Data Science for Marketing Analytics
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
- 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.
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
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.
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.
Who this course is 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.
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