Advanced Data Analytics for Managers
offered by IIM Kozhikode

Public/Government Institute
Estd. 1996

Advanced Data Analytics for Managers
at
IIM Kozhikode
Overview

Designed to emphasise the importance of being industry-ready in a data-driven future

Duration	10 months
Total fee	₹1.81 Lakh
Mode of learning	Online
Credential	Certificate

Advanced Data Analytics for Managers
at
IIM Kozhikode
Highlights

Earn a certificate & alumni status from IIM Kozhikode
Learn through lectures, assignments and case studies
Taught by distinguished IIM Kozhikode faculty

Advanced Data Analytics for Managers
at
IIM Kozhikode
Course details

Skills you will learn

Excel Data mining R Anova Data Science Data structures Business Analytics Statistics Hypothesis Testing Data visualization Data analysis

Who should do this course?

For mid to senior-level professionals seeking to gain cutting-edge analytical skills to establish a career in Business data Analytics and Data Science
For 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

What are the course deliverables?

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

More about this course

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

Advanced Data Analytics for Managers
at
IIM Kozhikode
Curriculum

Module 1: Introduction to Data Analytics & R

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

Module 2: Understanding Data Structure

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)

Module 3: Data Visualisation

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)

Module 4: Pre-process the Data

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

Module 5: Exploratory Data Analysis

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

Module 6: Statistical Inferences

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)

Advanced Data Analytics for Managers
at
IIM Kozhikode
Faculty details

Sreejesh S

Professor Sreejesh's primary research interests include marketing analytics, brand management, services marketing, and online marketing & advertising.

Advanced Data Analytics for Managers
at
IIM Kozhikode
Entry Requirements

Eligibility criteria Up Arrow Icon

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 March 28, 2023