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STARTS ON Sept 30, 2023 Live Online Sessions Application Deadline: Oct 5, 2023
DURATION 10 Months Live Online Sessions 3 Hours/ week Sunday 9 AM to 12 PM
PROGRAMME FEE INR 1,81,500 + GST View Payment Plan Loan offered at EMI of INR 7,522 Special Corporate Enrolment Pricing
ELIGIBILITY Graduates (10+2+3) or Diploma Holders (only 10+2+3) from a recognised university in any discipline with a minimum of 1 year of work experience (after graduation) as on Sept 30, 2023.
STARTS ON Sept 30, 2023 Live Online Sessions Application Deadline: Oct 5, 2023
DURATION 10 Months Live Online Sessions 3 Hours/ week Sunday 9 AM to 12 PM
PROGRAMME FEE INR 1,81,500 + GST View Payment Plan
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Why enrol in this Data Analytics programme
Advanced Data Analytics for Managers		 Other Data Analytics programmes (Online via recorded lectures)
All classes are delivered via LIVE ONLINE format by IIMK faculty & industry experts
Executive ALUMNI Status
PEER-TO-PEER learning through weekly interactions with other participants
Accredited institution Dual accreditation No accreditation
Best-in-class curriculum Created by one of India’s premier B-School Created by a non-accredited institution
FLEXIBLE PAYMENT options with multiple instalments

Programme Overview

  • 100+

    Learning Hours

  • 10

    Transformative Modules

  • 1

    Capstone Project

  • 200+

    Learners

  • 4+

    Case Studies

IIM Kozhikode’s Advanced Data Analytics for Managers is a guidepost to spur your understanding of analytics. This programme will help you manage and maximize a company’s data assets, integrate analytics into decisions and processes, and power innovation for businesses.

This programme’s focus on real-world examples, case studies, and practical sessions will ensure that you build a strong foundation in business analytics and make high-output business decisions.

Why enrol in this Data Analytics programme
Advanced Data Analytics for Managers		 Other Data Analytics programmes (Online via recorded lectures)
All classes are delivered via LIVE ONLINE format by IIMK faculty & industry experts
Executive ALUMNI Status
PEER-TO-PEER learning through weekly interactions with other participants
Accredited institution Dual accreditation No accreditation
Best-in-class curriculum Created by one of India’s premier B-School Created by a non-accredited institution
FLEXIBLE PAYMENT options with multiple instalments

  • $98B

    The predicted market size of the Indian analytics industry by 2025

    Source: Analytics India Industry Study 2021

  • 34.6%

    increase in the market value of the analytics industry in India in 2022

    Source: Analytics India Industry Study 2022

  • $215 B

    was the estimated global spend on big data and business analytics solutions in 2021, a 10% increase from 2021

    Source: IDC, 2021

The IIM Kozhikode Advantage

#2

Atal Innovation

Rankings (ARIIA) 2021

#3

India's Best B-School

The Week - Hansa Research Survey 2022

#3

NIRF India Rankings
2023: Management

Who is this Programme for?

This programme is best suited for mid to senior-level professionals seeking to gain cutting-edge analytical skills to establish a career in Business data Analytics and Data Science.

Professionals looking to develop a data-driven decisionmaking approach and the ability to leverage analytics for business growth and scale will also benefit from the programme.

Programme Highlights

10-month immersive and interactive programme

Learn through lectures, assignments and case studies

Peer-to-peer networking

Taught by distinguished IIM Kozhikode faculty

Comprehensive Capstone Project

Lifelong Executive Alumni Status

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Note: - The primary mode of learning for this programme is via live online sessions with faculty members. Post session video recordings will be made available, at the discretion of faculty member.

Programme Modules

  • Introduction R environment
  • IDE-R studio
  • Installing packages and loading packages in R
  • Creating variables
  • Scalars, vectors & matrices
  • List, data frames & data types
  • Converting between vector types
  • Cbind & Rbind
  • Attach and detach functions
  • Reading .csv and .txt files
  • Importing data from excel
  • Loading and storing data with a clipboard
  • Saving in R data, loading R data objects
  • Writing data into the file
  • Writing text and output from analyses to file
  • Rmarkdown
  • Data subsets
  • Selecting rows/observations
  • Rounding a number
  • Creating a string from variable
  • Factor labels
  • Selecting columns/fields
  • Merging data
  • Relabelling the column names
  • Data sorting, data aggregation, and finding and removing duplicate records
  • Application of dplyr package (select, arrange, mutate, aggregate, summarise, and group)
  • Basics of data visualisation using ggplot2
  • Aesthetic mappings
  • Common problems
  • Facets
  • Geometric objects
  • Position adjustments
  • Coordinate systems
  • The layered grammar of graphics
  • Combining plots
  • Execution of various types of plots (box plot, histogram, pie chart, line chart, scatterplot, word cloud, probability plots, mosaic plots, correlograms, and interactive graphs)
  • Data cleaning
  • Handling missing data
  • Data imputation
  • Feature filtering
  • Categorical feature filtering
  • Identifying misclassifications
  • Data transformation
  • Min-max normalisation
  • Z-score
  • Standardisation
  • Decimal scaling
  • Transformations to achieve normality
  • Outliers
  • Graphical methods for identifying outliers
  • Numerical methods for identifying outliers
  • Flag variables
  • Transforming categorical variables into numerical variables
  • Binning numerical variables reclassifying categorical variables
  • Adding an index field
  • Removing variables that are not useful
  • Data balancing techniques
  • Hypothesis testing versus exploratory data analysis
  • Getting to know the data set
  • Exploring categorical variables
  • Exploring numeric variables
  • Exploring multivariate relationships
  • Selecting interesting subsets of the data for further investigation
  • Using EDA to uncover anomalous fields
  • Binning based on predictive value
  • Deriving new variables: flag variables
  • Deriving new variables: numerical variables
  • Using EDA to investigate correlated predictor variables
  • Need for dimension-reduction in data mining
  • Principal components analysis (PCA)
  • Application of PCA
  • Statistical inference
  • Confidence interval estimation of the mean
  • The margin of error
  • Confidence interval estimation of the proportion
  • Hypothesis testing for the mean
  • Assessing the strength of evidence against the null hypothesis
  • Using confidence intervals to perform hypothesis tests
  • One-sample t-test
  • Paired sample t-test
  • Chi-square test for goodness of fit of multinomial data
  • Analysis of variance (ANOVA)
  • Supervised versus unsupervised methods
  • Statistical methodology and data mining methodology
  • Cross-validation
  • Overfitting
  • Bias-variance trade-off
  • Balancing the training data set
  • Establishing baseline performance
  • Simple regression analysis
  • Model formulation
  • Verifying the regression assumptions
  • Inference in regression
  • Multiple regression analysis
  • Dummy variable
  • Stepwise regression analyses
  • k-nearest neighbour algorithm
  • Decision tree
  • Random forest
  • Neural networks for estimation and prediction
  • Application of logistic regression for estimation and prediction
  • Naïve bayes and Bayesian networks
  • Hierarchical Clustering Methods
  • k-Means Clustering
  • Measuring Cluster Goodness
  • Affinity Analysis
  • Market Basket Analysis
  • Text mining and sentiment analysis
  • Social media analytics (Twitter)
  • Lexicon analysis
  • Social network analysis

Note:
- Modules/ topics are indicative only, and the suggested time and sequence may be dropped/ modified/ adapted to fit the participant profile & programme hours. The primary mode of learning for this programme is via live online sessions with faculty members. Post session video recordings will be made available, at the discretion of faculty member.

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Tools

Tools-Softwares Covered
Tools-Softwares Covered

Note:
- The tools will be taught by teaching faculty, industry practitioners, or linked to relevant knowledge bases for your reference and self-guided learning. All product and company names are trademarks or registered trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.

IIM Kozhikode Executive Alumni Status

On successful completion of the programme, participants will be eligible for the prestigious IIM Kozhikode Executive Alumni Status. These participants will subsequently receive the alumni registration details from IIM Kozhikode.

Note: The Executive Alumni benefits are subject to the discretion of IIM Kozhikode.

Learning Outcomes

  • Gain an in-depth understanding of data structures and data analysis to explore and visualise data for meaningful insights and identify relationships between large data sets
  • Learn to use analytical tool such as R to manipulate and analyse complex data sets and become proficient in building machine learning models using R
  • Explore text mining analysis/techniques to understand the influence of social media applications
  • Understand the nuances and applications of descriptive, predictive, and prescriptive analytics to enhance analytical skills and make real-time, data-driven business decisions
  • Gain the skills and knowledge required to manage data science and analytics teams or projects at your organisation
  • Get the managerial expertise of the tools and techniques used in Data Analytics and Machine Learning for business applications
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Real-world Case Studies

Data Science at Target
Data Science at Target by Srikant M. Datar, Caitlin N. Bowler (Harvard Business Publishing)

Exploring the technological and organisational challenges of a retail giant and trade-offs considered to develop it into a data science organisation.

Earning Manipulation
Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms by Dinesh Kumar Unnikrishnan, Tousif Ahmed Inayath Syed, Suresh Ganeshan (Harvard Business Publishing)

Using machine learning algorithms instead of traditional models such as Beneish model for better accuracy in Predicting Earnings Manipulation by Indian Firms.

Money Laundering
Armacord Incorporated: Combatting Money-laundering Using Data Analytics by Davit Khachatryan (Harvard Business Publishing)

Exploring predictive analytic solution, in the form of a time series model for combating money laundering.

Emeritus Insights

Free One-Year Access to Emeritus Insights

This programme features 1 year of free Premium Access to Emeritus Insights—a mobile app with 5,000+ bite-sized, business-focused videos to help you meet your daily learning goals on the go.

Participant Testimonials

"Course content was well curated for a manager’s need. Prof. Sreejesh is an excellent Teacher and he took immense efforts in ensuring everyone understood the lessons. The course was excellent. I do not have any prior experience in Data Science domain, this course provided me confidence to manage Data Science Projects."

Harikumar Vasudevan Nair

"I personally liked the following aspects of the program, The Faculty, Professor Sreejesh's ability to teach such a difficult subject so effectively to a diverse audience from the industry is amazing and commendable. The platform is glitch-free and we have never faced any issues while using it. The timeliness of all communications."

Kapil Chourasiya

Past Participant Profiles

Work Experience
Past Participant Experience
Top Industries
  • Infrastructure & Logistics
  • Manufacturing
  • IT & Services
  • Banking & Finance
  • Others*

*Others include Healthcare & Pharmaceuticals Shipping, Retail, Media, Consulting, etc.

Top Functions
  • Management
  • Operations
  • Finance & Accounting
  • Marketing & Sales
  • Others#

#Others include Customer Service, Engineering, Consulting, Legal etc.

Top Companies
  • Capgemini
  • Cisco Systems
  • Infosys
  • Johnson & Johnson
  • Oracle
  • Philips India

Past participants of Emeritus work at

Career Services image

Note: All product and company names are trademarks or registered trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.

Programme Directors

Prof. Sreejesh S, PhD

Programme Director & Associate Professor,

Marketing Management

Professor Sreejesh's primary research interests include marketing analytics, brand management, services marketing, and online marketing & advertising. His publications have appeared in Journal of Brand Management, Industrial Marketing Management, European Journal of Marketing, Computers in Human Behaviour, Journal of Travel and Tourism Marketing, International Journal of Contemporary Hospitality Management, Internet Research, Journal of Product and Brand Management, Journal of Service Theory and Practice, International Journal of Bank Marketing, etc. He serves on the editorial board of International Journal of Consumer Studies. He has also authored books of international repute with Pearson India and Springer International. Prof. Sreejesh S is currently working as an Assistant Professor of Marketing Management at IIM Kozhikode.

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Note:
- Programme Directors might change due to unavoidable circumstances, and revised details will be provided closer to programme start date.

Programme Certificate

Participants who successfully complete all evaluation components with minimum pass marks and meet the requisite 75% minimum attendance criteria will be awarded a Certificate of Completion from IIM Kozhikode. Participants who are unable to clear the evaluation criteria but have the requisite attendance will be awarded a Participation Certificate.

Sample Certificate

Note: We encourage you to attend all the live sessions and make the best out of these interactive learning experiences. Post-session video recordings will be made available, at the discretion of faculty member.

Emeritus Career Services

Career Services image
Career Services image
  • 15 Recorded sessions and resources in the following categories:
    • Resume and Cover Letter
    • Navigating Job Search
    • Interview Preparation
    • LinkedIn Profile Optimisation
    • Job Placement Assistance
  • Job Placement Assistance
    • Job openings are sourced by the CS Team and the virtual hiring drives are conducted for the applicants.
    • Dedicated online portal for all candidates where they can apply for jobs & track past applications.
    • Weekly job postings.

Please note:

  • This service is available only for Indian residents enrolled into select Emeritus programmes.
  • Opportunities for those with more than 5 years of experience may be more challenging due to the emphasis on transitioning to leadership positions within their current firms.
  • IIM Kozhikode or Emeritus do NOT promise or guarantee a job or progression in your current job. Career Services is only offered as a service that empowers you to manage your career proactively. The Career Services mentioned here are offered by Emeritus. IIM Kozhikode is NOT involved in any way and makes no commitments regarding the Career Services mentioned here.

Early applications encouraged. Limited seats are available.

View Payment Plan
Loan offered at EMI of INR 7,522
Special Corporate Enrolment Pricing

Round 1: The first application deadline is
July 31, 2023 and the fee to apply is INR 1,500 + GST

Round 2: The second application deadline is
Aug 28, 2023 and the fee to apply is INR 2,000 + GST

Round 3: The third application deadline is
Sept 22, 2023 and the fee to apply is INR 2,500 + GST

Round 4: The fourth application deadline is
Oct 5, 2023 and the fee to apply is INR 2,500 + GST

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The Learning Experience 

What is it like to learn with the learning collaborator, Emeritus? 

More than 300,000 professionals globally, across 200 countries, have chosen to advance their skills with Emeritus and its educational learning partners. In fact, 90 percent of the respondents of a recent survey across all our programs said that their learning outcomes were met or exceeded. All the contents of the course would be made available to students at the commencement of the course. However, to ensure the program delivers the desired learning outcomes, the students may appoint Emeritus to manage the delivery of the program in a cohort-based manner during the course period the cost of which is already included in the overall Course fee of the course.

A dedicated program support team is available 7 days a week to answer questions about the learning platform, technical issues, or anything else that may affect your learning experience.

In collaboration with Emeritus

Erulearning Solutions Private Limited (a company incorporated in India) is a subsidiary of Eruditus Learning Solutions Pte Ltd (a company incorporated in Singapore), and operates under the brand name of 'Eruditus' and 'Emeritus'.

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