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Udemy - Customer Analytics In Python

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Udemy - Customer Analytics In Python
Last updated 11/2020
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.51 GB | Duration: 5h 11m
Beginner and Advanced Customer Analytics in Python: PCA, K-means Clustering, Elasticity Modeling & Deep Neural Networks

What you'll learn
Master beginner and advanced customer analytics
Learn the most important type of analysis applied by mid and large companies
Gain access to a professional team of trainers with exceptional quant skills
Wow interviewers by acquiring a highly desired skill
Understand the fundamental marketing modeling theory: segmentation, targeting, positioning, marketing mix, and price elasticity;
Apply segmentation on your customers, starting from raw data and reaching final customer segments;
Perform K-means clustering with a customer analytics focus;
Apply Principal Components Analysis (PCA) on your data to preprocess your features;
Combine PCA and K-means for even more professional customer segmentation;
Deploy your models on a different dataset;
Learn how to model purchase incidence through probability of purchase elasticity;
Model brand choice by exploring own-price and cross-price elasticity;
Complete the purchasing cycle by predicting purchase quantity elasticity
Carry out a black box deep learning model with TensorFlow 2.0 to predict purchasing behavior with unparalleled accuracy
Be able to optimize your neural networks to enhance results
You'll need to install Anaconda. We will show you how to do it in one of the first lectures of the course
Basic Python programming
A willingness and enthusiasm to learn and practice
Data science and Marketing are two of the key driving forces that help companies create value and stay on top in today's fast-paced economy.Welcome to.Customer Analytics in Python – the place where marketing and data science meet!This course is the best way to distinguish yourself with a very rare and extremely valuable skillset.What will you learn in this course?This course is packed with knowledge, covering some of the most exciting methods used by companies, all implemented in Python.Since Customer Analytics is a broad topic, we have created 5 different parts to explore various sides of the analytical process. Each of them will have their strong sides and shortcomings. We will explore both sides of the coin for each part, while making sure to provide you with nothing but the most important and relevant information!Here are the 5 major parts:1. We will introduce you to the relevant theory that you need to start performing customer analyticsWe have kept this part as short as possible in order to provide you with more practical experience. Nonetheless, this is the place where marketing beginners will learn about the marketing fundamentals and the reasons why we take advantage of certain models throughout the course.2. Then we will perform cluster analysis and dimensionality reduction to help you segment your customersBecause this course is based in Python, we will be working with several popular packages - NumPy, SciPy, and scikit-learn. In terms of clustering, we will show both hierarchical and flat clustering techniques, ultimately focusing on the K-means algorithm. Along the way, we will visualize the data appropriately to build your understanding of the methods even further. When it comes to dimensionality reduction, we will employ Principal Components Analysis (PCA) once more through the scikit-learn (sklearn) package. Finally, we'll combine the two models to reach an even better insight about our customers. And, of course, we won't forget about model deployment which we'll implement through the pickle package.3. The third step consists in applying Descriptive statistics as the exploratory part of your analysisOnce segmented, customers' behavior will require some interpretation. And there is nothing more intuitive than obtaining the descriptive statistics by brand and by segment and visualizing the findings. It is that part of the course, where you will have the 'Aha!' effect. Through the descriptive analysis, we will form our hypotheses about our segments, thus ultimately setting the ground for the subsequent modeling.4. After that, we will be ready to engage with elasticity modeling for purchase probability, brand choice, and purchase quantityIn most textbooks, you will find elasticities calculated as static metrics depending on price and quantity. But the concept of elasticity is in fact much broader. We will explore it in detail by calculating purchase probability elasticity, brand choice own price elasticity, brand choice cross-price elasticity, and purchase quantity elasticity. We will employ linear regressions and logistic regressions, once again implemented through the sklearn library. We implement state-of-the-art research on the topic to make sure that you have an edge over your peers. While we focus on about 20 different models, you will have the chance to practice with more than 100 different variations of them, all providing you with additional insights!5. Finally, we'll leverage the power of Deep Learning to predict future behaviorMachine learning and artificial intelligence are at the forefront of the data science revolution. That's why we could not help but include it in this course. We will take advantage of the TensorFlow 2.0 framework to create a feedforward neural network (also known as artificial neural network). This is the part where we will build a black-box model, essentially helping us reach 90%+ accuracy in our predictions about the future behavior of our customers.An Extraordinary Teaching CollectiveWe at 365 Careers have 550,000+ students here on Udemy and believe that the best education requires two key ingredients: a remarkable teaching collective and a practical approach. That's why we ticked both boxes.Customer Analytics in Python was created by 3 instructors working closely together to provide the most beneficial learning experience.The course author, Nikolay Georgiev is a Ph.D. who largely focused on marketing analytics during his academic career. Later he gained significant practical experience while working as a consultant on numerous world-class projects. Therefore, he is the perfect expert to help you build the bridge between theoretical knowledge and practical application.Elitsa and Iliya also played a key part in developing the course. All three instructors collaborated to provide the most valuable methods and approaches that customer analytics can offer.In addition, this course is as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts, and course notes, as well as notebook files with comments, are just some of the perks you will get by enrolling.Why do you need these skills?1. Salary/Income – careers in the field of data science are some of the most popular in the corporate world today. All B2C businesses are realizing the advantages of working with the customer data at their disposal, to understand and target their clients better2. Promotions – even if you are a proficient data scientist, the only way for you to grow professionally is to expand your knowledge. This course provides a very rare skill, applicable to many different industries.3. Secure Future – the demand for people who understand numbers and data, and can interpret it, is growing exponentially; you've probably heard of the number of jobs that will be automated soon, right? Well, the marketing department of companies is already being revolutionized by data science and riding that wave is your gateway to a secure future.Why wait? Every day is a missed opportunity.Click the "Buy Now" button and let's start our customer analytics journey together!
Section 1: Introduction
Lecture 1 What Does the Course Cover
Section 2: A Brief Marketing Introduction
Lecture 2 Segmentation, Targeting, and Positioning
Lecture 3 Marketing Mix
Lecture 4 Physical and Online Retailers: Similarities and Differences
Lecture 5 Price Elasticity
Section 3: Setting up the Environment
Lecture 6 Setting up the Environment - Do not Skip, Please!
Lecture 7 Why Python and Why Jupyter
Lecture 8 Installing Anaconda
Lecture 9 Jupyter Dashboard - Part 1
Lecture 10 Jupyter Dashboard - Part 2
Lecture 11 Installing the Relevant Packages
Lecture 12 Installing the Relevant Packages: Homework
Lecture 13 Installing the Relevant Packages: Homework Solution
Section 4: Segmentation Data
Lecture 14 Getting to know the Segmentation Dataset
Lecture 15 Importing and Exploring Segmentation Data
Lecture 16 Standardizing Segmentation Data
Section 5: Hierarchical Clustering
Lecture 17 Hierarchical Clustering: Background
Lecture 18 Hierarchical Clustering: Implementation and Results
Section 6: K-Means Clustering
Lecture 19 K-Means Clustering: Background
Lecture 20 K-Means Clustering: Implementation
Lecture 21 K-Means Clustering: Results
Section 7: K-Means Clustering based on Principal Component Analysis
Lecture 22 Principal Component Analysis: Background
Lecture 23 Principal Component Analysis: Application
Lecture 24 Principal Component Analysis: Homework
Lecture 25 Principal Component Analysis: Results
Lecture 26 K-Means Clustering with Principal Components: Application
Lecture 27 K-Means Clustering with Principal Components: Results
Lecture 28 K-Means Clustering with Principal Components: Results Homework
Lecture 29 Saving the Models
Section 8: Purchase Data
Lecture 30 Purchase Analytics - Introduction
Lecture 31 Getting to know the Purchase Dataset
Lecture 32 Importing and Exploring Purchase Data
Lecture 33 Applying the Segmentation Model
Section 9: Descriptive Analyses by Segments
Lecture 34 Segment Proportions
Lecture 35 Purchase Occasion and Purchase Incidence
Lecture 36 Purchase Occasion and Purchase Incidence Homework
Lecture 37 Brand Choice
Lecture 38 Dissecting the Revenue by Segment
Section 10: Modeling Purchase Incidence
Lecture 39 The Model: Binomial Logistic Regression
Lecture 40 Prepare the Dataset for Logistic Regression
Lecture 41 Model Estimation
Lecture 42 Calculating Price Elasticity of Purchase Probability
Lecture 43 Price Elasticity of Purchase Probability: Results
Lecture 44 Purchase Probability by Segments
Lecture 45 Purchase Probability by Segments - Homework
Lecture 46 Purchase Probability Model with Promotion
Lecture 47 Calculating Price Elasticities with Promotion
Lecture 48 Calculating Price Elasticities (Without Promotion) - Homework
Lecture 49 Comparing Price Elasticities with and without Promotion
Section 11: Modeling Brand Choice
Lecture 50 Brand Choice Models. The Model: Multinomial Logistic Regression
Lecture 51 Prepare Data and Fit the Model
Lecture 52 Interpreting the Coefficients
Lecture 53 Own Price Brand Choice Elasticity
Lecture 54 Cross Price Brand Choice Elasticity
Lecture 55 Own and Cross-Price Elasticity by Segment
Lecture 56 Own and Cross-Price Elasticity by Segment Homework
Lecture 57 Own and Cross-Price Elasticity by Segment - Comparison
Lecture 58 Own and Cross-Price Elasticity by Segment Homework 2
Section 12: Modeling Purchase Quantity
Lecture 59 Purchase Quantity Models. The Model: Linear Regression
Lecture 60 Preparing the Data and Fitting the Model
Lecture 61 Calculating Price Elasticity of Purchase Quantity
Lecture 62 Calculating Price Elasticity of Purchase Quantity: Homework
Lecture 63 Price Elasticity of Purchase Quantity: Results
Lecture 64 Price Elasticity of Purchase Quantity: Homework
Section 13: Deep Learning for Conversion Prediction
Lecture 65 Introduction to Deep Learning for Customer Analytics
Lecture 66 Exploring the Dataset
Lecture 67 How Are We Going to Tackle the Business Case
Lecture 68 Why do We Need to Balance a Dataset
Lecture 69 Preprocessing the Data for Deep Learning
Lecture 70 Outlining the Deep Learning Model
Lecture 71 Training the Deep Learning Model
Lecture 72 Testing the Model
Lecture 73 Obtaining the Probability of a Customer to Convert
Lecture 74 Saving the Model and Preparing for Deployment
Lecture 75 Predicting on New Data
Lecture 76 Completing 100%
People who want a career in Data Science,People who want a career in Business Intelligence,Individuals who are passionate about numbers and quant analysis,People working in Data Science looking to expand their knowledge into Marketing analytics,People working in Marketing, looking for career growth in the realms of Data Science


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