Data Science For Marketing Analytics

Achieve your marketing goals with the data analytics power of Python

What you'll learn
Analyze and visualize data in Python using pandas and Matplotlib
Study clustering techniques, such as hierarchical and k-means clustering
Create customer segments based on manipulated data
Predict customer lifetime value using linear regression
Use classification algorithms to understand customer choice
Optimize classification algorithms to extract maximum information
Requirements
It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.
Description
Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments.The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.About the AuthorTommy Blanchard earned his Ph.D. from the University of Rochester and did his postdoctoral training at Harvard. Now, he leads the data science team at Fresenius Medical Care North America. His team performs advanced analytics and creates predictive models to solve a wide variety of problems across the company.Debasish Behera works as a Data Scientist for a large Japanese corporate bank, where he applies machine learning/AI for solving complex problems. He has worked on multiple use cases involving AML, predictive analytics, customer segmentation, chat bots, and natural language processing. He currently lives in Singapore and holds a Master’s in Business Analytics (MITB) from Singapore Management University.Pranshu Bhatnagar works as a Data Scientist in the telematics, insurance and mobile software space. He has previously worked as a Quantitative Analyst in the FinTech industry and often writes about algorithms, time series analysis in Python, and similar topics. He graduated with honours from the Chennai Mathematical Institute with a degree in Mathematics and Computer Science and has done certification courses in Machine Learning and Artificial Intelligence from the International Institute of Information Technology, Hyderabad. He is based out of Bangalore, India.Candas Bilgin is an experienced Data Science Specialist with a demonstrated history of working in the hospital & health care industry. Skilled in Python, R, Machine Learning, Predictive Analytics, and Data Science. Strong engineering professional with a Master of Science (M.Sc.) focused in Electrical, Electronics and Communications Engineering from Yildiz Technical University. He is a Microsoft Certified Data Scientist and also a Certified Tableau Developer.

Overview

Section 1: Data Preparation and Cleaning

Lecture 1 Course Overview

Lecture 2 Lesson Overview

Lecture 3 Data Models and Structured Data

Lecture 4 Pandas

Lecture 5 Data Manipulation

Lecture 6 Summary

Section 2: Data Exploration and Visualization

Lecture 7 Lesson Overview

Lecture 8 Identifying the Right Attributes

Lecture 9 Generating Targeted Insights

Lecture 10 Visualizing Data

Lecture 11 Summary

Section 3: Unsupervised Learning: Customer Segmentation

Lecture 12 Lesson Overview

Lecture 13 Customer Segmentation Methods

Lecture 14 Similarity and Data Standardization

Lecture 15 k-means Clustering

Lecture 16 Summary

Section 4: Choosing the Best Segmentation Approach

Lecture 17 Lesson Overview

Lecture 18 Choosing the Number of Clusters

Lecture 19 Different Methods of Clustering

Lecture 20 Evaluation Clustering

Lecture 21 Summary

Section 5: Predicting Customer Revenue Using Linear Regression

Lecture 22 Lesson Overview

Lecture 23 Feature Engineering for Regression

Lecture 24 Performing and Interpreting Linear Regression

Lecture 25 Summary

Section 6: Other Regression Techniques and Tools for Evaluation

Lecture 26 Lesson Overview

Lecture 27 Evaluating the Accuracy of a Regression Model

Lecture 28 Using Regularization for Feature Selection

Lecture 29 Tree Based Regression Models

Lecture 30 Summary

Section 7: Supervised Learning - Predicting Customer Churn

Lecture 31 Lesson Overview

Lecture 32 Understanding Logistic Regression

Lecture 33 Creating a Data Science Pipeline

Lecture 34 Modeling the Data

Lecture 35 Summary

Section 8: Fine-Tuning Classification Algorithms

Lecture 36 Lesson Overview

Lecture 37 Support Vector Machines

Lecture 38 Decision Trees and Random Forests

Lecture 39 Pre-processing Data and Model Evaluation

Lecture 40 Performance Metrics

Lecture 41 Summary

Section 9: Modeling Customer Choice

Lecture 42 Lesson Overview

Lecture 43 Understanding Multiclass Classification

Lecture 44 Class Imbalanced Data

Lecture 45 Summary

Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts.