IIM Kozhikode - IIM Kozhikode - Advanced Data Science for Managers

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IIM Kozhikode - Advanced Data Science for Managers
at
Emeritus
Overview

Gain an understanding of the tools and techniques used in Data Science and ML for business applications

Duration	12 months
Total fee	₹1.95 Lakh
Mode of learning	Online
Credential	Certificate

IIM Kozhikode - Advanced Data Science for Managers
at
Emeritus
Highlights

Earn a certificate of completion and alumni status from IIM Kozhikode
Learn from IIM Kozhikode faculty & industry experts
Learn from world-class curriculum, real-world case studies & a group project
Expand your professional network and interact with experienced peers
Three 90-minute workshops from career management industry experts

IIM Kozhikode - Advanced Data Science for Managers
at
Emeritus
Course details

Skills you will learn

R Python Data Processing Anova Data Science Statistics Hypothesis Testing Data visualization Data analysis

Who should do this course?

For managers and team leads managing analytics, data science and machine learning projects and teams
For business and data analysts looking to transition to data science roles or projects
For early-stage professionals interested in enhancing existing skills in data science and analytics
For Directors, business heads and senior management professionals
For Consultants who want to develop a strategic mindset
For Small business owners and entrepreneurs

What are the course deliverables?

Gain an understanding of data analysis and structures, and the relationship between data sets
Learn to use analytical tools such as R and Python
Explore text mining analysis and techniques to understand the influence of social media
Learn to use descriptive, predictive, and prescriptive analytics to make real-time, data-driven decisions
Gain the skills and knowledge required to manage data science and analytics teams or projects at your organisation

More about this course

This program provides insights on data management, exploratory data analysis, and the use of machine learning algorithms
The programme will help professionals enhance their proficiency in data science and gain in-depth skills and robust knowledge of machine learning algorithms and techniques supported by R and Python
Timing: Saturday 6:45 PM to 09:45 PM

IIM Kozhikode - Advanced Data Science for Managers
at
Emeritus
Curriculum

Module 1: Introduction to Data Science

Data Science - Scope, and Relevance

Data Science: Capabilities and Challenges

Module 2: Understanding the Python Environment

Introduction to Python and data types

Operations, functions, execution, and data processing

Modules, packages, exceptions, and working with files

Module 3: Understanding the R Environment

Introduction R - packages, data frames, data types, and functions

Importing, Loading, & Storing data and Writing output from analysis

Data aggregation & Data Handeling, and Applications of dplyr package

Module 4: Introductory Statistics & Hypothesis Testing

Statistical inference, hypothesis testing, one-sample and paired sample t-test

Test of proportions, two-sample t-test for difference in means

Chi-square test for goodness of fit of multinomial data, ANOVA

Module 5: Feature, Target Engineering & Pre-Processing

Data cleaning and Data transformation

Outliers, Binning and data balancing techniques

Module 6: Exploratory Data Analysis and Visualisation

Using EDA to generate useful insights, uncover anomalous fields & Application of PCA

Data visualisation and execution of various types of plots

Module 7: Machine Learning (Ml): Supervised Learning Algorithms

Bivariate and multiple regressions, dummy variable regression, and regularized regressions (RIDGE & LASSO regressions)

Supervised vs. unsupervised learning, and understanding/evaluating the model

Logistic regression: Bivariate and multinomial logistic regression

KNN algorithm for classification and prediction

Naïve Bayes Algorithm

Support vector machines

Decision trees, and Random Forest

Bagging and gradient boosting

Module 8: Machine Learning (Ml): Unsupervised Learning Algorithms

Clustering

Affinity analysis and market basket analysis

Principal component analysis (PCA)

Module 9: Deep Learning Algorithm

Deep learning, and artificial neural networks (ANN)

Module 10: Digital Media Analytics

Web Performance Analytics, and Google analytics

Optimal publisher strategy and complete copy improvement analysis in online platforms

Allocating resources between offline and online media

Introduction to social media analytics

Social network analysis (SNA) and generating social network metrics

Advanced SNA concepts using NodeXL and Gephi

Introduction to text analysis using WordArt, LIWC, and Weka

Deriving informative insights by combining social network and text analyses

IIM Kozhikode - Advanced Data Science for Managers
at
Emeritus
Faculty details

Sreejesh S

Prof. 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