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Statistics Bootcamp (with Python): Zero to Mastery

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.

42 Days

Average time students take to complete this course.

instructor
Taught by: Diogo Resende
Last updated: April 2024

Course overview

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.

What you'll learn

  • The skills to get hired in statistics-heavy fields like Data Analytics, Data Science, A.I., and Machine Learning
  • Learn the statistical concepts and skills needed for real-world jobs, including confidence intervals, hypothesis testing, multilinear regression, and cox proportional hazard regression
  • Learn to use Python from scratch to conduct statistical analysis (no prior knowledge required!)
  • Learn how to use ChatGPT to work with statistics, clean datasets, and to conduct data analysis more efficiently
  • Test your knowledge with more than 10 quizzes and practice tests
  • Build your skills with 6 capstone statistics-based projects including a Virtual Escape Room, Surviving Titanic, and more
  • Participate in case studies to understand how statistics have real-world impacts
  • Step-by-step guidance from an industry professional

Why learn Statistics with Python?

Whether you realize it or not...statistics is everywhere around you!

Everything is probabilities and relationships. Statistics is the science and art of collecting, analyzing, interpreting, and presenting data on those probabilities and relationships.

At its core, it aims to derive meaningful insights and make informed decisions based on the data, often amidst the inherent uncertainty and variability.

But perhaps more importantly, it's important for a huge array of jobs.

Businesses, governments, and organizations frequently use statistics to make decisions, and with the help of statistical tools one can predict future trends, which is essential in fields like finance, economics, and meteorology.

Plus statistics is the foundation for careers in tech like:

  • Data Analyst
  • Data Scientist
  • A.I. Machine Learning Engineer
  • And even non-tech careers like Economists and Financial Analysts!

In fact, there is such a high demand for those with a deep understanding of how to work with statistics that you can even get hired as a pure Statistician that designs surveys, interprets data, and provides insights.

And unlike other online courses and tutorials, you won't be learning alone.

Because by enrolling today, you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.

Most importantly, you'll be learning from an industry professional (Diogo) that has actual real-world experience working with statistics in numerous capacities. He teaches you the exact concepts, tools, and techniques he uses in his role.

Finally, this course will be constantly updated as the landscape changes.

As the tools and technologies evolve, we will ensure this course is constantly updated with new lectures and resources so that you will stay at the top of your field.

This course will be your go-to place to get all the latest statistics best practices now and in the future.

Here's a closer look at what you'll learn in this Statistics course:

This curriculum is very hands-on. But you'll still be walked through everything step-by-step in a beginner-friendly manner, meaning even if you have no prior knowledge in statistics or python you'll still have no problems getting up to speed.

You'll start from the very beginning by teaching you the fundamental building block of statistics and python.

But we don't stop there.

You'll then dive into using tools like ChatGPT for data analysis, advanced statistical concepts, building awesome probability-based projects, and working on challenges so that you can understand statistics and python on a deeper level.

Here's some of the key subjects and skills you'll be learning:

1. Introduction:

we'll start with an overview of the course, guiding you through setting up a Google Account, downloading and setting up course materials, and understanding the significance of statistics with reference to historical events like the Challenger Space Shuttle Disaster.

2. Python for Statistics - Essentials:

Dive deep into the fundamental elements of Python, essential for statistical analysis. This includes an understanding of the print and input functions, variable types, arithmetic operations, and control structures.

Practical challenges help reinforce these concepts.

3. Python for Statistics - Intermediate:

Build upon the essentials by learning intermediate Python topics like loops, lists, dictionaries, and randomization.

Practical exercises and challenges allow you to apply these concepts, ultimately leading them towards creating useful functions.

4. ChatGPT for Data Analysis:

ChatGPT and LLMs have changed the landscape for those working with data. It won't replace your job, but you need to know how to use it effectively to excel in your job.

Throughout this course you'll learn the real-world ChatGPT skills needed to work effectively with statistics, manipulate and clean datasets, and conduct data analysis efficiently.

5. Descriptive Statistics:

Focusing on descriptive statistical techniques, you'll learn about different types of variables, populations vs. samples, measures of central tendency like mean, median, mode, and other core concepts.

You'll also use Python to compute and visualize these statistics.

6. Confidence Intervals:

Learn about constructing and interpreting confidence intervals, which provide a range of plausible values for an unknown parameter. This involves understanding standard errors, Z-scores, and other foundational statistical concepts, all while applying them in Python.

7. Hypothesis Testing:

Central to inferential statistics, hypothesis testing is explored in-depth here. Learners will familiarize themselves with concepts like p-values, errors, different testing techniques, and apply them using Python.

Real-world case studies like that of Tesla Production give a practical edge to the learning.

8. Multilinear Regression:

Dive deeper than simple linear regression by learning about multilinear regression, which employs multiple predictors.

Through Python exercises, students will explore multicollinearity, model optimization, and the significance of predictors, enabling them to glean intricate insights from multifaceted datasets.

9. Logistic Regression:

Shifting to binary outcomes, this segment delves into logistic regression, essential for predicting categorical responses.

You will grasp the underlying theory, including the logit function and odds ratios, and apply these in Python to classify outcomes and evaluate model accuracy.

10. Cox Proportional Hazard Regression:

Tackle survival analysis via the Cox Proportional Hazard Regression model. Emphasizing proportional hazards assumption, you will immerse in key concepts like hazard ratios and censoring, using Python to handle time-to-event datasets adeptly.

What's the bottom line?

This course is not about making you just watch without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial... No!

This course will push you and challenge you to go from an absolute beginner to having a deep understanding of statistics and how to utilize them in real-world jobs 💪.

How do we know?

Because thousands of Zero To Mastery graduates have gotten hired and are now working at companies like Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, Shopify + other top tech companies.

They come from all different backgrounds, ages, and experiences. Many even started as complete beginners.

So there's no reason it can't be you too.

And you have nothing to lose. Because you can start learning right now and if this course isn't everything you expected, we'll refund you 100% within 30 days. No hassles and no questions asked.

When's the best time to get started? Today!

There's never a bad time to learn in-demand skills. But the sooner, the better. So start learning statistics with Python today by joining the ZTM Academy. You'll have a clear roadmap to developing the skills to build your own projects, get hired, and advance your career.

Join Zero To Mastery Now

What you'll build

The best way you learn is by doing. Not just watching endless tutorials. That's why a key part of this course is the capstone projects you'll build involving fun, statistics-based tasks. Plus they'll look great on your portfolio.

Virtual Escape Room

Virtual Escape Room

Design a story-driven virtual escape room where players solve a series of puzzles, including riddles, code-breaking challenges, and pattern recognition tasks, to progress through an exciting storyline.

Lights, Camera, Statistics!

Lights, Camera, Statistics!

Using inferential statistics and confidence intervals, you'll explore the relationships between movie genres, box office performance, and other factors that can turn a modest flick into a blockbuster hit.

Yelp Me!

Yelp Me!

Using ChatGPT you'll test different hypotheses to figure out how to increase a restaurant's Yelp ratings. You'll dig through feedback, formulate tests, and more. It's all about finding what gets people talking and coming back for more.

Titanic Survivor Prediction

Titanic Survivor Prediction

Build a logistic regression model that can predict whether a passenger survived the tragic sinking of the Titanic based on various factors, such as age, sex, and fare. You'll explore, clean, and prepare the famous real-world Titanic dataset.

Surviving the App-pocalypse

Surviving the App-pocalypse

You'll identify the factors that contribute to app churn and estimate the survival probability of apps on the Google Play Store. Understanding these factors can help developers make data-driven decisions to improve their apps and marketing strategies.

Sales Drivers

Sales Drivers

Figure out how to drive retail sales by using multilinear regression and data visualization techniques to understand the ability of your model to predict KPIs and sales drivers.

Join Zero To Mastery Now

Course curriculum

To make sure this course is a good fit for you, you can start learning Statistics with Python for free right now by clicking any of the PREVIEW links below.

Introduction

6 lectures

Learn Statistics with Diogo Resende1:13

PREVIEW

Course Outline2:49

PREVIEW

Why Statistics Matter - The Challenger Space Shuttle Disaster3:31

PREVIEW

Exercise: Meet Your Classmates and Instructor

PREVIEW

Understanding Your Video Player (notes, video speed, subtitles + more)

PREVIEW

Set Your Learning Streak Goal

PREVIEW

Getting Started

3 lectures

Creating a Google Account5:23

BEGIN

Resources - Download Course Materials

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Setting Up the Course Materials3:23

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Part 1 - Python for Statistics and Data Analysis

1 lectures

Overview

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Statistics with Python - The Essentials

21 lectures

Game Plan for Python Essentials1:53

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Print Function5:47

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Python - Print Function10:27

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Input Function4:19

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Python - Input Function8:08

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CHALLENGE - Your Superhero Name6:50

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Variable Types3:01

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Python - Variable Types6:26

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Arithmetic Operators6:03

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Python - Arithmetic Operators6:47

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Comparison Operators3:40

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Python - Comparison Operators4:56

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CHALLENGE - Split Bill Calculator11:28

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The if-else Condition6:21

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Python - if-else Conditions6:01

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EXERCISE - Can You Vote?2:28

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EXERCISE - Grading Papers5:21

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CHALLENGE - Berghain Club Bouncer11:54

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CHALLENGE - Your Monthly Savings Plan19:43

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Wrap Up - Python Essentials2:21

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Let's Have Some Fun (+ Free Resources)

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Statistics with Python - Intermediate

29 lectures

Game Plan for Intermediate Python for Statistics3:07

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While Loop2:26

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Python - While Loops6:44

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EXERCISE - Countdown Times5:02

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Python Lists7:00

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Python - Lists10:30

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EXERCISE - Monthly Expense Report6:42

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EXERCISE - Fibonacci Sequence6:06

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Randomization4:35

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Python - Randomization5:24

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EXERCISE - Movie Picker6:21

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CHALLENGE - Read my Mind9:00

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Dictionaries4:02

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Python - Dictionaries6:30

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EXERCISE - Magical Pet Sounds7:35

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CHALLENGE - Budget Mastermind16:19

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For Loops4:41

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Python - For Loops4:18

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EXERCISE - Sum of Numbers4:26

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EXERCISE - Counting the Number of Characters7:58

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CHALLENGE - Treasure Hunter14:08

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Functions6:48

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Python - Functions6:18

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EXERCISE - Function That Adds Numbers2:20

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EXERCISE - Function That Counts Vowels4:35

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EXERCISE - Function That Transforms Fahrenheit to Celsius6:34

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CHALLENGE - Recipe Converter24:40

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Wrap Up - Python Intermediate Skills3:23

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Unlimited Updates

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Capstone Project #1 - Virtual Escape Room with Python

6 lectures

Project Presentation - Virtual Escape Game5:51

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Python - Plan the Solution8:15

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Python - Check User's Answer Function REAL8:47

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Python - Prepare Game18:11

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Python - Solving with ChatGPT6:54

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Course Check-In

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Part 2 - Inferential Statistics

1 lectures

Overview

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Descriptive Statistics

37 lectures

Game Plan for Descriptive Statistics1:50

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Variable Types in Statistics2:55

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QUIZ - Variable Types

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QUIZ - Variable Types - Explanations

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Population vs. Sample3:28

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CASE STUDY Briefing - Moneyball2:16

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Python - Setting Up5:51

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Measures of Central Tendency3:13

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(Arithmetic) Mean3:38

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Python - Mean4:44

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EXERCISE - Python2:35

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Median2:16

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Python - Median1:51

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EXERCISE - Median1:08

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Mode1:27

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Python - Mode2:34

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EXERCISE - Mode2:36

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Standard Deviation and Variance4:56

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Python - Standard Deviation and Variance5:26

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EXERCISE - Standard Deviation and Variance2:37

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Coefficient of Variation4:31

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Python - Coefficient of Variation3:25

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EXERCISE - Coefficient of Variation1:03

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Covariance4:14

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Python - Covariance3:47

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EXERCISE - Covariance1:51

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Correlation6:54

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Python - Correlation5:36

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EXERCISE - Correlation2:00

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Normal Distribution4:08

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Python - Normal Distribution6:47

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EXERCISE - Normal Distribution3:11

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PRACTICE TEST: Descriptive Statistics

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PRACTICE TEST: Descriptive Statistics - Explanations

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CASE STUDY - Moneyball4:14

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Wrap Up - Descriptive Statistics1:55

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Implement a New Life System

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Confidence Intervals

20 lectures

Game Plan for Confidence Intervals1:04

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CASE STUDY Briefing - Dioguinis Pizza1:29

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Standard Error of the Sample Mean2:17

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Python - Libraries and Data4:39

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Python - Standard Error of the Sample Mean2:47

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Z-Score and Standardization3:12

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Python - Z-Score and Standardization9:57

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Confidence Level4:49

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Python - Confidence Level10:30

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Confidence Intervals for Large Samples6:17

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Python - Confidence Interval for Large Samples6:12

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EXERCISE - Confidence Interval Function with ChatGPT6:55

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CASE STUDY - Guinness Beer and t-distribution2:35

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Confidence Interval with Small Samples3:26

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Degrees of Freedom7:13

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Python - Confidence Interval with Small Samples8:14

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EXERCISE - Confidence Interval Function with ChatGPT5:11

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PRACTICE TEST: Confidence Intervals

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PRACTICE TEST - Confidence Intervals - Explanations

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Confidence Intervals Wrap Up4:27

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Capstone Project #2 - Lights, Camera, Statistics!

6 lectures

Project Presentation - Lights, Camera, Statistics2:40

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Python - Data Preparation and Cleaning20:52

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Python - Exploratory Data Analysis16:55

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Python - Estimating Average Ratings11:57

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Python - Conclusions5:43

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Exercise: Imposter Syndrome2:55

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Hypothesis Testing

40 lectures

Game Plan for Hypothesis Testing3:07

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What is Hypothesis Testing?4:54

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QUIZ - Hypothesis Testing

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QUIZ - Hypothesis Testing - Explanations

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P-Value4:36

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QUIZ - P-value

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QUIZ - P-value - Explanations

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Type I and Type II Errors4:05

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QUIZ - Type I and Type II Errors

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QUIZ - Type I and Type II Errors - Explanations

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CASE STUDY - Publication Bias in Statistics2:58

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How to Test Your Hypothesis (Known Population Variance).6:50

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CASE STUDY Briefing - Tesla Production1:48

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Python - Setting Up and Libraries2:52

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Python - How to Test Your Hypothesis (Known Population Variance)12:00

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Python - Build a Function to Test Your Known Variance Hypothesis5:22

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Hypothesis Testing with Unknown Population Variance2:54

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Python - How to Test Your Hypothesis (Unknown Population Variance) - Part 111:02

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Python - How to Test Your Hypothesis (Unknown Population Variance) - Part 26:38

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Paired T-Test3:54

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Python - Paired T-Test - Part 110:39

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Python - Paired T-Test - Part 23:30

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Two Sample T-Test5:30

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Python - Levene's Test8:04

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Python - Welch's T-Test3:44

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Python - Two-Sample T-Test2:12

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Exercise - Two-Sample Test Function4:41

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One-Tailed Test vs. Two-Tailed Test5:50

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Python - One-Tailed Test with Known Variance7:27

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Python - One-Tailed Test with Unknown Variance5:39

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Python - One-Tailed Paired T-Test5:54

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Python - One-Tailed Two-Sample T-Test5:29

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Chi-Square Test3:08

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Python - Chi-Square Test10:59

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Is Your Distribution Normal? - The Shapiro-Wilks Test2:43

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Python - Shapiro-Wilks Test5:34

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PRACTICE TEST - Hypothesis Testing

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PRACTICE TEST - Hypothesis Testing - Explanations

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Hypothesis Testing Wrap Up2:43

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Powerposing and P-Hacking3:39

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Capstone Project #3 - ChatGPT Data Analysis

5 lectures

Capstone Project with ChatGPT - Yelp me!2:43

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Python Solutions - Data14:41

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Python Solutions - Hypothesis 111:19

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Python Solutions - Hypothesis 29:07

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Python Solutions - Hypothesis 38:41

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Part 3 - Regression Analysis

1 lectures

Overview

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Multilinear Regression

26 lectures

Game Plan for Multilinear Regression1:22

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CASE STUDY Briefing - Pricing Diamonds1:53

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Linear Regression5:12

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Python - Libraries and Data4:20

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Python - Exploratory Data Analysis4:57

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Python - Linear Regression3:09

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Regression Statistics4:23

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Python - Linear Regression Output2:29

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Python - Plotting Regression Curve2:56

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Dummy Variable (Trap)4:00

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Python - Linear Regression with Dummy Variables6:52

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EXERCISE - Create Function that Reads the Regression Coefficients with ChatGPT7:10

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CASE STUDY - Linearity Bias - We Will All Be Obese! Wait What?4:01

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Multilinear Regression1:47

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Python - Categorical Variables5:47

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Python - Multilinear Regression Preparation2:10

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Under and Overfitting3:27

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Training and Test Set2:34

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Python - Training and Test Split3:14

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Python - Multilinear Regression8:42

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Assessing Regression Models6:07

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Python - Assessing Regression Model6:19

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PRACTICE TEST - Multilinear Regression

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PRACTICE TEST - Multilinear Regression - Explanations

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CASE STUDY - Dangers of Regression Analysis2:51

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Multilinear Regression Wrap Up2:02

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Capstone Project #4 - Multilinear Regression

4 lectures

Capstone Project - Understanding Sales Drivers1:07

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Python - Solutions - Step 17:13

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Python - Solutions - Steps 2-45:37

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Python - Solutions - Steps 5-67:15

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Logistic Regression

18 lectures

Game Plan for Logistic Regression1:39

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CASE STUDY Briefing - Spam Emails1:25

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Logistic Regression3:28

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Python - Preparing Script and Loading Data3:31

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Python - Summary Statistics3:44

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Python - Histograms and Outlier Detection5:36

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Python - Correlation Matrix3:26

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Python - Logistic Regression Preparation3:59

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How to Read Logistic Regression Coefficients2:11

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Python - Logistic Regression2:17

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Python - Build a Coefficient Function with ChatGPT9:06

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Python - Predictions3:19

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Confusion Matrix and Model Assessment6:24

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Python - Confusion Matrix and Classification Report5:34

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Python - Assessing Classification Models with ChatGPT5:30

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PRACTICE TEST - Logistic Regression

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PRACTICE TEST - Logistic Regression - Explanations

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Section Wrap Up - Logistic Regression3:15

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Capstone Project #5 - Surviving Titanic - Logistic Regression with ChatGPT

4 lectures

Capstone Project - Surviving Titanic1:33

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Python - Libraries and Data with ChatGPT8:12

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Python - Removing Outliers and EDA with ChatGPT12:34

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Python - Logistic Regression Model and Assessment with ChatGPT23:28

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Cox Proportional Hazard Regression

22 lectures

Game Plan for Cox Proportional Hazard Regression2:14

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Introduction to Survival Analysis7:47

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CASE STUDY - Briefing1:47

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Python - Libraries and Data5:09

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Kaplan-Meier Estimator4:35

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Python - Kaplan Meier Estimator4:22

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Python - Calculating for a Specific Event2:46

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Python - Plotting Kaplan-Meier and Cumulated Curves3:51

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Censoring3:45

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Log Rank Test2:55

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Python - Kaplan-Meier Estimator per Gender and Visualization5:50

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Python - Log Rank Test6:33

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Cox Proportional Hazard Regression4:51

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Python - Prepare Data for CPH Model3:11

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Python - Cox Proportional Hazard Regression9:36

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Python - Visualize Results2:12

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Assessing Cox Proportional Hazard Models5:18

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Python - Assessing the CPH Model8:37

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Python - Predicting Specific Instances3:38

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PRACTICE TEST - Cox Regression

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PRACTICE TEST - Cox Regression - Explanations

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Cox Proportional Hazard Regression Wrap Up3:14

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Capstone Project #6 - Cox Regression with ChatGPT

6 lectures

Capstone Project - Will Your App Make it?1:23

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Python - Libraries and Data7:10

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Python - Data Cleaning19:10

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Python - Dependent Variable8:26

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Python - Kaplan-Meier Estimator4:30

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Python - Cox Model10:00

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Where To Go From Here?

6 lectures

Thank You!1:17

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Review This Course!

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Become An Alumni

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Learning Guideline

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ZTM Events Every Month

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LinkedIn Endorsements

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Meet your instructor

Your instructor (Diogo) isn't just an expert with years of real-world professional experience. He has been in your shoes. He makes learning fun. He makes complex topics feel simple. He will motivate you. He will push you. And he will go above and beyond to help you succeed.

Diogo Resende

Hi, I'm Diogo Resende!

Diogo has been working for over a decade as a data scientist. He loves harnessing the power of data and analytics to understand what has happened, what will happen next, and how to use that information to your advantage.

SEE MY BIO & COURSES

Diogo Resende

Data Scientist

Frequently asked questions

Are there any prerequisites for this course?

  • A computer (Windows, Mac, or Linux) with an internet connection
  • No prior Math or Python knowledge is required! You'll learn everything from scratch
  • A willingness and enthusiasm to learn and take action

Who is this Statistics course for?

  • Students who have struggled with math or coding but want to learn the wonders of Statistics
  • Students who want to go beyond all of the "beginner" Statistics tutorials out there
  • Individuals who want a launchpad for a career using statistics including Data Analyst, Data Scientist, and AI Machine Learning Engineer
  • Developers that want to use their skills in a new discipline
  • Students who want to gain experience working on large, interesting datasets
  • Bootcamp or online tutorial graduates who want to go beyond the basics
  • Students who want to learn from an industry professional with real-world experience who is there to help you every step of the way

Do you provide a certificate of completion?

We definitely do and they are quite nice. You will also be able to add Zero To Mastery Academy to the education section of your LinkedIn profile as well.

Are there subtitles?

Yes! We have high quality subtitles in 11 different languages: English, Spanish, French, German, Dutch, Romanian, Arabic, Hindi, Portuguese, Indonesian, and Japanese.

You can even adjust the text size, color, background and more so that the subtitles are perfect just for you!

Still have more questions about the Academy?

Still have more questions specific to the Academy membership? No problem, we answer some more here.

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