Data Science for Marketing Analytics
Data Science for Marketing Analytics, available at $44.99, has an average rating of 4.35, with 45 lectures, 9 quizzes, based on 143 reviews, and has 950 subscribers.
You will learn about 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 This course is ideal for individuals who are Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It is particularly useful for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts.
Enroll now: Data Science for Marketing Analytics
Summary
Title: Data Science for Marketing Analytics
Price: $44.99
Average Rating: 4.35
Number of Lectures: 45
Number of Quizzes: 9
Number of Published Lectures: 45
Number of Published Quizzes: 9
Number of Curriculum Items: 54
Number of Published Curriculum Objects: 54
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts.
Target Audiences
- Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts.
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 Author
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Tommy 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.
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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.
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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.
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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.
Course Curriculum
Chapter 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
Chapter 2: Data Exploration and Visualization
Lecture 1: Lesson Overview
Lecture 2: Identifying the Right Attributes
Lecture 3: Generating Targeted Insights
Lecture 4: Visualizing Data
Lecture 5: Summary
Chapter 3: Unsupervised Learning: Customer Segmentation
Lecture 1: Lesson Overview
Lecture 2: Customer Segmentation Methods
Lecture 3: Similarity and Data Standardization
Lecture 4: k-means Clustering
Lecture 5: Summary
Chapter 4: Choosing the Best Segmentation Approach
Lecture 1: Lesson Overview
Lecture 2: Choosing the Number of Clusters
Lecture 3: Different Methods of Clustering
Lecture 4: Evaluation Clustering
Lecture 5: Summary
Chapter 5: Predicting Customer Revenue Using Linear Regression
Lecture 1: Lesson Overview
Lecture 2: Feature Engineering for Regression
Lecture 3: Performing and Interpreting Linear Regression
Lecture 4: Summary
Chapter 6: Other Regression Techniques and Tools for Evaluation
Lecture 1: Lesson Overview
Lecture 2: Evaluating the Accuracy of a Regression Model
Lecture 3: Using Regularization for Feature Selection
Lecture 4: Tree Based Regression Models
Lecture 5: Summary
Chapter 7: Supervised Learning – Predicting Customer Churn
Lecture 1: Lesson Overview
Lecture 2: Understanding Logistic Regression
Lecture 3: Creating a Data Science Pipeline
Lecture 4: Modeling the Data
Lecture 5: Summary
Chapter 8: Fine-Tuning Classification Algorithms
Lecture 1: Lesson Overview
Lecture 2: Support Vector Machines
Lecture 3: Decision Trees and Random Forests
Lecture 4: Pre-processing Data and Model Evaluation
Lecture 5: Performance Metrics
Lecture 6: Summary
Chapter 9: Modeling Customer Choice
Lecture 1: Lesson Overview
Lecture 2: Understanding Multiclass Classification
Lecture 3: Class Imbalanced Data
Lecture 4: Summary
Instructors
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Packt Publishing
Tech Knowledge in Motion
Rating Distribution
- 1 stars: 4 votes
- 2 stars: 14 votes
- 3 stars: 20 votes
- 4 stars: 47 votes
- 5 stars: 58 votes
Frequently Asked Questions
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