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Python for Business Data Analytics & Intelligence

Become a top Business Data Analyst. We’ll teach you everything you need to go from a complete beginner to getting hired as an analytics professional. You’ll learn to use Python and the latest industry tools and techniques to make data-driven decisions.

instructor

Taught by: Diogo Resende

Last updated: May 2023

Course overview

We guarantee you this is the most up-to-date and comprehensive course on learning how to use Python and the latest industry tools and techniques for business data analysis. You'll learn analytics by using real-world data and examples, including the data used in the hit movie Moneyball, to become a top Business Data Analyst and get HIRED this year.

What you'll learn

  • The skills to become a professional Business Analyst and get hired
  • Step-by-step guidance from an industry professional
  • Learn to use Python for statistics, causal inference, econometrics, segmentation, matching, and predictive analytics
  • Master the latest data and business analysis tools and techniques including Google Causal Impact, Facebook Prophet, Random Forest and much more
  • Participate in challenges and exercises that solidify your knowledge for the real world
  • Learn what a Business Analyst does, how they provide value, and why they're in demand
  • Analyze real datasets related to Moneyball, wine quality, Wikipedia searches, employee remote work satisfaction, and more
  • Learn how to make data-driven decisions
  • Enhance your proficiency with Python, one of the most popular programming languages
  • Use case studies to learn how analytics have changed the world and help individuals and companies succeed

What is business data analytics? Why learn business analytics? What does a business data analyst do?

Glad you asked!

We now live in a data-driven economy and companies around the world are in a race to make the best data-driven decisions.

Enter Business Data Analysts (future you!).

Being a Business Analyst is like being a detective.

You use tools (like Python, Facebook Prophet, Google Causal Impact) to investigate and analyze data to understand the past and predict what is most likely to happen in the future. From there, you'll determine the best course of action to take.

Companies need these Analysts because they're able to turn data into $$$.

They use the tools and techniques (that we teach you in this course) to quickly interpret and analyze data and turn it into actionable information and insights. These insights are relied upon to make key business decisions.

And making the right decision can be difference between gaining or losing millions of dollars.

That's why people with these data analysis skills are extremely in-demand. And why companies are willing to pay great salaries to attract them.

Using the latest industry techniques, this business data analytics course is focused on efficiency. So you never have to waste your time on confusing, out-of-date, incomplete tutorials anymore.

You'll learn by doing by completing exercises and fun challenges using real-world data. This will help you solidify your skills, push you beyond the basics and ensure that you have a deep understanding of each topic and feel confident using your new skills on any project you encounter.

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 as a Business Data Analyst. He teaches you the exact tools and techniques he uses in his role.

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

Just as the business data analytics & business intelligence ecosystems 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 analytics best practices anytime in the future.

Here's a section by section breakdown of what you'll learn in this course:

The curriculum is very hands-on. But you'll still be walked through everything step-by-step, so even if you have limited knowledge in statistics and Python, you'll have no problems getting up to speed.

We start from the very beginning by teaching you the fundamental building block of data analytics: statistics with Python.

But we don't stop there.

We'll then dive into advanced topics so that you can make good, analytical decisions and know which tools in your toolbox are right for any project.

1. Basic & Intermediary Statistics with Python - Statistics are the basis of analytics and are critical for analytical thinking. Even basic concepts like Mean, Standard Deviation, and Confidence Interval will be a game-changer in helping you interpret, challenge, and present your arguments and reasoning in the professional world.

You'll also learn how to calculate all this and more using one of the world's most popular programming languages: Python.

This section will also lay the foundation for you to understand the more advanced analytics concepts.

2. Linear, Multilinear, & Logistic Regression - You'll learn how and why to use Python for the most commonly used type of predictive analysis: regression.

The idea of regression is to examine the relationship between certain variables, and it's most commonly used in finance and investing, but it's relevant for every sector (if you want to impress your boss, analyze a relationship using regression!).

3. Econometrics & Causal Inference - Now you'll start learning more advanced topics. Econometrics & Causal Inference may sound scary, but they are probably the most important concepts for you to master to become a top Business Analyst.

They help you answer all sorts of problems using analytics and most importantly you'll be a better decision maker once you learn to use them. You will learn how to tackle biases, like the omitted variable bias or the self-selection bias, which are biases that companies very commonly fall victim too.

Once you know how to these concepts to help you find the solutions, you'll also learn how to better spot the problems.

4. Google Causal Impact - Now we'll start using some of the key tools that the real-world professionals use, starting with Google Causal Impact, an open-source package for estimating causal effects in time series.

How can we measure the number of additional clicks or sales that a digital ads campaign generated? How can we estimate the impact of a new feature on your app downloads?

In principle, these questions can be answered through causal inference. But in practice, estimating a causal effect accurately is hard, especially when a randomised experiment is not available. Thankfully, we can use Google Causal Impact to make causal analyses simple and fast.

5. Matching - Here you'll learn how to use data matching to compare data stored in different systems in and across organizations, helping you reduce data duplication and improve data accuracy. By the end, you'll know exactly when and how to use data matching to efficiently match and compare data.

6. RFM (Recency, Frequency, Monetary) Analysis - In this section, you'll learn about a marketing technique called RFM Analysis. It's used to quantitatively rank and group customers based on the recency, frequency and monetary total of their recent transactions to identify the best customers and perform targeted marketing campaigns.

So what does that mean?

Well, do you think Amazon or Facebook show each of their customers the same things? Spoiler alert: they definitely do not.

The truth is that some customers are essential for companies, and some don’t matter as much. The FAANG companies (and every company using analytics) uses RFM Analysis to determine who their key customers are, and how customers should be treated differently (aka the "VIP Treatment" 😉).

7. Gaussian Mixture - Now you're really cookin'! Next you'll learn about using Python to create a probabilistic model called Gaussian Mixture that's used for representing normally distributed sub-groups within a larger group.

Sound complex? That's because it is! But you're going to learn it all step-by-step so that you can use it for your own business or as a professional analyst!

8. Predictive Analytics - Random Forest, Facebook Prophet - Okay now this is the coolest part, where you start to utilize machine learning to predict the future (insert spooky sounds here).

In every company, there's always something that is being predicted, and humans simply can’t do it as well as machines.

Knowing the future means having an advantage over everyone else, and that is precisely the advantage that you'll be able to provide as an analyst by using predictive analytics.

That's why you're going to learn how to use tools like Random Forest and Facebook Prophet to harness the power of machines to predict the future and make actionable plans from that information.

What's the bottom line?

This course is not about making you just code along 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 someone that is in the top 10% of Business Data Analysts 💪.

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 business data analytics 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.

Start learning now

Course curriculum

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

Section 1 - Introduction

7 lectures

Python for Business Analytics & Intelligence2:34

PREVIEW

Introduction1:55

PREVIEW

Exercise: Meet Your Classmates and Instructor

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Setting up the Course Material9:40

PREVIEW

The Modern Day Business Analyst5:00

PREVIEW

How-to's: Speed up videos, Downloading videos, Subtitles

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Unlimited Updates

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PART A: STATISTICS

1 lectures

What are Statistics and why are they important?

PREVIEW

Section 2 - Basic Statistics

18 lectures

Basic Statistics - Game Plan1:06

PREVIEW

Arithmetic Mean1:56

PREVIEW

CASE STUDY: Moneyball (Briefing)0:58

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Python - Directory, Libraries and Data8:03

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Python - Mean9:16

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EXERCISE: Python - Mean2:20

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Median and Mode2:41

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Python - Median5:01

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EXERCISE: Python - Median2:57

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Python - Mode3:03

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EXERCISE: Python - Mode1:36

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Correlation4:16

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Python - Correlation8:41

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EXERCISE: Python - Correlation3:33

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Standard Deviation2:07

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Python - Standard Deviation2:23

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EXERCISE: Python - Standard Deviation1:04

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CASE STUDY: Moneyball3:56

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Section 3 - Intermediary Statistics

25 lectures

Intermediary Statistics - Game Plan0:46

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Normal Distribution3:00

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CASE STUDY: Wine Quality (Briefing)2:22

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Python - Preparing Script and Loading Data5:00

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Python - Normal Distribution Visualization7:34

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EXERCISE: Python - Normal Distribution5:41

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P-Value5:33

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Shapiro-Wilks Test1:51

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Python - Shapiro-Wilks Test7:42

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EXERCISE: Python - Shapiro-Wilks2:49

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Standard Error of the Mean2:36

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Python - Standard Error4:24

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EXERCISE: Python - Standard Error2:10

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Z-Score2:40

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Confidence Interval5:48

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Python - Confidence Interval6:23

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EXERCISE: Python - Confidence Interval2:19

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T-test2:17

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CASE STUDY: Remote Work Predictions (Briefing)0:39

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Python - T-test10:20

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EXERCISE: Python - T-test5:22

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Chi-square test2:28

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Python - Chi-square test7:29

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EXERCISE: Python - Chi-square3:14

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Powerposing and p-hacking3:20

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Section 4 - Linear Regression

12 lectures

Linear Regression - Game Plan1:27

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CASE STUDY: Diamonds (Briefing)0:57

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Linear Regression5:11

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Python - Preparing Script and Loading Data4:36

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Python - Isolate X and Y1:47

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Python - Adding Constant2:43

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Linear Regression Output3:36

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Python - Linear Regression Model and Summary3:20

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Python - Plotting Regression4:23

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Dummy Variable Trap3:09

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Python - Dummy Variable3:35

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EXERCISE: Python - Linear Regression5:51

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Section 5 - Multilinear Regression

22 lectures

Multilinear Regression - Game Plan1:34

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The Concept of Multilinear Regression1:45

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CASE STUDY: Professors' Salary (Briefing)0:45

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Python - Preparing Script and Loading Data5:05

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Python - Summary Statistics2:59

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Outliers2:43

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Python - Plotting Continuous Variables4:54

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Python - Correlation Matrix2:51

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Python - Categorical Variables4:30

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Python - For Loop4:43

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Python - Creating Dummy Variables3:09

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Python - Isolate X and Y3:28

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Python - Adding Constant1:26

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Under and Over Fitting1:32

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Training and Test Set1:03

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Python - Train and Test Split2:42

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Python - Multilinear Regression5:01

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Accuracy KPIs (Key Performance Indicators)3:19

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Python - Model Predictions1:31

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Python - Accuracy Assessment5:36

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CHALLENGE: Introduction5:08

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CHALLENGE: Solutions15:59

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Section 6 - Logistic Regression

20 lectures

Logistic Regression - Game Plan1:13

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CASE STUDY: Spam Emails (Briefing)1:00

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Logistic Regression2:06

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Python - Preparing Script and Loading Data4:16

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Python - Summary Statistics3:19

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Python - Histogram and Outlier Removal7:02

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Python - Correlation Matrix2:32

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Python - Transforming Dependent Variable2:39

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Python - Prepare X and Y2:09

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Python - Training and Test Set2:42

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How to Read Logistic Regression Coefficients2:40

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Python - Logistic Regression2:19

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Python - Function to Read Coefficients8:30

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Python - Predictions3:06

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Confusion Matrix6:17

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Python - Confusion Matrix5:25

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Python - Manual Accuracy Assessment7:05

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Python - Classification Report2:45

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CHALLENGE: Introduction4:49

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CHALLENGE: Solutions13:39

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PART B: ECONOMETRICS & CAUSAL INFERENCE

1 lectures

What are Econometrics & Causal Inference and why are they important?

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Section 7 - Google Causal Impact (Econometrics and Causal Inference)

23 lectures

Why Econometrics and Causal Inference4:20

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Google Causal Impact - Game Plan1:20

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Time Series Data1:30

PREVIEW

CASE STUDY: Bitcoin Pricing (Briefing)2:28

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Difference-in-Differences Framework2:21

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Causal Impact Step-by-Step2:20

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Python - Installing and Importing Libraries3:54

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Python - Defining Dates3:34

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Python - Bitcoin Price loading5:12

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Assumptions2:54

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Python - Load Control Groups3:59

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Python - Preparing DataFrame6:00

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Python - Preparing for Correlation Matrix2:42

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Correlation Recap and Stationarity4:16

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Python - Stationarity8:05

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Python - Correlation3:22

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Python - Google Causal Impact Setup2:41

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Python - Google Causal Impact3:23

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Interpretation of Results4:17

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Python - Impact Results5:04

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CHALLENGE: Introduction7:14

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CHALLENGE: Solutions13:13

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EXERCISE: Imposter Syndrome2:55

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Section 8 - Matching

28 lectures

Matching - Game Plan2:50

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Matching2:51

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CASE STUDY: Catholic Schools & Standardized Tests (Briefing)1:00

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Python - Directory and Libraries2:53

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Python - Loading Data2:23

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Unconfoundedness2:16

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Python - Comparing Means2:42

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Python - T-Test4:09

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Python - T-Test Loop4:37

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Python - Chi-square Test3:27

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Python - Chi-square Loop4:26

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Python - Other Variables1:49

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The Curse of Dimensionality1:40

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Python - Race Variable Transformation6:59

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Python - Education Variables5:30

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Python - Cleaning and Preparing Dataset3:31

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Common Support Region4:04

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Python - Logistic Regression and Debugging7:22

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Python - Preparing for Common Support Region5:39

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Python - Common Support Region Visualization1:41

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Python - Matching4:51

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Robustness Checks2:13

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Python - Robustness Check - Repeated experiments7:00

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Python - Outcome Visualization1:55

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Python - Robustness Check - Removing 1 confounder3:38

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CHALLENGE: Introduction5:25

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CHALLENGE: Solutions14:03

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My Experience with Matching2:41

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PART C: SEGMENTATION

1 lectures

What is Segmentation and why is it important?

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Section 9 - RFM (Recency, Frequency, Monetary) Analysis

18 lectures

RFM - Game Plan0:45

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Value Based Segmentation2:52

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RFM Model4:53

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CASE STUDY: Online Shopping (Briefing)0:53

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Python - Directory and Libraries2:17

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Python - Loading Data2:29

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Python - Creating Sales Variable1:45

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Python - Date Variable3:33

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Python - Customer Level Aggregation3:49

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Python - Monetary Variable1:23

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Python - Tidying up Dataframe2:52

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Python - Quartiles6:34

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Python - RFM Score1:51

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Python - RFM Function4:41

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Python - Applying RFM Function2:09

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Python - Results Summary4:29

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CHALLENGE: Introduction3:31

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CHALLENGE: Solutions12:16

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Section 10 - Gaussian Mixture

15 lectures

Gaussian Mixture - Game Plan1:10

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Clustering2:09

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Gaussian Mixture Model3:57

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CASE STUDY: Credit Cards #1 (Briefing)0:53

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Python - Directory and Data2:11

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Python - Load Data1:50

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Python - Transform Character variables1:21

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AIC and BIC2:15

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Python - Optimal Number of Clusters6:24

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Python - Gaussian Mixture Model1:11

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Python - Cluster Prediction and Assignment2:50

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Python - Interpretation7:46

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CHALLENGE: Introduction4:35

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CHALLENGE: Solutions18:04

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My Experience with Segmentation3:15

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PART D: PREDICTIVE ANALYTICS

1 lectures

What are Predictive Analytics and why are they important?

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Section 11 - Random Forest

21 lectures

Random Forest - Game Plan1:05

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Ensemble Learning and Random Forest2:16

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How Decision Trees Work4:19

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CASE STUDY: Credit Cards #2 (Briefing)0:37

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Python - Directory and Libraries2:02

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Python - Loading Data1:50

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Python - Transform Object into Numerical Variables1:43

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Python - Summary Statistics2:21

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Random Forest Quirks2:30

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Python - Isolate X and Y1:32

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Python - Training and Test Set3:40

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Python - Random Forest Model2:59

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Python - Predictions1:18

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Python - Classification Report and F1 score3:44

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Python - Feature Importance4:22

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Parameter Tuning2:45

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Python - Parameter Grid3:14

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Python - Parameter Tuning7:10

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CHALLENGE: Introduction4:24

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CHALLENGE: Solutions (Part 1)8:29

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CHALLENGE: Solutions (Part 2)9:40

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Section 12 - Facebook Prophet

32 lectures

Facebook Prophet - Game Plan1:20

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Structural Time Series2:25

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Facebook Prophet3:37

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CASE STUDY: Wikipedia (Briefing)0:51

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Python - Directory and Libraries2:05

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Python - Loading Data2:34

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Python - Transforming Date Variable2:48

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Python - Renaming Variables1:31

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Dynamic Holidays2:10

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Python - Easter Holidays5:16

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Python - Black Friday2:50

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Python - Combining Events and Preparing Dataframe2:33

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Training and Test Set2:12

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Python - Training and Test Set3:17

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Facebook Prophet Parameters2:13

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Additive vs. Multiplicative Seasonality2:37

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Facebook Prophet Model4:44

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Python - Regressor Coefficients1:49

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Python - Future Dataframe4:37

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Python - Forecasting2:19

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Python - Accuracy Assessment3:41

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Python - Visualization5:40

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Cross-validation1:07

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Python - Cross-validation7:59

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Parameters to tune1:22

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Python - Parameter Grid4:03

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Python - Parameter Tuning7:28

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CHALLENGE: Introduction4:47

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CHALLENGE: Solutions (Part 1)9:17

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CHALLENGE: Solutions (Part 2)11:07

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CHALLENGE: Solutions (Part 3)8:08

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Forecasting at Uber4:38

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Where To Go From Here?

4 lectures

Thank You!1:17

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Review This Course!

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Become An Alumni

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Become a ZTM Ambassador ➡ Refer new students. Earn cash.

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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
  • Basic Python knowledge. Don't know Python? No problem, you'll get access to our Python Bootcamp here as well where we'll teach you Python from scratch
  • A willingness and enthusiasm to learn and take action

Who is this course for?

  • Developers that want a step-by-step guide to learn and master Business Data Analytics from scratch all the way to being able to get hired at a top company
  • Students who want to go beyond all of the "beginner" Python and Data Analytics tutorials out there
  • Developers that want to use their skills in a new discipline
  • Programmers who want to learn one of the most in-demand skills
  • Developers that want to be in the top 10% of Business Data Analysts
  • Students who want to gain experience working on large, interesting datasets
  • Bootcamp or online tutorial graduates that want to go beyond the basics
  • Students who want to learn from an industry professional with real-world experience, not just another online instructor that teaches off of documentation

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.

Can I download the videos?

Definitely. You can download any and all lessons for personal use. We do everything we can to make learning easy, fun and accessible. Whether that’s on your commute, on a flight or just when you have limited access to good WiFi.

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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