Business Data Analytics & Intelligence With Python

Become a Business Data Analyst. You’ll learn to use Python and the latest industry tools to make data-driven decisions.

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
Requirements
Basic Python knowledge is recommended.
A willingness and enthusiasm to learn and take action.
Description
What is business data analytics? Why learn business analytics? What does a business data analyst do?Good questions, we're 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 (a.k.a. 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 money.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.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 randomized 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.

Overview

Section 1: Introduction

Lecture 1 Python for Business Analytics & Intelligence

Lecture 2 Introduction

Lecture 3 Setting up the Course Material

Lecture 4 The Modern Day Business Analyst

Lecture 5 Join Our Online Classroom!

Section 2: PART A: STATISTICS

Lecture 6 What are Statistics and why are they important?

Section 3: Basic Statistics

Lecture 7 Basic Statistics - Game Plan

Lecture 8 Arithmetic Mean

Lecture 9 CASE STUDY: Moneyball (Briefing)

Lecture 10 Python - Directory, Libraries and Data

Lecture 11 Python - Mean

Lecture 12 EXERCISE: Python - Mean

Lecture 13 Median and Mode

Lecture 14 Python - Median

Lecture 15 EXERCISE: Python - Median

Lecture 16 Python - Mode

Lecture 17 EXERCISE: Python - Mode

Lecture 18 Correlation

Lecture 19 Python - Correlation

Lecture 20 EXERCISE: Python - Correlation

Lecture 21 Standard Deviation

Lecture 22 Python - Standard Deviation

Lecture 23 EXERCISE: Python - Standard Deviation

Lecture 24 CASE STUDY: Moneyball

Section 4: Intermediary Statistics

Lecture 25 Intermediary Statistics - Game Plan

Lecture 26 Normal Distribution

Lecture 27 CASE STUDY: Wine Quality (Briefing)

Lecture 28 Python - Preparing Script and Loading Data

Lecture 29 Python - Normal Distribution Visualization - Remake

Lecture 30 EXERCISE: Python - Normal Distribution

Lecture 31 P-value

Lecture 32 Shapiro-Wilks Test

Lecture 33 Python - Shapiro-Wilks Test

Lecture 34 EXERCISE: Python - Shapiro-Wilks

Lecture 35 Standard Error of the Mean

Lecture 36 Python - Standard Error

Lecture 37 EXERCISE: Python - Standard Error

Lecture 38 Z-Score

Lecture 39 Confidence interval

Lecture 40 Python - Confidence Interval

Lecture 41 EXERCISE: Python - Confidence Interval

Lecture 42 T-test

Lecture 43 CASE STUDY: Remote Work Predictions (Briefing)

Lecture 44 Python - T-test

Lecture 45 EXERCISE: Python - T-test

Lecture 46 Chi-square test

Lecture 47 Python - Chi-square test

Lecture 48 EXERCISE: Python - Chi-square

Lecture 49 Powerposing and p-hacking

Section 5: Linear Regression

Lecture 50 Linear Regression - Game Plan

Lecture 51 CASE STUDY: Diamonds (Briefing)

Lecture 52 Linear Regression

Lecture 53 Python - Preparing Script and Loading Data

Lecture 54 Python - Isolate X and Y

Lecture 55 Python - Adding Constant

Lecture 56 Linear Regression Output

Lecture 57 Python - Linear Regression model and summary

Lecture 58 Python - Plotting Regression

Lecture 59 Dummy Variable Trap

Lecture 60 Python - Dummy Variable

Lecture 61 EXERCISE: Python - Linear Regression

Section 6: Multilinear Regression

Lecture 62 Multilinear Regression - Game Plan

Lecture 63 The Concept of Multilinear Regression

Lecture 64 CASE STUDY: Professors' Salary (Briefing)

Lecture 65 Python - Preparing Script and Loading Data

Lecture 66 Python - Summary Statistics

Lecture 67 Outliers

Lecture 68 Python - Plotting Continuous Variables

Lecture 69 Python - Correlation Matrix

Lecture 70 Python - Categorical Variables

Lecture 71 Python - For Loop

Lecture 72 Python - Creating Dummy Variables

Lecture 73 Python - Isolate X and Y

Lecture 74 Python - Adding Constant

Lecture 75 Under and Over Fitting

Lecture 76 Training and Test Set

Lecture 77 Python - Train and Test Split

Lecture 78 Python - Multilinear Regression

Lecture 79 Accuracy KPIs (Key Performance Indicators)

Lecture 80 Python - Model Predictions

Lecture 81 Python - Accuracy Assessment

Lecture 82 CHALLENGE: Introduction

Lecture 83 CHALLENGE: Solutions

Section 7: Logistic Regression

Lecture 84 Logistic Regression - Game Plan

Lecture 85 CASE STUDY: Spam Emails (Briefing)

Lecture 86 Logistic Regression

Lecture 87 Python - Preparing Script and Loading Data

Lecture 88 Python - Summary Statistics

Lecture 89 Python - Histogram and Outlier Removal

Lecture 90 Python - Correlation Matrix

Lecture 91 Python - Transforming Dependent Variable

Lecture 92 Python - Prepare X and Y

Lecture 93 Python - Training and Test Set

Lecture 94 How to Read Logistic Regression Coefficients

Lecture 95 Python - Logistic Regression

Lecture 96 Python - Function to Read Coefficients

Lecture 97 Python - Predictions

Lecture 98 Confusion Matrix

Lecture 99 Python - Confusion Matrix

Lecture 100 Python - Manual Accuracy Assessment

Lecture 101 Python - Classification Report

Lecture 102 CHALLENGE: Introduction

Lecture 103 CHALLENGE: Solutions

Section 8: PART B: ECONOMETRICS & CAUSAL INFERENCE

Lecture 104 What are Econometrics & Causal Inference, and why are they important?

Section 9: Google Causal Impact (Econometrics and Causal Inference)

Lecture 105 Why Econometrics and Causal Inference

Lecture 106 Google Causal Impact - Game Plan

Lecture 107 Time Series Data

Lecture 108 CASE STUDY: Bitcoin Pricing (Briefing)

Lecture 109 Difference-in-Differences Framework

Lecture 110 Causal Impact Step-by-Step

Lecture 111 Python - Installing and Importing Libraries

Lecture 112 Python - Defining Dates

Lecture 113 Python - Bitcoin Price loading

Lecture 114 Assumptions

Lecture 115 Python - Load Control Groups

Lecture 116 Python - Preparing DataFrame

Lecture 117 Python - Preparing for Correlation Matrix

Lecture 118 Correlation Recap and Stationarity

Lecture 119 Python - Stationarity

Lecture 120 Python - Correlation

Lecture 121 Python - Google Causal Impact Setup

Lecture 122 Python - Google Causal Impact

Lecture 123 Interpretation of Results

Lecture 124 Python - Impact Results

Lecture 125 CHALLENGE: Introduction

Lecture 126 CHALLENGE: Solutions

Lecture 127 EXERCISE: Imposter Syndrome

Section 10: Matching

Lecture 128 Matching - Game Plan

Lecture 129 Matching

Lecture 130 CASE STUDY: Catholic Schools & Standardized Tests (Briefing)

Lecture 131 Python - Directory and Libraries

Lecture 132 Python - Loading Data

Lecture 133 Unconfoundedness

Lecture 134 Python - Comparing Means

Lecture 135 Python - T-Test

Lecture 136 Python - T-Test Loop

Lecture 137 Python - Chi-square Test

Lecture 138 Python - Chi-square Loop

Lecture 139 Python - Other Variables

Lecture 140 The Curse of Dimensionality

Lecture 141 Python - Race Variable Transformation

Lecture 142 Python - Education Variables

Lecture 143 Python - Cleaning and Preparing Dataset

Lecture 144 Common Support Region

Lecture 145 Python - Logistic Regression and Debugging

Lecture 146 Python - Preparing for Common Support Region

Lecture 147 Python - Common Support Region Visualization

Lecture 148 Python - Matching

Lecture 149 Robustness Checks

Lecture 150 Python - Robustness Check - Repeated experiments

Lecture 151 Python - Outcome Visualization

Lecture 152 Python - Robustness Check - Removing 1 confounder

Lecture 153 CHALLENGE: Introduction

Lecture 154 CHALLENGE: Solutions

Lecture 155 My Experience with Matching

Section 11: PART C: SEGMENTATION

Lecture 156 What is Segmentation and why is it important?

Section 12: RFM (Recency, Frequency, Monetary) Analysis

Lecture 157 RFM - Game Plan

Lecture 158 Value Based Segmentation

Lecture 159 RFM Model

Lecture 160 CASE STUDY: Online Shopping (Briefing)

Lecture 161 Python - Directory and Libraries

Lecture 162 Python - Loading Data

Lecture 163 Python - Creating Sales Variable

Lecture 164 Python - Date Variable

Lecture 165 Python - Customer Level Aggregation

Lecture 166 Python - Monetary Variable

Lecture 167 Python - Tidying up Dataframe

Lecture 168 Python - Quartiles

Lecture 169 Python - RFM Score

Lecture 170 Python - RFM Function

Lecture 171 Python - Applying RFM Function

Lecture 172 Python - Results Summary

Lecture 173 CHALLENGE: Introduction

Lecture 174 CHALLENGE: Solutions

Section 13: Gaussian Mixture

Lecture 175 Gaussian Mixture - Game Plan

Lecture 176 Clustering

Lecture 177 Gaussian Mixture Model

Lecture 178 CASE STUDY: Credit Cards #1 (Briefing)

Lecture 179 Python - Directory and Data

Lecture 180 Python - Load Data

Lecture 181 Python - Transform Character variables

Lecture 182 AIC and BIC

Lecture 183 Python - Optimal Number of Clusters

Lecture 184 Python - Gaussian Mixture Model

Lecture 185 Python - Cluster Prediction and Assignment

Lecture 186 Python - Interpretation

Lecture 187 CHALLENGE: Introduction

Lecture 188 CHALLENGE: Solutions

Lecture 189 My Experience with Segmentation

Section 14: PART D: PREDICTIVE ANALYTICS

Lecture 190 What are Predictive Analytics and why are they important?

Section 15: Random Forest

Lecture 191 Random Forest - Game Plan

Lecture 192 Ensemble Learning and Random Forest

Lecture 193 How Decision Trees Work

Lecture 194 CASE STUDY: Credit Cards #2 (Briefing)

Lecture 195 Python - Directory and Libraries

Lecture 196 Python - Loading Data

Lecture 197 Python - Transform Object into Numerical Variables

Lecture 198 Python - Summary Statistics

Lecture 199 Random Forest Quirks

Lecture 200 Python - Isolate X and Y

Lecture 201 Python - Training and Test Set

Lecture 202 Python - Random Forest Model

Lecture 203 Python - Predictions

Lecture 204 Python - Classification Report and F1 score

Lecture 205 Python - Feature Importance

Lecture 206 Parameter Tuning

Lecture 207 Python - Parameter Grid

Lecture 208 Python - Parameter Tuning

Lecture 209 CHALLENGE: Introduction

Lecture 210 CHALLENGE: Solutions (Part 1)

Lecture 211 CHALLENGE: Solutions (Part 2)

Section 16: Facebook Prophet

Lecture 212 Facebook Prophet - Game Plan

Lecture 213 Structural Time Series

Lecture 214 Facebook Prophet

Lecture 215 CASE STUDY: Wikipedia (Briefing)

Lecture 216 Python - Directory and Libraries

Lecture 217 Python - Loading Data

Lecture 218 Python - Transforming Date Variable

Lecture 219 Python - Renaming Variables

Lecture 220 Dynamic Holidays

Lecture 221 Python - Easter Holidays

Lecture 222 Python - Black Friday

Lecture 223 Python - Combining Events and Preparing Dataframe

Lecture 224 Training and Test Set

Lecture 225 Python - Training and Test Set

Lecture 226 Facebook Prophet Parameters

Lecture 227 Additive vs. Multiplicative Seasonality

Lecture 228 Facebook Prophet Model

Lecture 229 Python - Regressor Coefficients

Lecture 230 Python - Future Dataframe

Lecture 231 Python - Forecasting

Lecture 232 Python - Accuracy Assessment

Lecture 233 Python - Visualization

Lecture 234 Cross-validation

Lecture 235 Python - Cross-validation

Lecture 236 Parameters to tune

Lecture 237 Python - Parameter Grid

Lecture 238 Python - Parameter Tuning

Lecture 239 CHALLENGE: Introduction

Lecture 240 CHALLENGE: Solutions (Part 1)

Lecture 241 CHALLENGE: Solutions (Part 2)

Lecture 242 CHALLENGE: Solutions (Part 3)

Lecture 243 Forecasting at Uber

Section 17: Where To Go From Here?

Lecture 244 Thank You!

Lecture 245 Become An Alumni

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