MICA - MICA Advanced Certificate Program in Marketing Analysis

Offered byEmeritus

MICA Advanced Certificate Program in Marketing Analysis
at
Emeritus
Overview

Grow with digital ad revenue worth almost INR 540 billion by the fiscal year 2024 in India as per Statista, 2020

Duration	6 months
Total fee	₹71,500
Mode of learning	Online
Credential	Certificate

MICA Advanced Certificate Program in Marketing Analysis
at
Emeritus
Highlights

Earn a Certificate and Executive Alumni Status from MICA, Ahmedabad
Enjoy Personalised Career Services with Eruditus India Career Services (Plus)
Learn through 15+ Quizzes & Assignments, 10 Discussion Boards and 4 Live Webinars
Placement support through a 90-minute workshops from career management industry experts

MICA Advanced Certificate Program in Marketing Analysis
at
Emeritus
Course details

Skills you will learn

Anova SEM Marketing Statistics Hypothesis Testing Data analysis Excel Data Modeling R Advertising Business Analytics Data visualization Forecasting

Who should do this course?

Professionals seeking to acquire marketing analytics skills and adopting quantitative market research practices
Professionals who are looking to create impactful marketing strategies by quantifying marketing efforts and analytics-driven insights

What are the course deliverables?

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

More about this course

Explore the latest methods and concepts in market research, to help you craft a productive and result-oriented marketing strategy to perfection
Learn to 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
Program Fee: INR 71,750 + GST

MICA Advanced Certificate Program in Marketing Analysis
at
Emeritus
Curriculum

MODULE 1 - Introduction to Market Research & Business Analytics

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

MODULE 2 - Descriptive Statistics: Statistical Measures

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

MODULE 3 - Descriptive statistics: Data Visualisation on Tableau

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

MODULE 4 - Inferential Statistics: Sampling and Estimation

Sample and Population

Statistical Sampling

A sampling plan

Sampling Methods

Estimating Population Parameters and sampling errors

Sampling Distributions

Normal Distribution

Business Use Cases

MODULE 5 - Univariate Statistics

Statistical Inference: Hypothesis Testing

Z - Test

T - Test

One-Sample Hypothesis Tests

Selecting the Test Statistics

P - Values

Drawing a Conclusion Using Hypothesis Testing

MODULE 6 - Bivariate Statistics: Two-sample t-Test

Two-sample Hypothesis Tests

Two-Sample t-Test for Means: Independent Samples

Two-Sample t-Test for Means: Paired Samples

MODULE 7 - Bivariate Statistics: Covariance, Correlation and Regression

Covariance

Correlation

Regression

Analysis of Variance (One-Way ANOVA)

MODULE 8 - Bivariate Statistics: Chi-Square Test for Independence

Chi-Square Test for independence

Debrief of the Project

Project

MODULE 9 - Fundamentals of MDA and associated techniques

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

MODULE 10 - Discriminant Analysis

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

MODULE 11 - Principal Component Analysis and MDS

Introduction to PCA

Modelling, Inference and Evaluation

MODULE 12 - Structural Equation Modelling (SEM)

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

MODULE 13 - Introduction to Classification

Introduction to Machine Learning

Fundamentals of Classification and associated techniques

Evaluating Classification Models

Testing, Training, and Validation

Validation Methods

Applications in Marketing

MODULE 14 - Logistic Regression and SVM

Introduction to the model

Introduction to the data

Modelling, Inference and Evaluation

MODULE 15 - Decision Trees and Random Forest

Introduction to Machine Learning Regression

Introduction to the model (decision tree, random forest, bagging and boosting)

Modelling, Inference and Evaluation

MODULE 16 - Random Forest Regression

Introduction to Random Forest regression

Modelling, Inference and Evaluation

MODULE 17 - Boosting and Bagging Regression

Ensemble learning (parallel and sequential ensemble methods)

Bagging and boosting

Modelling, Inference and Evaluation

MODULE 18 - Fundamentals of Clustering and associated techniques

Introduction to Unsupervised Learning and Clustering

Introduction to K-means

Modelling, Inference and Evaluation

MODULE 19 - Apriori Algorithm

Introduction to the Model

Modelling, Inference and Evaluation

Other Clustering methods

Final Project

MODULE 20 - Introduction to Time Series

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

MODULE 21 - ARMA and ARIMA

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)

MICA Advanced Certificate Program in Marketing Analysis
at
Emeritus
Faculty details

Prof. Dharun Kasilingam

He was the youngest researcher to receive a PhD at NIT Trichy in Management at the early age of 25. His research interests majorly are two folds: Unstructured Data Analytics and Technology and Consumer Behavior. He has also published nine research articles in top marketing and medical journals.

Professor Niyati Bhanja

Prof. Niyati specializes in Economics with a specific interest in Macroeconomics and Econometric Modelling. She has over 31 publications; out of which, 21 publications are classified under the Australian Business Deans Council (ABDC) journal quality list.