Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based | Weekends |
Course Overview
The Advanced Certification in Data Science and Decision Science is a eight months executive course designed to bridge the gap between data-driven insights and strategic decision making. Offered by the Department of Management Studies at II Delhi in collaboration with TimesPro. This Advanced Certification in Data Science and Decision Science course provides a comprehensive curriculum that blends theoretical knowledge with practical application. The participants will engage in live online sessions, hands-on-projects and capstone assignments, all aimed at equipping them with the skills necessary to excel in the dynamic fields of data and decision sciences.
The participants will learn without disrupting their work schedules. This Advanced Certification in Data Science and Decision Science course ensures a balanced and in-depth learning experience. The participants will also have the opportunity for an optional immersion at IIT Delhi, fostering deeper engagement and networking.
The Highlights
- 8 month intensive programme
- Offered by IIT Delhi
- Certificate from CEP, IIT Delhi
- Campus immersion
- Direct-to-device learning for convenience
- Capstone projects
- Expert faculty
- Sessions on Generative AI and Large Language Models
Important dates
Course Commencement Date
Start Date : 05 Jul, 2025
Programme Offerings
- Offered by IIT Delhi
- Campus immersion
- Certificate from CEP
- IIT Delhi
- direct-to-device learning
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIT Delhi |
The fees for the Advanced Certification in Data Science and Decision Science course is :
Particulars | Amount |
Programme Fee | Rs. 1,89,000 |
GST @18% | Rs. 34,020 |
Total Fees | Rs. 2,23,020 |
Instalment schedule
Instalment | Particulars | Amount |
Application Fee* | To be paid at the time of application | Rs. 1,000 |
1st Instalment | Within 4 days of offer letter | Rs. 48,000 |
2nd Instalment | 18th August, 2025 | Rs. 47,000 |
3rd Instalment | 19th September, 2025 | Rs. 47,000 |
4th Instalment | 19th October, 2025 | Rs. 47,000 |
Eligibility Criteria
The participants who have completed their education in fields such as Science, Technology, Engineering, or Management, as well as other disciplines closely related to these areas. Also strong foundation in Mathematics is a key criterion for preference.
What you will learn
With the help of this Advanced Certification in Data Science and Decision Science course, the participants will delve into the intricacies of data manipulation and analysis using Python, gaining proficiency in handling large datasets and applying various statistical models. The curriculum emphasise the development of predictive models through supervised and unsupervised machine learning techniques, enabling participants to forecast outcomes and derive actionable insights. This Advanced Certification in Data Science and Decision Science course focuses on decision science methodologies, teaching participants to build and interpret models that aid in strategic business decisions.
Who it is for
The target audience for the Advanced Certification in Data Science and Decision Science course includes:
Admission Details
Follow the steps given below to take admission in Advanced Certification in Data Science and Decision Science:
Step 1: Visit the course page : https://timespro.com/executive-education/iit-delhi-advanced-certification-in-data-science-and-decision-science
Step 2: After ensuring that you fulfil the eligibility criteria, scroll down the page to fill out the application form.
Step 3: Enter your name, phone number, email ID, DOB, the exam percentage in 10th, 12th and graduation.
Step 4: Finally, select your centre, city and state and click on ‘Enquire now’ to submit the form.
The Syllabus
Module 1: Python Programming and Machine Learning Model Implementation
- Data Management and Manipulation
- Central Tendencies, Dispersion and Correlation Analysis
- Clustering, Multinomial Regression and Logistic Regression Analysis
- Longitudinal Data / Time Dependent Data Analysis
- Supervised and Unsupervised Machine Learning Implementation
- Natural Language Processing, Topic Modelling, and Sentiment Analysis
Learning outcomes
- Develop knowledge about data manipulation in Python
- Learn how to handle large volumes of data
- Learn to use different data analysis models on complex data for prediction models and forecasting
- Implement supervised and unsupervised machine learning models using Python
- Learn to develop managerial inferences from machine learning models
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
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
- Data Science
- Decision Science
Evaluation process
The participants will be evaluated through a combination of examination, capstone projects implementations and in-class assessments. Each vertical, data science and decision science carries equal weightage. To receive a 'Certificate of Successful Completion,' the participants must score at least 50% overall and maintain a minimum attendance of 40%. Those who score less than 50% but meet the attendance requirement will receive a 'Certificate of Participation.'