Invite Your Colleague and Save INR 19,470 (Inclusive of GST)
STARTS ON September 30, 2022 Live Online Sessions
DURATION 10 Months Live Online Sessions 3 Hours/ week Sunday 6:45 PM to 9:45 PM
PROGRAMME FEE INR 1,81,500 + GST View Payment Plan 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 TBD.
STARTS ON September 30, 2022 Live Online Sessions
DURATION 10 Months Live Online Sessions 3 Hours/ week Sunday 6:45 PM to 9:45 PM
PROGRAMME FEE INR 1,81,500 + GST View Payment Plan

Programme Overview

The concept of data and analytics is now part of the business lexicon. Indian multinational organisations are integrating data science and analytics in their operations and have already seen big wins. Companies like Infosys (Times of India, 2020), Tata Consultancy Services (Analytics India Magazine, 2018), and Titan (Economic Times, 2020) have used analytics to inform their business strategy and added millions of dollars to their revenue. Yet only a minority of business managers have perfected the practice of using data to manage information and performance.

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.

As part of the programme, you will also get access to Emeritus Career Services, which empowers you to manage your career proactively.

Emeritus Career Services

Career Services image
Career Services image
  • Three 90-minute workshops from career management industry experts
  • Job placement assistance from partner companies are published, applied to, and tracked to success via an online platform (offered in partnership with Superset)
  • Past participants of Emeritus work at Microsoft, ICICI Bank, Infosys, HDFC, AirBnB, TCS, Ola, Flipkart, JSW, Wipro, Honeywell, JP Morgan, Reliance Jio, Mahindra, Gartner, Accenture, Cognizant, amongst others

Please note:

  • This service is available only for Indian residents enrolled into select Emeritus programmes.
  • 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.
  • The Indian analytics industry is predicted to grow to a market size of $98.0 billion by 2025 and $118.7 billion by 2026.

    (Analytics India Magazine, 2021)
  • By 2024, over 50% of enterprises will replace outdated operational models with cloud-centric models that facilitate rather than inhibit organizational collaboration, resulting in better business outcomes.

    (IDC, 2021)
  • The most in-demand skills currently are Data Analytics, Digital Literacy, Sales & Influencing, Data-based Decisions and Innovative Thinking.

    (Economic Times, 2021)

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

Develop a data-driven mindset to manage, visualise and analyse data effectively

Hands-on exercises using real-world data sets & practical sessions

Immersive learning journey with real-world case studies, business decision-related projects & Capstone Project

Taught by eminent IIM Kozhikode faculty, Emeritus global faculty & renowned industry experts

Certificate of Completion from IIM Kozhikode, one of India’s Leading B-Schools

Receive Lifelong Executive Alumni Status & Networking Opportunities

Tools

Tools-Softwares Covered
Tools-Softwares Covered

Note:
- R will be the primary tool for Data Science
- Tableau will be the primary tool for Data Visualisation

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.

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

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

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.

Read More

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

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

Early applications encouraged. Limited seats are available.

View Payment Plan
Special Corporate Enrolment Pricing

Round 1: The first application deadline is
Aug 03, 2022 and the fee to apply is INR 1,500 + GST

Round 2: The second application deadline is
Aug 31, 2022 and the fee to apply is INR 2,000 + GST

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'.

DOWNLOAD BROCHURE