Marketing Analytics Training with Python for Data-Driven Decisions

This marketing analytics training explores how Python can turn campaign, customer, and channel data into practical marketing insight. Learn to work with marketing datasets, analyse performance metrics, segment audiences, visualise results, and support data-driven marketing decisions. The course connects Python-based analysis with everyday marketing questions, from measuring campaign outcomes to identifying patterns that inform targeting and budget choices.
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Marketing Analytics With Python Training is backed by leading analytics platforms. Partners such as Google Cloud and Tableau provide sandbox environments for live experimentation.

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Curriculum

marketing analytics training, python marketing course, zeblearnindia pricing, data-driven marketing skills, online marketing analytics certification Course Syllabus Structure

Marketing Analytics With Python Training covers data cleaning, customer clustering, and churn prediction. Learners build a dashboard that tracks key performance indicators in real time.

  • Introduction
  • Machine Learning Express Code Folder

  • Setting up the Jupyter and Code Folder

  • Overview of Machine Learning
  • Fundamentals of Supervised and Unsupervised Learning
  • Steps to Build a Model
  • Model Fit and Bias-Variance Tradeoff
  • Model Performance Metrics

  • Set up the Machine Learning Express Code and Data Folder
  • Introduction
  • Import Libraries and Data
  • Check Types of Features
  • Independent and Target Split
  • Check Percentage of Missing Values
  • Imputation of Missing Values
  • Outlier Handling
  • Encoding Categorical Features
  • Feature Scaling
  • Feature Discretization
  • Joining of Data
  • Train Test Split
  • Concept of Outlier Handling
  • Concept of Imputation of Missing Values
  • Concept of Feature Scaling

  • Introduction
  • Feature Selection for Regression Problems
  • Feature Selection for Classification Problems

  • Introduction
  • Business Problem and Data Description
  • Hierarchical Clustering - Concept
  • Hierarchical Clustering - Python  
  • K Means Clustering - Concept
  • K Means Clustering

  • Introduction
  • Business Problem and Data Description
  • PCA

  • Introduction
  • Business Problem 1 and Data Description
  • Simple Linear Regression  
  • Business Problem 2 and Data Description
  • Multiple Linear Regression - Concept
  • Multiple Linear Regression  
  • Decision Tree Regression - Concept
  • Decision Tree Regression  
  • Random Forest Regression - Concept
  • Random Forest Regression  
  • Gradient Boosting Regression - Concept
  • Gradient Boosting Regression 

  • Introduction
  • Business Problem and Data Description
  • Logistic Regression - Concept
  • Decision Tree Classification - Concept
  • Random Forest Classification - Concept
  • Gradient Boosting Classification - Concept
  • Stacking Classifier - Concept
  • KNN Classifier - Concept
  • Naive Bayes Classifier - Concept
  • SVM Classification - Concept

  • Introduction
  • Hyper Parameter Tuning with Grid Search - Concept
  • Grid Search - Python Implementation with SVM

  • Introduction
  • What is Marketing Analytics
  • The 4 P's of Marketing
  • Customer Lifecycle
  • Data Sources and Use Cases for Marketing Analytics
  • Customer Segmentation
  • Marketing Analytics Strategy from Machine Learning
  • Marketing Analytics Use Cases for Retail Domain

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    SOLD OUT 30 Aug 2026 Weekend SAT - SUN (08 Weeks) 18:00 - 20:00
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    FAQ

    Frequently Asked Questions

    Q: What core competencies does marketing analytics training develop? A: Marketing analytics training develops statistical modeling, data visualization, and campaign attribution skills. Q: How does a Python marketing course enhance data-driven decision making? A: A Python marketing course equips learners with libraries like pandas and scikit-learn to process large datasets and predict customer behavior. Q: Is Zeblearn's online marketing analytics certification recognized by employers? A: Zeblearn's online marketing analytics certification is listed on major job platforms and cited in hiring guidelines for data‑focused roles.

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