Advanced Certification in Data Science and Decision Science (Batch 5)

Module 1: Overview to 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 

Learning outcomes:

• 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 2: Prescriptive Decision Science 

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

• Sensitivity Analysis using Solver/LINGO/Linear, Interactive, and Discrete Optimiser

• Goal Programming for Divergent Complex Objective (Multiple Objectives) using Solver/LINGO/Linear, Interactive, and Discrete Optimiser

• Application of Linear and Non-Linear Programming for Improving Business Decision-making through Case Study 

Learning outcomes:

• Understand Prescriptive Decision Science 

• Learn to develop prescriptive models using examples

• Learn to solve the prescriptive models 

• Understand the use of Excel solver and LINGO packages in solving the prescriptive models 

• Discuss practical cases to learn application of prescriptive decision science

Module 3: Predictive Decision Science and Temporal Data Models 

• Time Series Analysis (Moving Average, Exponential-based Predictive Model) 

• Advanced Time Series Analysis (Holtz and Winter-Holts Model) 

• Auto Regressive Integrated Moving Average Models 

Learning outcomes:

• Understand Predictive Decision Science 

• Discuss time series methods in Predictive Decision Science

• Discuss regression methods in predictive decision science

Module 4: Multi-Criteria Decision Science

• Multi-criteria Decision-making: Interpretive Structural Modelling and DEMATEL

• Multi-criteria Decision-making: Interpretive ranking Process and Technique for Order of Preference by Similarity to Ideal Solution

• Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) 

Learning outcomes:

• Understand Descriptive Decision Science

• Discuss popular Descriptive Decision Science using practical examples

Module 5: Decision Science Learning Enrichment and Assessment 

• Decision Science Case Study Approaches 

• Decision Science Capstone Project 

• Individual Evaluation on Decision Science  

Learning outcomes:

• Participate in group case study presentations 

• Experience the real-life applications of all pillars of decision science

• Participate in individual evaluation 

Module 1: Data Analysis Principles and Feature Engineering for ML Application Development

• Introduction to Data Science and Types of Data Management Enterprise Systems 

• Data Visualisation - Methods and Approaches in Computer Human Interaction Principles

• Data Model-building and Feature Selection Process for ML and Big Data Applications - Boston City Case Study 

Learning outcomes:

• Understand the range of data analysis using different methods

• Understand different visualisation methods to interpret large volumes of data 

• Develop aptitude to understand feature selection for building data models for ML applications

Module 2: Artificial Intelligence and Machine Learning

• Multidimensional Data Handling, Multinomial and Logistic Regression, Unsupervised Machine Learning for Clustering, and Association Rule Mining

• Supervised Machine Learning for Pattern Classification, Distance Based Algorithms, Data Normalisation for Machine Learning, Anomaly Detection, and Sequence Mining Algorithms

• Supervised ML - Decision Trees, Random Forest, SVM, Naïve Bayes Classifiers, and Ensemble Learning

• Governance of AI/ML – Establishing and Maintaining Fairness, Accountability, Transparency, Ethics, User Experience, and Regulations 

Learning outcomes:

• Learn the computational background of supervised machine learning algorithms

• Understand the feature selection process for model building

• Learn the computational background of unsupervised machine learning algorithms 

Module 3: Cognitive Deep Learning and Generative Artificial Intelligence 

• Machine Learning using Artificial Neural Networks, Feed Forward Neural Networks, Back Propagation, and Apriori Algorithm 

• Machine Learning using Deep Learning and Convoluted Neural Networks, Computer Vision Principles using CNN

• Generative Artificial Intelligence and Chatbots, Large Language Models using Deep Learning, and Decoding Transformer Architectures 

• Generative Artificial Intelligence, Conversational AI, and Prompt Engineering

• Reinforcement Learning and Federated learning 

Learning outcomes:

• Understand different models of Artificial Neural Networks (ANN) for complex data mining objectives 

• Understand the building blocks for computer vision

• Understand how large-scale graphs operate in internet ecosystems

• Understand how chatbots are designed

• Learn technological modules of generative AI systems

Module 4: AI for Cognitive Science and Big Data 

• NLP for Social Media and Text Mining - Sentiment Analysis, Opinion Mining, Topic Modelling, Latent Dirichlet Allocation, and Latent Semantic Analysis 

• Network Science with Graph Theory, Community Analysis, Node Association Analysis, and Hands-on Exercises with Small Networks Data 

• Using No-code Platforms for Running Advanced ML Applications

Learning outcomes:

• Understand how web search and social networks operate on user-generated data

• Learn using ML for analysing text and networks data 

• Understand the evolution of code based to no-code environments for data scientists

• Understand emerging ML paradigms for future 

Module 5: Data Science Learning Enrichment and Assessment

• Data Science Capstone Project Part 1 – Data Descriptives and Feature Selection Models

• Data Science Capstone Project Part 2 - Unsupervised and Supervised Machine Learning Implementations

• Individual Evaluation on Data Science and Machine Learning 

Learning outcomes:

• Learn model development for complex and large data sets for business problem-solving

• Learn how to deploy AI/ML algorithms for data science projects

• Develop an understanding on futuristic issues for data science professionals

Data Science: Learners will be provided with datasets containing large volumes of data. They will first need to demonstrate their proficiency in feature selection. Subsequently, they will apply unsupervised algorithms to the dataset. Finally, learners must implement multiple supervised machine learning algorithms and evaluate their suitability in the context of the given data or case study. The project may be undertaken using a combination of SPSS/PSPP, Python, and Orange.  

Decision Science: Learners will be introduced to all three pillars of decision science—prescriptive, predictive, and descriptive decision-making—through various modules. To facilitate the practical application of these concepts, a case study-based capstone project will be used. The relevant tools and techniques will be explored using Excel, Excel Solver, Python, and LINGO.

Graduates or Postgraduates in Science, Engineering, Business or any related disciplines can apply for this IIT Delhi Advanced Certification in Data Science and Decision Science programme.

The IIT Delhi Advanced Certification in Data Science and Decision Science is suitable for professionals who want to enhance their career prospects in the field of data science and decision science. The programme is designed for working professionals who want to learn the skills and knowledge needed to make data-driven decisions.

To begin the application, click the "Enrol Now" button and provide your full name, phone number, email address, and city to apply for this programme. Press the submit button. Our programme advisors will give you a call with specific instructions.

In case you see an ‘admission closed’ or ‘to be announced’ status, please fill out the Enquiry Form by selecting the "Enquire Now" option. Our course advisers will contact you to provide additional information.

The IIT Delhi Advanced Certification in Data Science and Decision Science is designed to be completed in 8 months.

Yes, IIT Delhi's Advanced Certification in Data Science and Decision Science programme is designed with a flexible schedule that allows students to balance their work and study commitments.

Yes, upon successful completion of the programme, students will receive an e-certificate from IIT Delhi. A "Certificate of Successful Completion" will be awarded to candidates with a minimum attendance requirement of 50% and an overall score of at least 50%. Those who complete the exam with fewer than 50% of the possible points and at least 50% of the required attendance will be awarded a "Certificate of Participation."

This is a live online sessions which will be delivered Direct-to-Device (D2D). Learners can access the classes on their Desktop, Laptop, Tablet, or Smartphone.

The IIT Delhi Advanced Certification in Data Science and Decision Science offers a comprehensive scope tailored for professionals seeking expertise in these cutting-edge fields in India. This certification provides rigorous training in essential areas such as statistical analysis, machine learning, big data analytics, and decision-making frameworks. You gain practical skills to harness data-driven insights for optimising business strategies, improving operational efficiencies, and innovating across diverse sectors, including IT, finance, healthcare, and e-commerce. The program's affiliation with IIT Delhi ensures credibility and recognition in the industry, enhancing career opportunities for roles such as Data Scientist, Machine Learning Engineer, Business Analyst, and Decision Scientist. Participants also benefit from networking opportunities with peers and industry leaders, further enriching their professional growth and potential for impactful contributions in India’s dynamic data science landscape.

After completing the IIT Delhi Advanced Certification in Data Science and Decision Science, various career opportunities open up, including roles such as data scientist, machine learning engineer, business analyst, data analyst, and decision scientist. Graduates can find employment in diverse industries like finance, healthcare, technology, and consulting, leveraging their skills in data analysis, predictive modelling, and decision-making to drive impactful insights and strategies.