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STARTS ON September 30, 2021
DURATION 6 Months Online Sessions & 4 Live Webinars with Faculty
STARTS ON September 30, 2021
DURATION 6 Months Online Sessions & 4 Live Webinars with Faculty

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.

Eligibility: Graduate or Diploma (10+2+3) in any discipline.

  • 91%

    CMOs believe marketing will undergo fundamental change over the next five years, with analytics being one of the key drivers

    Source: Accenture, 2020
  • 90%

    Online advertisers are expected to implement personalized marketing in some form or another. Such personalization can only be achieved with condensed data from the people themselves.

    Source: Nexoya, 2020
  • 81%

    Global market players admit that machine learning capabilities are essential when it comes to delivering personalized experiences to the customer.

    Source: CODE IT, 2021

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.

Programme Highlights

100+ Video

10 Discussion

15+ Quizzes and

Receive Executive
Alumni Status

The programme includes live webinars with MICA faculty.

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

MICA Executive Education Alumni Status

With over 4,500+ alumni across the globe, MICAns are flagbearers of excellence in the field of strategic marketing and communication


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Programme Modules

  • Introduction to Market Research
  • Overview of types of Market Research
  • Structure of a Research Report
  • Introduction to Business Analytics
  • Overview of Business Analytics Tools
  • Applications of Analytics in Business
  • Introduction to Descriptive Statistics
  • Measures of Central Tendency
  • Calculating Measures of Central Tendency using Excel
  • Measures of Dispersion
  • Calculating Measures of Dispersion using Excel
  • Measures of Shape
  • Calculating Measures of Shape using Excel
  • Data Visualisation and its application in Business
  • Ways of Data Visualisation
  • Data Visualisation Using Different Charts
  • Data Analysis Using Filtering
  • Data Analysis: Pareto Principle and its Application
  • Filtering and Pareto Analysis using Tableau
  • Ways of Summarising Data
  • Summarizing Data Using Excel and Tableau
  • Sample and Population
  • Statistical Sampling
  • A sampling plan
  • Sampling Methods
  • Estimating Population Parameters and sampling errors
  • Sampling Distributions
  • Normal Distribution
  • Business Use Cases
  • Statistical Inference: Hypothesis Testing
  • Z - Test
  • T - Test
  • One-Sample Hypothesis Tests
  • Selecting the Test Statistics
  • P - Values
  • Drawing a Conclusion Using Hypothesis Testing
  • Two-sample Hypothesis Tests
  • Two-Sample t-Test for Means: Independent Samples
  • Two-Sample t-Test for Means: Paired Samples
  • Covariance
  • Correlation
  • Regression
  • Analysis of Variance (One-Way ANOVA)
  • Chi-Square Test for independence
  • Debrief of the Project


  • Introduction to univariate, bivariate and multivariate data
  • MVA techniques: Dependence and interdependence methods
  • Introduction to regression: simple linear regression and multiple linear regression
  • Measures to evaluation prediction models: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R Squared (R2)
  • Apply linear regression to a dataset and evaluate its accuracy
  • Discriminant analysis - introduction
  • Discriminant analysis – working: linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA)
  • Modelling, Inference and Evaluation
  • Conjoint analysis - introduction and working
  • Modelling, Inference and Evaluation
  • Introduction to PCA
  • Modelling, Inference and Evaluation
  • Introduction to SEM (concept and terminologies)
  • Working of SEM (measurement model, structural model, metrics for reliability and validity, CB-SEM and PLS-SEM)
  • Modelling, Inference and Evaluation
  • Introduction to Machine Learning
  • Fundamentals of Classification and associated techniques
  • Evaluating Classification Models
  • Testing, Training, and Validation
  • Validation Methods
  • Applications in Marketing
  • Introduction to the model
  • Introduction to the data
  • Modelling, Inference and Evaluation
  • Introduction to Machine Learning Regression
  • Introduction to the model (decision tree, random forest, bagging and boosting)
  • Modelling, Inference and Evaluation
  • Introduction to Random Forest regression
  • Modelling, Inference and Evaluation
  • Ensemble learning (parallel and sequential ensemble methods)
  • Bagging and boosting
  • Modelling, Inference and Evaluation
  • Introduction to Unsupervised Learning and Clustering
  • Introduction to K-means
  • Modelling, Inference and Evaluation
  • Introduction to the Model
  • Modelling, Inference and Evaluation
  • Other Clustering methods


  • Fundamentals of Time Series Analysis and associated techniques (concept, components of time series, decomposition of time series, autocorrelation function (ACF))
  • Naive Model
  • Averaging model
  • Simple Moving Average
  • Exponential Smoothing Methods
  • Stationarity and differencing, ACF and PACF
  • AR and MA model
  • ARMA (Auto Regressive-Moving Average)
  • Introduction to ARIMA, steps in ARIMA modelling, forecasting with ARIMA (Auto Regressive Integrated Moving Average)
  • Final Project

- Modules/ topics are indicative only, and the suggested time and sequence may be dropped/ modified/ adapted to fit the participant profile & programme hours.

Programme Director

Prof. Dharun Kasilingam

Associate Professor,

Digital Platform & Strategies,

& Specialisation Lead – Marketing Analytics

Dr Dharun holds a Masters of Engineering (M.E.) degree in Industrial Engineering from PSG College of Technology, Coimbatore and a PhD in Marketing and Analytics from the National Institute of Technology (NIT) Tiruchirappalli... More info

Programme Faculty

Professor Niyati Bhanja

Associate Professor,

Area Leader

Business Management

Prof. Niyati specializes in Economics with a specific interest in Macroeconomics and Econometric Modelling. Her academic credentials include an MPhil in Economics (Class of 2010) & Ph.D. (Class of 2014) from Pondicherry Central University... More info

Programme Certificate

Participants will be awarded a Certificate from MICA on successful completion of the programme with a 70% of score in evaluation. Participants with less than 70% of score in overall evaluation will not be awarded any certificate.

Sample Certificate

Note: All certificate images are for illustrative purposes only and may be subject to change at the discretion of MICA.

Early applications encouraged. Limited seats are available.

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