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MICA | Advanced Certificate Program in Marketing Analytics

Acquire hands-on analytical techniques to generate valuable data, filter it, and identify patterns to meet market research goals.

Course Snapshot
  • FeeINR 71,750 + GST
  • Work Experience2 - 30 Years
  • Duration6 Months
  • Delivery MethodOnline
Course Detail

Programme Overview
MICA's Advanced Certificate Programme in Marketing Analytics explores the latest methods and concepts in market research, to help you craft a productive and result-oriented marketing strategy to perfection. Filter the correct data, apply multiple research techniques for a data-informed marketing strategy and use Machine Learning applications to predict and prepare for future outcomes.

Programme Highlights
● 4 Live Webinars by MICA Faculty
● 100+ Video Lectures
● 10 Discussion Boards
● 15 + Quizzes and Assignments
● Career Services (Plus)

Learning Outcomes
● Acquire hands-on analytical techniques to generate valuable data, filter it, and identify patterns to meet market research goals
● Apply machine learning principles to generate insights and chalk-out reliable forecasts for business success
● Learn how to align research outcomes to develop impactful marketing strategies
● Gain exposure to recent market research trends and learn real-world business applications and strategies
● Gain hands-on experience in end-to-end market research management
● Deploy multiple research techniques to develop a market research strategy

Who is this Programme for?
● Professionals seeking to acquire marketing analytics skills and adopting quantitative market research practices to implement result-oriented marketing strategies
● Professionals who are looking to create impactful marketing strategies by quantifying marketing efforts and analytics-driven insights

Course Modules

● Module 1: Introduction to Market Research & Business Analytics
● Module 2: Descriptive Statistics: Statistical Measures
● Module 3: Descriptive statistics: Data Visualisation on Tableau
● Module 4: Inferential Statistics: Sampling and Estimation
● Module 5: Univariate Statistics
● Module 6: Bivariate Statistics: Two-sample t-Test
● Module 7: Bivariate Statistics: Covariance, Correlation and Regression
● Module 8: Bivariate Statistics: Chi-Square Test for Independence
● Module 9: Fundamentals of MDA and associated techniques
● Module 10: Discriminant Analysis
● Module 11: Inferential Statistics: Sampling and Estimation
● Module 12: Structural Equation Modelling (SEM)
● Module 13: Introduction to Classification
● Module 14: Logistic Regression and SVM
● Module 15: Decision Trees and Random Forest
● Module 16: Random Forest Regression
● Module 17: Boosting and Bagging Regression
● Module 18: Fundamentals of Clustering and associated techniques
● Module 19: Apriori Algorithm
● Module 20: Introduction to Time Series
● Module 21: ARMA and ARIMA