Course description
Who Should attend & Course Pedagogy
- Bachelor’s Degree or Equivalent (10+2+3 or 10+2+4)/ 2 Years Masters Degree or Equivalent/ from a recognized university (UGC/AICTE/DEC/AIU/State Government) in any discipline.
- One-Year corporate experience is preferable.
- Participants who fulfil the above criteria are eligible for the programme.
ELIGIBILITY
- For Indian Participants – Graduates from a recognized University(UGC/AICTE/DEC/AIU/State Government) in any discipline.
- For International Participants – Graduation or equivalent degree from any recognized University or Institution in their respective country.
- Preferably the candidate should have studied Mathematics/Statistics up to 10+2 level.
- One-year corporate experience is preferable.
- On case-to-case basis less experience can be considered.
COURSE PEDAGOGY
The programme is a blended comprising both online and on-campus modules. For the online modules, the primary method of instruction will be through LIVE lectures that will be delivered online via the internet to participants’ desktops/laptops or classrooms. The lectures will be delivered by eminent faculty from IIM Kashipur and expert(s) from the industry. The programme will be primarily taught through a combination of class exercises, presentations, take-home exercises, simulation and case studies. The program contents are organized in a way to provide the participants with an introduction on the application of content to various business aspects.
THE CAPSTONE PROJECT
The Capstone Project gives you the opportunity to apply what you’ve learned about how to make data-driven decisions to real business scenarios. The project shall be mentored and evaluated by faculty from IIM Kashipur. The project will be presented to the faculty board during campus visit as a part of the requirement for successful completion of the program.
COURSE OBJECTIVES
- To help participants understand AI foundations and machine learning concepts.
- To help participants use R & Python software to implement machine learning concepts.
- To help participants learn to generate insights from real world data.
- To help participants appreciate the importance of data-driven decision-making in the organizational context.
Note
Nulearn is the authorized admissions and technology partner for IIM Kashipur, please visit the following link to verify: http://www.iimkashipur.ac.in/executive-education/online-certificate-programme/
Assignments, Videos & Case Studies
Curated Course Syllabus
Module: 1- Introduction to AI and Machine Learning
- In this module, participants will master the basics of the widely used open-source language R and Python programming. Participants will be able to utilize the various packages available in R and Python programming for visualization, reporting, data manipulation, and statistical analysis. Students can import data sets, transform, and manipulate those datasets for various analytical purposes. This module also briefs about artificial intelligence, business analytics, and machine learning.
AI Introduction
- Introduction to AI
- Goals of Artificial Intelligence
- Evolution of Artificial Intelligence
Business Analytics and Machine Learning
- Overview of Business Analytics
- Foundations of Business Analytics and Data-Driven Decision Making
- Basics of Machine Learning
Introduction to R
- Introduction to R Programming
- Getting Started with R
- Loading and Handling Data in R
- Exploring Data in R
- Operators and Expressions
- Control Structures
- Loops
- Functions
- R Packages
Introduction to Python
- Basics of Python Programming
- Operators and Expressions
- Decision Statements
- Loop Control Statements
- Functions & Python Packages
- Working with Files
- Object Oriented Concepts
Module: 2- Statistical Methods in Business Analytics
- In this module, participants will be introduced to fundamentals of statistical methods and its applications in business practices. Participants will also learn about steps of conducting Data Analysis, ways of summarizing data using charts and graphs, and drawing inferences from the data.
Probability and Random Variables
- Random Experiment, Rules of Probability
- Conditional Probability, Statistical Independence
- Joint Probability, Marginal Probability
- Random Variables, Expectation and Variance
- Probability Distribution of Discrete and Continuous Variables
Descriptive Analytics
- Measures of Central Tendency, Dispersion and Shape
- Correlation and Covariance
- Outliers and Missing Data
Inferential Analytics
- Sampling Methods
- Statistical Plots and Applications: Histogram, Bar Plot, Pie Chart, Box Plot, Density Plot, Scatter Plot etc.
- Creating Awesome Plots using R/Python
Data Driven Decision-Making
- Problem Solving in Simulated/Real Business Situations
Module: 3- AI -Supervised Machine Learning & Applications - Part I
- This module prepares participants to develop the necessary understanding and skills to use data and supervised machine learning techniques for causal inference. The module begins with an understanding of causality, and then move towards learning how to establish causality using regression models. Participants will also understand the basics of time series forecasting.
Causal Inference
- A Brief History of Causal Inference
- Causal Machine Learning
- Directed Acyclic Graph (DAG)
Building Statistical Models
- The Art and Science of Statistical Modelling
- Statistical Modelling from a Business Perspective
Dimensionality Reduction Techniques
- Principal Component Analysis
Regression Techniques
- Motivation for Linear Regression
- Ordinary Least Squares
- Inference
- Dummy Variables
- Functional Forms
- Advanced Techniques
- Linear Probability Model
Time Series and Forecasting Techniques
- Trends and Seasonality
- Stationarity
- Error Correction Models
- One-step-ahead Forecast
- Multiple-step-ahead Forecast
Module: 4- AI - Supervised Machine Learning & Applications - Part II
- Classification is the task of predicting the label or target group of an object, using its attributes or features. Classification task can be accomplished through various supervised machine learning techniques. We also discuss Ensemble techniques. Ensemble technique combines the decisions of multiple techniques and tries to improve the performance of the task, for example, regression or classification. This module covers various classification and ensemble techniques, along with relevant concepts.
Classification Techniques
- Logistic Regression
- Decision Trees
- Support Vector Machines
Evaluating Classification Models
- K-fold validation
- ROC and AUC Metric
- Confusion Matrix
Ensemble Techniques
- Random Forest
- Bagging
- Boosting
- Stochastic Gradient Boosting
Module: 5- AI - Unsupervised Machine Learning & Applications
- Clustering is an unsupervised machine learning technique used to identify groups of objects such that the objects are of a group are more similar to each other than to objects of other groups. This module discusses various clustering concepts and techniques in detail.
Clustering Techniques
- K-means
- Hierarchical
- DBSCAN
Clustering Concepts
- Distance Types in Clustering
- Clustering Performance Measures
Module: 6- AI - Machine Learning Techniques for Text Data
- In the current business environment, abundant unstructured data is available to organizations. In this unstructured data a large amount of data is in the form of text. The text data from within the organization, social media and external environment can be utilized to generate insights. Text analytics process can be used to build corpus and build representation of textual data for various text analytics based applications such as sentiment analysis, topic modelling, text clustering among others.
Text Analytics Process & Applications
Building Corpus
Corpus Pre-Processing
Sentiment Analysis
Topic Modelling
Text Clustering
Module: 7- AI – Deep Learning & Applications - Part I
- Deep learning based models are used to generate insights, answer business problems and build new products/services. Deep learning models utilizes neural network architecture and are used to provide solutions to tasks such as, speech recognition, image classification and handwriting transcription among others. Deep learning utilizing keras and tensorflow can lead to required data representations based on neural networks.
Introduction to Neural Networks
Tensors & Tensor Operations
TensorFlow & Keras
Deep learning & Anatomy of Neural Networks
Recurrent Neural Networks
Module: 8- AI – Deep Learning & Applications - Part II
- Computer vision (CV) technique enables computers to understand and interpret the visual objects. Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision to create real-world applications like medical discoveries, visual recognition systems, self-driving cars, emerging e-commerce and many more. Natural Language Processing (NLP) enables machines to understand, analyse, and generate natural languages.
Convolutional Neural Networks
- CNN overview
- Convolutional Layer
- Pooling Layer
- CNN Model Architecture
- Building CNN Model
Image Analytics
- Understanding Image
- Image Formation
- Image Segmentation
- Pattern Recognition in Image
- Image Pre-processing and Features Extraction
Computer Vision
- Basic Concepts
- Business Applications
- Learning and Inference in Vision
- Models for Computer Vision
- TensorFlow Recognition Application
- Challenges and Risks Associated with Implementing Computer Vision
Natural Language Processing
- Fundamentals of NLP
- Business Applications
- Building NLP System
Module: 9- Capstone Project
Capstone Project
Capstone Project Review
Capstone Project Presentations
Synthesis
30 Hrs. Tableau Masterclass by Nulearn
Introduction
- Introduction to Tableau
- Data Connection
- Tableau Environment
Basic Charts
- Text Tables
- Highlight Tables
- Pie Charts
- Bar Chart
- Stacked Bar Chart
- Side by Side Bar Chart
- Circle Chart
- Side by Side Circle Chart
- Bubble Chart
Advanced Charts - I
- Heat Map
- Tree Map
- Area Chart Discrete
- Area Chart Continuous
- Bullet Graph
- Symbol Map
- Filled Map
- Histogram & Bins
Advanced Charts - II
- Box and Whisker
- Line Chart Continuous
- Line Chart Discrete
- Dual Line Chart
- Dual Combination
- Scatter Plot
Customizing Charts
- Percentage Of
- Totals
- Filters & Highlighters
- Groups
- Hierarchy
- Sets
- Parameters
- Calculated Fields
- Dashboard
- Storyboard
Program Fees
*(Campus Fee & Alumni Fee charged separately)
For Indian Residents
Rs. 1,70,000 + GST*
Tenure
Deadline
Amount (INR)
1st instalment
Immediate Payment
Rs. 30,000 + GST*
2nd Instalment
20 March 2021
Rs. 40,000 + GST*
3rd Instalment
20 May 2021
Rs. 40,000 + GST*
4th instalment
20 July 2021
Rs. 30,000 + GST*
5th instalment
20 September 2021
Rs. 30,000 + GST*
For International Students
USD 2800
Campus Fee Rs. 40,000 + *GST & Alumni Fee Rs. 10,000 + *GST * EMI dates may vary on a case to case basis. For more information, please refer the welcome email sent to you after the registration.
* Loan option available in 6, 9 & 12 EMIs from loan partners.
* 1% + GST* processing fees will be charged by loan partner in 6/9/12 months EMI.
* 1% + GST* processing fees will be charged by loan partner in 6/9/12 months EMI.
Course Certificate
Certificate of Completion/ Participation will be given to participants on the basis of Assessments, Quizzes & Final Exam.
Click to enlarge
Tools to be Used
Program faculty members
Learn from the best
Dr. Rajiv Kumar
PhD - IIM Ranchi
Dr. Rajiv Kumar holds a Ph.D. in Information Systems from Indian Institute of Management Ranchi. He has completed M.Tech in media and sound enginee...
Dr. Abhradeep Maiti
PhD - Middle Tennessee State University USA
Prof Abhradeep Maiti holds a PhD in Economics from Middle Tennessee State University (USA) and a Master’s degree in Economics from University of ...
Dr. Harish Kumar
PhD - IIT Delhi
Dr. Harish Kumar earned his PhD from Department of Management Studies, Indian Institute of Technology Delhi and M.Tech. from International Institut...
Dr. Mayank Sharma
PhD - IIM Lucknow
Mayank Sharma is an Assistant Professor at Indian Institute of Management Kashipur in the area of Information Technology & Systems. He complete...
Dr. Sabyasachi Patra
PhD - IIT Kanpur
Dr. Sabyasachi Patra is a PhD from Industrial and Management Engineering, Indian Institute of Technology Kanpur and a Master of Production Engineer...
Dr. Venkataraghavan K
PhD - IIT Madras
Dr. Venkataraghavan K holds a PhD in Information Systems from the Indian Institute of Technology Madras. He researches around the themes of Busines...
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About the institute
IIM KASHIPUR
- The Institute is set up with the objective of providing quality management education while sensitizing students towards the needs of the society.
- The faculty at IIM Kashipur represent the best mix of academic and industrial experiences.
- The Institute is located in one of the most densely industrialized regions in the country with over 180 industries having plants in and around the region. This places the Institute at a vantage point when it comes to ‘Learning by Doing’ via frequent industry interactions and live projects.
- The rigorous curriculum seeks to instil a passion for knowledge and ability to apply that knowledge to real life scenarios.
The Indian Institute of Management Kashipur is set up with the mission to drive academic excellence using innovative teaching methods, promoting research and practice of sustainable leadership. The institute commenced operations in July 2011 with a vision to become, over the next 10 years, one of the top business schools in India developing leaders who can perform globally. The fraternity diligently follows the core values of mutual respect, lifelong learning, the pursuit of excellence, transparency, and integrity.
The flagship programmes of the institute are Master of Business Administration (MBA) and Master of Business Administration (Analytics) which are full time, two-year residential programmes. The rigorous educational curriculum tries to ingrain an enthusiasm for learning and the capacity to apply that information to real-life scenarios. The programme lays accentuation on all-round personality.
The institute also offers a two-year weekend Executive Master in Business Administration (MBA-WX) for working executives. Doctoral Programme – PhD and Executive Fellow Programme (EFPM) are the two doctoral programmes launched in 2015 and 2014 respectively. The faculty at IIM Kashipur represents one of the best mixes of academic and industrial experiences.
The 200-acre campus spreads loudness and cheers in the serene town of Kashipur, nested in the lap of Himalayas. The vibrant youth converging from across the country has lent life to and energized the beautiful campus. The institute brags of being situated in one of the densest industrialized districts in the nation with more than 180 ventures that have set up their plants in and around the region. This places the institution at a vantage moment when it comes to ‘Learning by Doing’ through regular industry interaction and live projects.
Start Your Application
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I have read the details and eligibility criteria for admission to the program, that I am applying for.
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I understand that my application shall be considered complete only when the Application fee along with required documents are received by IIM Rohtak and all mandatory fields in the application form are completed.
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I understand that merely submitting the application form and fee, does not entitle me for the admission to the Diploma programme The admission is purely on the basis of my profile scoring, qualification and further evaluation by the Institute.
Career Assistance
Corporate Prices / Plans
No. of Nomination
Discount
2 to 5
5%
6 to 10
7.5%
11 to 20
10%
21 and above
Contact the team
EMI Options / Plans
Partner
Tenure
EMI
1 Month
Rs. 1,60,000 + 18% GST
5 Months
Rs. 32,000 + 18% GST
9 Months
Rs. 29,891 Monthly
12 Months
Rs. 20,494 Monthly
15 Months
Rs. 15,725 Monthly
Please Note
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Processing Charges may be applicable as per bank rules.
-
No loan facility is available for foreign students.
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