1 - Data Preparation and Cleaning
Data Models and Structured DatapandasData Manipulation
2 - Data Exploration and Visualization
Identifying the Right AttributesGenerating Targeted InsightsVisualizing Data
3 - Unsupervised Learning: Customer Segmentation
Customer Segmentation MethodsSimilarity and Data Standardizationk-means Clustering
4 - Choosing the Best Segmentation Approach
Choosing the Number of ClustersDifferent Methods of ClusteringEvaluating Clustering
5 - Predicting Customer Revenue Using Linear Regression
Understanding RegressionFeature Engineering for RegressionPerforming and Interpreting Linear Regression
6 - Other Regression Techniques and Tools for Evaluation
Evaluating the Accuracy of a Regression ModelUsing Regularization for Feature SelectionTree-Based Regression Models
7 - Supervised Learning: Predicting Customer Churn
Classification ProblemsUnderstanding Logistic RegressionCreating a Data Science Pipeline
8 - Fine-Tuning Classification Algorithms
Support Vector MachineDecision TreesRandom ForestPreprocessing Data for Machine Learning ModelsModel EvaluationPerformance Metrics
9 - Modeling Customer Choice
Understanding Multiclass ClassificationClass Imbalanced Data
Actual course outline may vary depending on offering center. Contact your sales representative for more information.
Who is it 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. 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.