Data Science for Marketing Analytics Training

Data Science for Marketing Analytics Training Outline

• Lesson 1: Data Preparation and Cleaning

  • Data Models and Structured Data
  • Pandas
  • Data Manipulation

• Lesson 2: Data Exploration and Visualization

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

• Lesson 3: Unsupervised Learning: Customer Segmentation

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

• Lesson 4: Choosing the Best Segmentation Approach

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

• Lesson 5: Predicting Customer Revenue Using Linear Regression

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

• Lesson 6: Other Regression Techniques and Tools for Evaluation

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

• Lesson 7: Supervised Learning: Predicting Customer Churn

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

• Lesson 8: Fine-Tuning Classification Algorithms

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

• Lesson 9: Modeling Customer Choice

  • Understanding Multiclass Classification
  • Class Imbalanced Data