Data Science for Marketing Analytics

Focused View

Packt Publishing

4:33:31

20 View

1 - Data Preparation and Cleaning

1 - Code.zip

1 - Course Overview.mp4

11:29

1 - Test Your Knowledge.html

2 - Lesson Overview.mp4

00:48

3 - Data Models and Structured Data.mp4

02:42

4 - Pandas.mp4

15:29

5 - Data Manipulation.mp4

22:47

6 - Summary.mp4

01:05

2 - Data Exploration and Visualization

2 - Test Your Knowledge.html

7 - Lesson Overview.mp4

01:25

8 - Identifying the Right Attributes.mp4

12:58

9 - Generating Targeted Insights.mp4

13:12

10 - Visualizing Data.mp4

05:51

11 - Summary.mp4

00:27

3 - Unsupervised Learning Customer Segmentation

3 - Test Your Knowledge.html

12 - Lesson Overview.mp4

01:01

13 - Customer Segmentation Methods.mp4

01:50

14 - Similarity and Data Standardization.mp4

11:23

15 - kmeans Clustering.mp4

10:09

16 - Summary.mp4

00:49

4 - Choosing the Best Segmentation Approach

4 - Test Your Knowledge.html

17 - Lesson Overview.mp4

01:11

18 - Choosing the Number of Clusters.mp4

07:58

19 - Different Methods of Clustering.mp4

06:55

20 - Evaluation Clustering.mp4

07:08

21 - Summary.mp4

00:45

5 - Predicting Customer Revenue Using Linear Regression

5 - Test Your Knowledge.html

22 - Lesson Overview.mp4

01:26

23 - Feature Engineering for Regression.mp4

12:26

24 - Performing and Interpreting Linear Regression.mp4

09:42

25 - Summary.mp4

00:31

6 - Other Regression Techniques and Tools for Evaluation

6 - Test Your Knowledge.html

26 - Lesson Overview.mp4

00:56

27 - Evaluating the Accuracy of a Regression Model.mp4

06:13

28 - Using Regularization for Feature Selection.mp4

03:34

29 - Tree Based Regression Models.mp4

06:40

30 - Summary.mp4

00:37

7 - Supervised Learning Predicting Customer Churn

7 - Test Your Knowledge.html

31 - Lesson Overview.mp4

01:13

32 - Understanding Logistic Regression.mp4

08:13

33 - Creating a Data Science Pipeline.mp4

16:11

34 - Modeling the Data.mp4

09:22

35 - Summary.mp4

01:01

8 - FineTuning Classification Algorithms

8 - Test Your Knowledge.html

36 - Lesson Overview.mp4

01:00

37 - Support Vector Machines.mp4

05:35

38 - Decision Trees and Random Forests.mp4

10:04

39 - Preprocessing Data and Model Evaluation.mp4

10:52

40 - Performance Metrics.mp4

05:24

41 - Summary.mp4

00:51

9 - Modeling Customer Choice

9 - Test Your Knowledge.html

42 - Lesson Overview.mp4

00:49

43 - Understanding Multiclass Classification.mp4

09:03

44 - Class Imbalanced Data.mp4

12:59

45 - Summary.mp4

01:27

Description

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

Who is this 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.

What You Need to Know?

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.

More details

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 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 Masterâ€™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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