Data Science for Marketing Analytics

3 Days

18 CPD hours

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'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.

By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.

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 course, 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 sections, 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.

Data Preparation and Cleaning

• Data Models and Structured Data
• pandas
• Data Manipulation

Data Exploration and Visualization

• Identifying the Right Attributes
• Generating Targeted Insights
• Visualizing Data

Unsupervised Learning: Customer Segmentation

• Customer Segmentation Methods
• Similarity and Data Standardization
• k-means Clustering

Choosing the Best Segmentation Approach

• Choosing the Number of Clusters
• Different Methods of Clustering
• Evaluating Clustering

Predicting Customer Revenue Using Linear Regression

• Understanding Regression
• Feature Engineering for Regression
• Performing and Interpreting Linear Regression

Other Regression Techniques and Tools for Evaluation

• Evaluating the Accuracy of a Regression Model
• Using Regularization for Feature Selection
• Tree-Based Regression Models

Supervised Learning: Predicting Customer Churn

• Classification Problems
• Understanding Logistic Regression
• Creating a Data Science Pipeline

Fine-Tuning Classification Algorithms

• Support Vector Machine
• Decision Trees
• Random Forest
• Preprocessing Data for Machine Learning Models
• Model Evaluation
• Performance Metrics

Modeling Customer Choice

• Understanding Multiclass Classification
• Class Imbalanced Data

Additional course details:

Nexus Humans Data Science for Marketing Analytics training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.