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Business Analytics Bootcamp (with Python): Zero to Mastery

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 by learning cutting-edge tools and techniques to make data-driven decisions.

20 Days

Average time students take to complete this course.

instructor
Taught by: Diogo Resende
Last updated: March 2024

Course overview

We guarantee you this is the most up-to-date and comprehensive course on learning the latest industry tools and techniques for business data analysis. You'll learn analytics by using real-world data and examples 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 data visualization, 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
  • 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 the 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 breakdown of what you'll learn in this course:

The curriculum is very hands-on. But you'll still be taught 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.

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. A/B TESTING AND EXPERIMENTATION

You'll dive deeper into the science of decision-making with A/B Testing and Experimentation. From formulating hypotheses to mastering A/B testing techniques, you'll learn to leverage statistical methods and Python to validate business strategies, drawing insights from significant industry case studies such as Google's subtle nuances in design and Obama's campaign optimizations.

Here's a more detailed breakdown of what you'll learn about A/B Testing:

  • Introduction to A/B Testing

You'll dive into a core tenant of business analytics: A/B testing. And we'll make sure it's interesting and fun but focusing on mobile gaming. From setting up your test to analyzing results with Python, you'll learn crucial A/B testing terminology and principles.

  • Hypothesis Testing for A/B Testing

Build upon your foundation with hypothesis testing. You'll solidify your new skills with Python exercises and case studies like Google's "41 Shades of Blue" to understand confidence levels and p-values.

  • Mastering A/B Testing

Finally you'll A/B testing with Amazon's "Buy Button" case study. Engage with Bayesian A/B testing, sequential testing, and other advanced techniques for nuanced insights.

  • Multivariate A/B Testing

Okay I lied, that's not it...because we go from zero to true mastery here, and to do that you need to go deep! So you'll explore the depths of multivariate A/B testing with Google's "Homepage Experiment" to evaluate and predict outcomes using advanced Python analysis.

2. ECONOMETRICS AND CAUSAL INFERENCE

This is a core tenant for any great business analyst. With econometrics and causal inference, you’ll learn to measure the actual impact of business initiatives.

Using cutting edge business analysis tools like Google Causal Impact and methods like matching, this part provides a robust framework for understanding and executing causal analysis with real-world applications in cryptocurrency markets and educational program evaluations.

Here's a more detailed breakdown of what you'll learn about Econometrics and Causal Inference:

  • Google Causal Impact

You'll also dive deep on using Google Causal Impact by analyzing Bitcoin market data as a case study to understand the difference-in-differences framework and time series data analysis.

  • Matching

You'll discover the power of matching in econometrics with real-world applications. Learn from practical Python examples and a case study on the impact of educational programs.

3. DATA VISUALIZATION WITH PYTHON

You'll learn to transform raw data into compelling stories using data visualization. Starting with the essentials of visual representation through various chart types, you'll then move on to enhancing your analytical narratives. This part takes you through the creation of intuitive and insightful plots, with case studies including the analysis of Pokémon data sets and global happiness metrics.

  • Distribution Charts and Plotting Basics

Learn the fundamentals of data storytelling with distribution charts, histograms, box plots, and violin plot designs by having fun and using Pokémon data for practical insights.

  • Ranking Charts

You'll learn to visualize competitive analysis with ranking charts. The World Happiness Report serves as a case study to create impactful bar and lollipop charts.

4. SEGMENTATION

Segmentation teaches you the art of identifying distinct groups within your data to tailor marketing efforts effectively. Utilizing RFM and Gaussian Mixture models, you'll practice segmenting with Python, analyzing consumer behavior, and studying market stratification to drive targeted business decisions.

Here's a more detailed breakdown of what you'll learn about Segmentation:

  • RFM

You'll learn to segment your customer base effectively using RFM analysis, and apply Python to sales data to uncover actionable marketing strategies.

  • Gaussian Mixture

You'll take your segmentation skills to the next level with Gaussian Mixture models by exploring market segmentation for strategic business decisions. And as usual, you'll solidify your new skills with case studies and Python exercises!

5. PREDICTIVE ANALYTICS

Predictive analytics unlocks the potential of forecasting and trend analysis. That means you’ll be able to predict the future...sort of. In reality you'll be able to better predict future trends, but that's almost the same thing right?

Here's a more detailed breakdown of what you'll learn about Predictive Analytics:

  • Random Forest

Understand ensemble learning through Random Forests and tackle prediction problems with a hands-on approach, learning from case studies in various industries.

  • Prophet

Forecast the future with Facebook's Prophet and dive into time series data from companies like Uber to anticipate market trends and plan effectively.

6. ADVANCED BUSINESS ANALYTICS

We're all about going from zero to real mastery here, so that means diving straight into the advanced end of the pool too. With advanced analytics you'll be taken deeper into your business analytics journey. You'll learn to conduct and infer complex tests, combining Python's analytical capabilities with case studies from Google and Coca-Cola, to optimize and innovate in product development and marketing strategies.

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.

Join Zero To Mastery Now

Course curriculum

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

Section 1 - Introduction

7 lectures

Business Analytics Bootcamp (with Python): Zero to Mastery2:34

PREVIEW

Introduction3:50

PREVIEW

Exercise: Meet Your Classmates and Instructor

PREVIEW

Get The Course Materials

BEGIN

The Value of a Business Analyst4:45

PREVIEW

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

PREVIEW

Set Your Learning Streak Goal

PREVIEW

PART A: A/B Testing and Experimentation

1 lectures

What Is A/B Testing And Why It Is Important?

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Section 2 - Hypothesis Testing for A/B Testing

18 lectures

Game Plan for Hypothesis Testing for A/B Testing3:32

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What is Hypothesis Testing?6:14

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CASE STUDY: FashionFiesta (Briefing)2:02

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Python: Hypothesis Testing Exercise10:35

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Confidence Level5:04

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P-value5:05

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Python: Build a P-value Function with ChatGPT4:51

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Two Sample T-Test7:16

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Python: Get to Know the Data with ChatGPT8:16

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Python: Levene's Test5:33

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Python: Two Sample T-Test8:10

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One-Tailed Test vs. Two-Tailed Test5:27

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Python: Get to Know the Data4:55

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Python: 1-Tailed Test3:43

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Chi-square Test3:13

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Python: Get to Know the Data5:10

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Python: Chi-square Test4:21

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CASE STUDY: Google 41 Shades of Blue3:10

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Section 3 - Introduction to A/B Testing

27 lectures

Game Plan for Introduction to A/B Testing3:31

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CASE STUDY: Krushing Kingdoms (Briefing)3:42

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Python: Libraries and Data8:05

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Python: EDA with ChatGPT12:21

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Python: Cleaning Outliers with ChatGPT10:43

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A/B Testing Terminology and Parameters6:04

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Setting Up Your A/B Test for Success7:59

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Randomization Techniques for A/B Testing12:21

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Python: Simple Randomization3:09

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Python: Block Randomization5:13

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Python: Stratified Randomization13:08

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Python: Clustered Randomization11:06

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Determining Sample Size Using Power Analysis5:57

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Python: Sample Size Calculator for Proportions11:04

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Determining A/B Test Sample Sizes for Continuous Outcomes5:34

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Python: Sample Size Calculator for Continuous Variables7:00

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Python: What if We Don't Clean the Outliers?3:38

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Danger of a too High Sample Size2:11

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Type I and Type II Error3:15

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Hypothesis Testing for Proportions4:12

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Python: Sampling Based on Optimal Sample Size5:24

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Python: Preparing Analysis4:33

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Python: Retention Test Post-Analysis.8:15

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Python: What if We Don't Sample?5:24

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Python: A/B Test Post-analysis5:17

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What Did You Learn in This Section?3:03

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CASE STUDY: How A/B Testing Helped Obama Raise Millions4:39

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Section 4 - Mastering A/B Testing

23 lectures

Game Plan for Intermediate A/B Testing3:57

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CASE STUDY: Amazon's Buy Button (Briefing)2:15

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Python: Kick off5:00

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Python: EDA with ChatGPT13:35

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Bayesian A/B Testing4:39

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Python: Bayesian AB Testing Setup5:53

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Bayesian Statistics8:18

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Python: Bayesian A/B Testing with TensorFlow14:06

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Python: Proportions Test with ChatGPT10:37

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Sequential Testing and Early Stopping4:37

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Python: Sequential Testing and Early Stopping14:30

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CASE STUDY: Netflix's Wednesday Thumbnails (Briefing)1:20

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A/B/C Test3:56

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Python: EDA with ChatGPT10:24

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Python: Chi-square Test with ChatGPT5:14

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Bonferroni Method2:25

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Python: Bonferroni Method11:11

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ANOVA Test and Hukey's HSD Test6:17

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Python: ANOVA Test4:58

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Python: Hukey's HSD Test6:01

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Limitations and Misinterpretations in A/B Tests5:13

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What Did You Learn in This Section?3:25

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CASE STUDY: The Ethical Considerations of Testing - AI in Recruitment2:52

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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 5 - Google Causal Impact (Econometrics and Causal Inference)

23 lectures

Why Econometrics and Causal Inference4:20

PREVIEW

Google Causal Impact - Game Plan1:25

PREVIEW

Time Series Data1:27

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CASE STUDY: Bitcoin and Paypal2:16

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Difference-in-Differences Framework2:58

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Causal Impact Step-by-Step Guide1:59

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Python - Installing Packages and Libraries3:22

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Python - Defining Dates3:58

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Python - Loading Bitcoin Data4:22

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Assumptions Needed3:33

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Python - Loading More Data3:49

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Python - Data Preparation4:38

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Python - Preparing for Correlation Matrix1:58

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Correlation Recap and Stationarity3:56

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Python - Stationarity Test7:24

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Python - Correlation Matrix and Heatmap8:21

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Python - Google Causal Impact Setup2:12

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Python - Google Causal Impact2:53

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Interpretation of Results4:10

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Python - Causal Impact Results4:49

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CHALLENGE: Introduction5:48

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CHALLENGE: Solutions19:32

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EXERCISE: Imposter Syndrome2:55

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Section 6 - Matching

25 lectures

Matching - Game Plan2:45

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What is Matching?3:18

PREVIEW

CASE STUDY: Catholic Schools & Standardized Tests (Briefing)1:40

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Python - Directory and Libraries3:16

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Python - Loading Data2:28

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Unconfoundedness2:50

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Python - Comparing Means3:04

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Python - T-Test4:16

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Python - T-Test Loop6:04

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Python - Chi-square Test3:18

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Python - Chi-square Loop3:53

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The Curse of Dimensionality1:52

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Python - Transforming Race Variable8:22

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Python - Transforming Education Variable5:02

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Python - Cleaning and Preparing Dataset2:56

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Common Support Region4:30

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Python - Logistic Regression for Common Support Region4:20

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Python - Visualizing Common Support Region7:19

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Python - Matching6:29

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Matching Robustness Check1:55

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Python - Repeated Experiment8:31

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Python - Removing 1 Confounder2:34

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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: DATA VISUALIZATION

1 lectures

What Is Data Visualization And Why It Is Important?

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Section 7 - Distribution Charts and Plotting Basics

24 lectures

Game Plan for Distribution Charts2:44

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Histogram2:57

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CASE STUDY: Pokemon Master (Briefing)1:46

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Python: Libraries and Data4:10

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Python: Defining Chart Size2:41

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Python: Basic Histogram3:17

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Python: Customizing Histogram4:16

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Python: Adding Vertical Lines3:31

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Python: Adding Horizontal Lines3:33

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Box Plot4:14

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Python: Basic Box Plot4:44

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Python: Customizing Box Plot6:15

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Python: Defining Order of the Plot5:01

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Python: Swarmplot6:29

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Violin Plot3:23

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Python: Basic Violin Plot5:34

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Python: Customize Violin Plot10:24

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Ridgeline3:06

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Python: Prepare Data for Ridgeline8:05

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Python: Prepare Chart for Ridgeline12:04

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Python: Customize Layout5:07

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Python: HTML Export and Building a Cheat Sheet5:15

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Colours7:47

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What Did You Learn In This Section?3:18

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Section 8 - Ranking Charts

17 lectures

Game Plan for Ranking Charts2:58

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Bar Charts3:41

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CASE STUDY: World Happiness (Briefing)2:24

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Python: Libraries and Data3:40

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Python: Horizontal Bar Chart5:16

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Python: Customizing Bar Chart5:28

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Python: Vertical Bar Chart3:59

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Python: Highlight a Bar2:19

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Lollipop4:45

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Python: Lollipop Chart4:04

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Python: Customizing a Lollipop Chart3:11

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Spider Chart3:43

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Python: Spider Chart Preparation9:15

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Python: Basic Spider Chart5:56

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Python: Customizing Spider Chart10:51

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"The Visual Display of Quantitative Information" by Edward R. Tufte3:10

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What Did You Learn In This Section?2:38

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PART D: SEGMENTATION

1 lectures

What is Segmentation and why is it important?

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

18 lectures

Game Plan for RFM0:45

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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

Game Plan for Gaussian Mixture1:10

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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 E: PREDICTIVE ANALYTICS

1 lectures

What are Predictive Analytics and why are they important?

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Section 11 - Random Forest

21 lectures

Game Plan for Random Forest1:05

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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

34 lectures

(Facebook) Prophet - Game Plan1:41

PREVIEW

Structural Time Series2:37

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(Facebook) Prophet3:39

PREVIEW

CASE STUDY: Wikipedia (Briefing)0:59

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Python: Directory and Libraries2:47

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Python: Loading and Inspecting the Data4:50

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Python: Formatting the Date Variable3:15

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Python: Renaming Variables1:32

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Dynamic Holidays2:26

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Python: Easter Holiday4:35

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Python: Black Friday Holidays4:53

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Python: Finishing Holiday Preparation1:14

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Training and Test Set in Time Series1:55

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Python: Training and Test Set2:03

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(Facebook) Prophet Model2:24

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Additive vs. Multiplicative Seasonality2:19

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Python: (Facebook) Prophet Model5:52

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Python: Regressor Coefficients3:06

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Python: Forecasting6:44

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Python: Event Assessment6:48

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Python: Accuracy Assessment4:36

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Python: Visualization5:51

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Cross-validation1:15

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Python: Cross-validation6:02

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Python: Cross-validation Results and Visualization5:49

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Parameters to Tune1:54

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Python: Parameter Grid4:48

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Python: Parameter Tuning7:00

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Python: Parameter Tuning Results3:19

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CHALLENGE: Introduction - Demand in NYC2:02

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CHALLENGE: Solutions (Part 1)10:44

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CHALLENGE: Solutions (Part 2)15:27

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CHALLENGE: Solutions (Part 3)16:03

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Forecasting at Uber4:38

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Part F - Advanced Analytics

1 lectures

What is Advanced Analytics and why it is so important?

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Section 13 - Multivariate A/B Testing

23 lectures

Game Plan for Multivariate A/B Testing1:47

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CASE STUDY: Google's Homepage Experiment (Briefing)0:59

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Python: Libraries and Data3:37

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Python: EDA with ChatGPT6:42

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Multivariate A/B Testing (MVT)4:00

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Python: Full Factorial Setup8:16

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Python: Full Factorial Testing9:29

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Partial Factorial Deep Dive3:57

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Python: Partial Factoral Combinations4:57

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Python: ANOVA and Tukey's HSD Test3:48

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Python: Encoding Variables and Generate Combinations8:53

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Python: Regression Analysis Setup2:27

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Python: Regression Analysis9:54

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Python: Random Forest Model3:29

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Python: Random Forest Evaluation3:50

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Random Forest Parameter Tuning2:25

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Python: Parameter Tuning8:55

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Python: Parameter Tuning Best Model4:09

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Python: Inferring Untested Variants - Predicting10:40

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Python: Inferring Untested Variants - Comparing13:43

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Python: Inferring Untested Variants - Visualizing7:06

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What Did You Learn in This Section?2:54

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CASE STUDY: Coca Cola "Share a Coke" Campaign5:06

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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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ARCHIVED - AVAILABLE UNTIL MAY 1 2024: PART A: STATISTICS

1 lectures

What are Statistics and why are they important?

PREVIEW

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
  • You'll need basic knowledge of Python and Statistics for this course. Don't know those? No problem, you'll get access to our Statistics Bootcamp course as well where you'll learn both 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
  • Students 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.

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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