IIT Delhi Advanced Certificate Programme in Data Science and Decision Science Batch 04

About Course

The IIT Delhi Advanced Certification in Data Science and Decision Science addresses the dynamic needs of the industry, equipping participants with advanced skills in data analytics, artificial intelligence, and machine learning. With a strong focus on practical problem-solving for management decision-making, this programme blends rigorous theoretical knowledge with hands-on experience. Graduates are well-prepared to excel in the field of data science and drive substantial career advancements.

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Develop a strong understanding of different types of data science, artificial intelligence and machine learning algorithms and related mathematical models of decision science.
Develop a strong focus and problem-solving logic for handling complex data problems for management decision making involving data science and decision science.
Develop an acumen towards problem solving for complex data analysis with an algorithmic and systematic approach, which is techno-functional in nature.
Develop an acumen to understand and analyse large datasets with computationally intensive and mathematical algorithms.
Enable problem solving ability through hands on exercises and capstone projects.
Gear up for a transition towards a data science and decision science career whereby the shift may happen within the organisation or in a new organization

Course Content

Common Module for Data Science and Decision Science Vertical
Develop knowledge about data manipulation in python Learn how to handle large volumes of data Build skills to implement machine learning using python Develop managerial inferences from Big Data

Python programming

Tools to be Learnt

Capstone Project

Module 1: Descriptive and Inferential Analysis
Develop knowledge about data manipulation in python Learn how to handle large volumes of data Build skills to implement machine learning using python Develop managerial inferences from Big Data

Module 2: Artificial Intelligence and Machine Learning
Learn the computational background of supervised machine learning algorithms Learn the computational background of unsupervised machine learning algorithms

Module 3: AI/ML for Big Data and Cognitive Science
Build blocks for computer vision Understand how large-scale graphs operate in internet ecosystems Understand how web search and social networks operate on user generated data Learn to design Chatbots

Module 4: AI/ML for Managers
Understand governance of AI/ML systems in enterprises Learn the evolution of code based to no-code environments for data scientists Master emerging machine learning paradigms for future

Module 5: Data Science Learning Enrichment & Assessment
Learn how to deploy AI/ML algorithms for data science projects Develop understanding on futuristic issues for data science professional

Module 6: Overview to Decision Science
Understand the main pillars of Decision Science viz. Prescriptive, Predictive and Descriptive Decision Science To provide basics on Statistics to understand the main pillars of Decision Science

Module 7: Prescriptive Decision Science
Understand Prescriptive Decision Science Develop Prescriptive models using examples Solve Prescriptive models. Explain the use of Excel solver and LINGO packages in solving the prescriptive models Discuss practical cases to show application of Prescriptive Decision Science

Module 8: Predictive Decision Science
Understand Predictive Decision Science Discuss time series methods in Predictive Decision Science Learn regression methods in Predictive Decision Science

Module 9: Multi Criteria Decision Science
Understand Descriptive Decision Science Discuss popular Descriptive Decision Science using practical examples

Module 10: Decision Science Learning Enrichment & Assessment
Group case study presentations Demonstrate the real-life applications of all pillars of Decision Science Individual evaluation

Tools

Python
Pandas
Orange
Seaborn
NumPy
Matplotlib
PSPP
Excel SOLVER
LINGO
IBM SPSS
VOS Viewer

About Course

What Will You Learn?

Course Content

Common Module for Data Science and Decision Science Vertical Develop knowledge about data manipulation in python Learn how to handle large volumes of data Build skills to implement machine learning using python Develop managerial inferences from Big Data

Python programming

Tools to be Learnt

Capstone Project

Data Science Capstone Project

Decision Science Capstone Project

Module 1: Descriptive and Inferential Analysis Develop knowledge about data manipulation in python Learn how to handle large volumes of data Build skills to implement machine learning using python Develop managerial inferences from Big Data

Introduction to Data Science and Types of Data Management Enterprise Systems

Data Visualisation – Methods and Approaches in Computer Human Interaction Principles

Module 2: Artificial Intelligence and Machine Learning Learn the computational background of supervised machine learning algorithms Learn the computational background of unsupervised machine learning algorithms

Multidimensional Data handling, Regression, Unsupervised Machine Learning

Predictive Analytics with AI/ML – Advanced Supervised and Unsupervised Machine Learning

Machine Learning using Artificial Neural Networks and Fuzzy Set Theory

Supervised ML – Decision Trees, Random Forest, SVM, Naïve Bayes Classifiers, Ensemble Learning

Module 3: AI/ML for Big Data and Cognitive Science Build blocks for computer vision Understand how large-scale graphs operate in internet ecosystems Understand how web search and social networks operate on user generated data Learn to design Chatbots

Machine Learning using Deep Learning and Convoluted Neural Networks

NLP in Social Media Analytics – Sentiment Analysis, Text Summarisation, Topic Modelling, LDA, Network Analytics

Network Science with Graph Theory, hands on exercises with small networks data

Generative Artificial Intelligence and Chatbots, Large Language Models using Deep Learning

Module 4: AI/ML for Managers Understand governance of AI/ML systems in enterprises Learn the evolution of code based to no-code environments for data scientists Master emerging machine learning paradigms for future

Data model building for ML and Big Data applications – Boston City Case Study

Governance of AI/ML – Fairness, Accountability, Transparency, Ethics, UX & Regulations

UI driven Python (Orange), Supervised and Unsupervised Machine Learning

Generative Artificial Intelligence, Conversational AI and Prompt Engineering

Reinforcement Learning and Federated learning

Module 5: Data Science Learning Enrichment & Assessment Learn how to deploy AI/ML algorithms for data science projects Develop understanding on futuristic issues for data science professional

Data Science Capstone Project – Unsupervised and Supervised Machine Learning Implementations

Individual Evaluation on Data Science and Machine Learning

Module 6: Overview to Decision Science Understand the main pillars of Decision Science viz. Prescriptive, Predictive and Descriptive Decision Science To provide basics on Statistics to understand the main pillars of Decision Science

Understanding Main Pillars of Business Decision Science and Heuristics/Meta-Heuristics/AI

Central Limit Theorem, Distributions, Dispersion, Population, Sample, T Test, Z Test, Chi Square Test

Comparing Multiple Groups – ANOVA, MANOVA

Linear Algebra – Matrix Operations, Determinants, Vectors and Eigen values

Introduction to Linear Programming (Single Objective) and solving using Solver/ LINGO

Sensitivity Analysis using Solver/LINGO

Goal Programming (Multiple Objectives) Using Solver/LINGO

Application of LP/NLP in Business Decisions through Case Study

Module 8: Predictive Decision Science Understand Predictive Decision Science Discuss time series methods in Predictive Decision Science Learn regression methods in Predictive Decision Science

Time Series Analysis (Moving Average, Exponential)

Time Series Analysis (Holtz and Winter-Holts Model)

Auto Regressive Integrated Moving Average Models

Module 9: Multi Criteria Decision Science Understand Descriptive Decision Science Discuss popular Descriptive Decision Science using practical examples

Multi Criteria Decision Making: ISM, DEMATEL, AHP

Multi Criteria Decision Making: IRP, ANP, TOPSIS

Module 10: Decision Science Learning Enrichment & Assessment Group case study presentations Demonstrate the real-life applications of all pillars of Decision Science Individual evaluation

Decision Science Case Study Approaches

Decision Science Capstone Project

Individual Evaluation on Decision Science

Tools

Python

Pandas

Orange

Seaborn

NumPy

Matplotlib

PSPP

Excel SOLVER

LINGO

IBM SPSS

VOS Viewer