn

You begin with descriptive stats and grow into core tools in data analysis. You learn how these tools work and how to use them with real data.

Key Topics

  • Confidence intervals to measure uncertainty
  • Hypothesis tests for clear comparisons
  • ANOVA for group analysis
  • Nonparametric tests when data breaks common rules

Work With Python

You apply every idea in Python. You see how formulas turn into code. You run tests, read results, and explain what they mean.

Practical Skills

You work with real and messy datasets. You learn to pick the right method, check your data, and judge if a result makes sense.

Methods You Practice

  • Check metrics and spot issues
  • Run t-tests for simple comparisons
  • Choose the right test for your data shape
  • Make clear decisions based on evidence

Capstone Projects

You end the course with projects that mirror real work. You combine all methods you learned and explain your findings as you would in a job setting.

Who This Course Helps

The course fits you if you aim for your first role in analytics or data science. It also fits you if you want to sharpen your statistical thinking and use tools that teams rely on each day.

Who teaches Introduction to Inferential Statistics? Zero To Mastery

Zero To Mastery thumbnail

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 Introduction to Inferential Statistics?

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#1: Introduction
All Course Lessons (112)
#Lesson TitleDurationAccess
1
Introduction Demo
02:42
2
Game Plan for Descriptive Statistics
01:51
3
Variable Types in Statistics
02:56
4
Population vs. Sample
03:29
5
CASE STUDY Briefing - Moneyball
02:17
6
Python - Setting Up
05:52
7
Measures of Central Tendency
03:14
8
(Arithmetic) Mean
03:39
9
Python - Mean
04:45
10
EXERCISE - Mean
02:36
11
Median
02:17
12
Python - Median
01:52
13
EXERCISE - Median
01:09
14
Mode
01:28
15
Python - Mode
02:35
16
EXERCISE - Mode
02:37
17
Standard Deviation and Variance
04:57
18
Python - Standard Deviation and Variance
05:27
19
EXERCISE - Standard Deviation and Variance
02:38
20
Coefficient of Variation
04:32
21
EXERCISE - Coefficient of Variation
01:04
22
Python - Coefficient of Variation
03:26
23
Covariance
04:15
24
Python - Covariance
03:48
25
EXERCISE - Covariance
01:52
26
Correlation
06:55
27
Python - Correlation
05:37
28
EXERCISE - Correlation
02:01
29
Normal Distribution
04:09
30
Python - Normal Distribution
06:48
31
EXERCISE - Normal Distribution
03:12
32
CASE STUDY - Moneyball
04:15
33
Wrap Up - Descriptive Statistics
01:56
34
Game Plan for Confidence Intervals
01:05
35
CASE STUDY Briefing - Dioguinis Pizza
01:30
36
Standard Error of the Sample Mean
02:17
37
Python - Libraries and Data
04:40
38
Python - Standard Error of the Sample Mean
02:48
39
Z-Score and Standardization
03:13
40
Python - Z-Score and Standardization
09:57
41
Confidence Level
03:49
42
Python - Confidence Level
10:31
43
Confidence Intervals for Large Samples
06:18
44
Python - Confidence Interval for Large Samples
06:13
45
EXERCISE - Confidence Interval Function with ChatGPT
06:56
46
CASE STUDY - Guinness Beer and t-distribution
02:36
47
Degrees of Freedom
07:14
48
Confidence Interval with Small Samples
03:27
49
Python - Confidence Interval with Small Samples
08:15
50
EXERCISE - Confidence Interval Function with ChatGPT
05:12
51
Confidence Intervals Wrap Up
04:28
52
Project Presentation - Lights, Camera, Statistics
02:41
53
Python - Data Preparation and Cleaning
20:53
54
Python - Exploratory Data Analysis
16:56
55
Python - Estimating Average Ratings
11:58
56
Python - Conclusions
05:44
57
Game Plan for Hypothesis Testing
03:08
58
What is Hypothesis Testing?
04:55
59
P-Value
04:37
60
Type I and Type II Errors
04:06
61
CASE STUDY - Publication Bias in Statistics
02:58
62
How to Test Your Hypothesis (Known Population Variance).
06:51
63
CASE STUDY Briefing - Tesla Production
01:49
64
Python - Setting Up and Libraries
02:52
65
Python - How to Test Your Hypothesis (Known Population Variance)
12:01
66
Python - Build a Function to Test Your Known Variance Hypothesis
05:23
67
Hypothesis Testing with Unknown Population Variance
02:55
68
Python - How to Test Your Hypothesis (Unknown Population Variance) - Part 1
11:03
69
Python - How to Test Your Hypothesis (Unknown Population Variance) - Part 2
06:39
70
Paired T-Test
03:55
71
Python - Paired T-Test - Part 1
10:40
72
Python - Paired T-Test - Part 2
03:30
73
Two Sample T-Test
05:31
74
Python - Levene's Test
08:05
75
Python - Welch's T-Test
03:45
76
Python - Two-Sample T-Test
02:13
77
Exercise - Two-Sample Test Function
04:42
78
One-Tailed Test vs. Two-Tailed Test
05:51
79
Python - One-Tailed Test with Known Variance
07:28
80
Python - One-Tailed Test with Unknown Variance
05:40
81
Python - One-Tailed Paired T-Test
05:55
82
Python - One-Tailed Two-Sample T-Test
05:30
83
Chi-Square Test
08:55
84
Python - Chi-Square Test
10:18
85
Is Your Distribution Normal? - The Shapiro-Wilks Test
02:44
86
Python - Shapiro-Wilks Test
05:35
87
Powerposing and P-Hacking
03:40
88
Hypothesis Testing Wrap Up
02:44
89
Capstone Project with ChatGPT - Yelp me!
02:44
90
Python Solutions - Data
14:42
91
Python Solutions - Hypothesis 1
11:20
92
Python Solutions - Hypothesis 2
09:08
93
Python Solutions - Hypothesis 3
08:42
94
Game Plan for Advanced Hypothesis Testing
04:52
95
Python - Setup
04:08
96
Mann-Whitney U Test
06:25
97
Python - Box plot for Normality
03:33
98
Python - Shapiro Wilks Test
03:33
99
D'Agostino and Pearson Test
05:01
100
Python - D'Agostino and Pearson Test
03:00
101
Python - Mann-Whitney U Test
03:54
102
ANOVA
04:38
103
Python - ANOVA
08:39
104
Python - D'Agostino and Pearson Test
02:07
105
Kruskal-Wallis Test
03:32
106
Python - Kruskal-Wallis Test
03:15
107
Spearman Correlation
04:35
108
Python - Spearman Correlation
05:37
109
Wilcoxon Signed-Rank Test
04:07
110
Python - Wilcoxon Signed-Rank Test
08:08
111
Key Learnings and Outcomes - Advanced Hypothesis Testing
02:33
112
Let's Keep Learning Together!
00:52
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What courses are similar to Introduction to Inferential Statistics?

Frequently asked questions

What prerequisites do I need for this course?
This course does not explicitly list prerequisites, making it suitable for beginners. However, familiarity with basic Python programming and a general understanding of statistics can help you grasp the concepts more quickly. The course covers Python setup and coding for statistical methods, so some prior exposure to Python will be beneficial.
What projects will I work on during the course?
The course includes capstone projects that mirror real-world work scenarios. You will combine all the methods learned, such as hypothesis testing, confidence intervals, and ANOVA, to analyze data and present findings. These projects are designed to simulate tasks you might encounter in a data analysis or data science role.
Who is the target audience for this course?
The course is ideal for individuals aiming for their first role in analytics or data science. It is also suitable for those looking to enhance their statistical thinking and practical application of statistical tools. The content is structured to aid both beginners and those looking to deepen their understanding of inferential statistics.
How does the depth of this course compare to similar courses?
This course offers a practical approach to inferential statistics, covering key topics like confidence intervals, hypothesis tests, and ANOVA. It provides hands-on experience with Python, allowing learners to apply theoretical concepts in coding exercises. Compared to other introductory courses, it emphasizes real-world application and data handling.
Are there any statistical topics not covered in this course?
The course focuses on fundamental inferential statistics topics such as confidence intervals, hypothesis testing, ANOVA, and nonparametric tests. It does not cover advanced statistical methods like regression analysis or machine learning algorithms, which may be found in more specialized or advanced courses.
What is the time commitment for completing this course?
The course consists of 112 lessons, each designed to build on the previous one. The total runtime is not specified, but the course's step-by-step structure suggests a moderate time investment. Learners are encouraged to practice coding exercises and capstone projects to reinforce their understanding, which may require additional time.
How does this course prepare me for a career in data science?
By completing this course, you gain practical skills in handling real and messy datasets, performing hypothesis tests, and making data-driven decisions. These skills are crucial for entry-level positions in data science and analytics. The focus on using Python for statistical analysis provides a foundation that is applicable in various data-centric roles.