Syllabus for
Master of Science (Computational Statistics and Applied AI)
Academic Year  (2023)

MSc (Computational Statistics and Applied AI) is a two year postgraduate programme offered in partnership with CleverInsight, Bangalore. The programme consists of 4 semesters where the classes are conducted on weekends. The industry-integrated MSc programme in Computational Statistics and Applied AI is an excellent opportunity for professionals who want to transition into the data science and AI fields. The programme structure is designed to equip students with industry-focused data science, data engineering and applied AI modules. The programme's practical training and hands-on experience will provide students with the skills and knowledge needed to become data-driven decision-makers. The collaboration with industry experts and live projects provides students with real-world experience, making them industry-ready.

CleverInsight: CleverInsight is a data science company focused on creating meaningful insights from customers' data. With an array of expertise across diverse fields, including banking and finance, healthcare, retail, social media, telecom, IOT, and government, CleverInsight offers an unmatched level of service and support that sets them apart from the competition. Their team of experienced bankers, data scientists, engineers, programmers, and marketers work collaboratively to deliver transformative solutions that help clients optimize their business outcomes, enhance their customer experiences, and promote innovation across their organizations.

CleverInsight takes pride in its ability to stay on the forefront of technological advancements, constantly seeking out cutting-edge tools and strategies to ensure that their clients remain competitive in an ever-changing market. By leveraging the latest technologies and methodologies, CleverInsight empowers clients to maximize their resources, improve operational efficiency, and achieve sustained growth. The company's commitment to excellence is evident in everything they do. From their customer-centric approach to their rigorous attention to detail, CleverInsight delivers measurable results that drive tangible value for their clients. Whether clients are seeking to optimize their business processes, drive innovation, or transform their operations, CleverInsight has the expertise and experience to help them achieve their goals. Website: https://cleverinsight.co

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO2: To identify opportunities in organizations for deploying statistical tools and methods.

PO3: To exercise data driven methodologies using AI and Statistical techniques to enhance organizational performance.

PO4: To reinvent the current business practices using applied AI.

MCAI131 (AZ) - Statistical Methods - (Centralized)

MCAI135 (SS) - Data Engineering & Data Pipelines - (Centralized)

MCAI132 (AVL)- Scripting Languages- (Department)

MCAI133 (AK) -Visual Analytics- (Department)

MCAI134 (BR)Prescriptive & Operational ModelsDepartment

To enable the students to understand the fundamentals of statistics to apply and infer descriptive measures and probability for data analysis. This course will enable students to understand basic probability and distribution, the concept of inference, analysis of variance, and regression and to apply these in real-life scenarios.

Data Representation, Average, Spread, Experiments, Outcomes, Events, Probability, Permutations and Combinations, Random Variables, Probability Distributions, Mean and Variance of a Distribution, Binomial, Poisson, and Hypergeometric Distributions, Normal Distribution, Distributions of Several Random Variables.

Types of Statistical Inference, Descriptive Statistics, Inferential Statistics, Descriptive Statistics, Measures of Central Tendency: Mean, Median, Mode, Midrange, Measures of Dispersion: Range, Variance, Mean Deviation, Standard Deviation. Coefficient of variation: Moments, Skewness, Kurtosis,

Estimation-properties, Methods of estimation-Point estimation, Interval estimation, One sample hypothesis testing: hypothesis, Testing of Hypothesis, Binomial distribution and normal distribution, Chi-Square Tests, t-test, Z test, F- test

Analysis of Variance: Fixed Effects, Random Effects, Mixed Models, 12 Introducing the Analysis of Variance (ANOVA), Performing the ANOVA, Random Effects ANOVA and Mixed Models, One-Way Random Effects ANOVA,

Correlation: Scatter plot, Karl Pearson coefficient of correlation, Spearman's rank correlation coefficient, multiple and partial correlations (for 3 variates only). Simple Linear Regression-OLS estimates- Properties.

[3]. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013.

[4]. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi, 2008.

ESE - 40%

The objective of this course is to provide comprehensive knowledge of scripting languages required for Data Science. Introduces computational thinking through applications of data science using the Julia programming language.

Structure of Python Program-Underlying mechanism of Module Execution-Branching and Looping-Problem Solving Using Branches and Loops-Functions - Lists and Mutability- Problem Solving Using Lists and Functions

Sequences, Mapping and Sets- Dictionaries- -Classes: Classes and Instances-Inheritance- Exceptional Handling- Introduction to Regular Expressions using “re” module Sequences, Mapping and Sets- Dictionaries- -Classes: Classes and Instances-Inheritance- Exceptional Handling-Introduction to Regular Expressions using “re”module.

Basics of NumPy-Computation on NumPy-Aggregations-Computation on Arrays- Introduction to Pandas Objects-Data indexing and Selection-Operating on Data in Pandas- Handling Missing Data-Hierarchical Indexing - Combining Data Sets- Aggregation and Grouping-Pivot Tables-Vectorized String Operations -Working with Time Series

Introduction, Basic Features, HTML and CSS, Visualizing Data, Managing Data, Creating an interactive dashboard with dc.js-summary

Introduction, Key features - Getting started with Julia- Working with packages and modules- Working with collections in Julia- arrays, Getting the data into a matrix, Computing basic statistics of the data stored in a matrix- Fitting a linear regression- Plotting the Anscombe's quartet data- Handling time series data and missing values- Toolbox for Data Analysis

[2] Jake VanderPlas ,Python Data Science Handbook - Essential Tools for Working with Data, O’Reily Media,Inc

ESE - 40%

Visual Analytics enables effective analytical reasoning based on data and interactive visual interfaces. Exploratory data visualization and analysis, in combination, are used for solving complex problems in data science.

Introduction of visual perception - visual representation of data - Gestalt principles -information overloads - Basic Chart Types- Effectiveness of Data Visualization - Visual Encoding: Marks and Channels - Use of Color in Visualization

Visualization reference model - visual mapping - visual analytics - Design of visualization Applications -Classification of visualization systems - Interaction and visualization techniques misleading. Visualization of one, two and multi-dimensional data, text and text documents Visualization elements -Visualization of groups, trees, graphs, clusters, networks, software - Metaphorical visualization

Design Principles- Choosing the Right Chart Type- Creation-formatting-create a device preview - filters - objects - story creation

Visual Analytics for Data Exploration- - Visual Analytics for Model Building and Understanding - Visual Analytics Techniques

Visualization of volumetric data, - vector fields, processes and simulations - Visualization of maps, geograp- Online Visualization Tools- Plotly, Sisense, PowerBI, IBM Watson analytics, Kibana, Grafana, D3.js, Fusion charts, Tableau public, charted, Google charts, Flot, chartist.js, High charts, Datawrapper, Dygraphs, Raw, Timeline JS, Polymaps

[2] Visualization Analysis & Design ,Tamara Munzner and Eamonn Maguire, A K Peters/CRC Press,2014 ISBN: 978-1-4665-0891-0

ESE - 40%

This course is designed to provide a comprehensive understanding of Prescriptive and Operational Models to individuals with little to no experience in data science, as well as to working professionals who want to expand their knowledge and skills in these areas. The course covers the basics of prescriptive modeling, such as optimization and decision analysis, and operational models, such as forecasting, simulation, and risk analysis.

Overview of Prescriptive and Operational Models -Linear Programming-Sensitivity Analysis and Interpretation

Non-linear Programming-Decision Analysis -Decision Tree Modeling

Forecasting-Time Series Analysis-ARIMA and Seasonal ARIMA models

Simulation-Monte Carlo Simulation-Risk Analysis

Optimization Techniques in Operational Models-Transportation and Assignment Problems-Case studies and real-world applications.

[2] Dursun Delen, “Prescriptive Analytics: The Final Frontier for Evidence-Based Management and Optimal Decision Making”, Pearson FT Press; 1st edition (24 October 2019)

[2] Dursun Delen, “Prescriptive Analytics: The Final Frontier for Evidence-Based Management and Optimal Decision Making”, Pearson FT Press; 1st edition (24 October 2019)

ESE - 40%

The course covers the topics of Data Engineering, Data pipelines, and big data technologies. It focuses on the implementation of Big Data Technologies such as Hadoop, Waddle, Spark, and Kafka and the application of Data pipeline tools.

Overview of Data Engineering and its importance -Data Modeling and Data Architecture -Understanding of Data Warehousing and ETL Processes.

Data Storage and Retrieval-SQL and NoSQL databases-Data warehousing, data marts, and data lakes.

Data Pipeline Architecture and Design-Batch Processing and Real-time processing-ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes.

Introduction to Big Data and its challenges-Distributed Computing and Parallel Processing-Big Data Technologies such as Hadoop, Waddle, Spark, and Kafka.

Data Pipeline Tools such as Apache Airflow, Luigi, or AWS Glue-Deployment and Maintenance of Data Pipelines-Best Practices and Case Studies.

ESE - 40%

This course enables the students to apply the concepts of neural networks and understand the concepts and models of the neural networks and deep learning and its applications.

Neural Networks-Application Scope of Neural Network Fundamental Concept of ANN: The Artificial Neura Network Biological Neural Network-Comparison between Biological Neuron and Artificial Neuron-Evolution of Neural Network

Basic models of ANN-Learning Methods- Activatio Functions Importance Terminologies of ANN

Shallow neural networks- Perceptron Networks-Theory-Perceptron Learning RuleArchitecture-Back Propagation Network- Theory - Architecture - Learning Factors for Back-Propagation Network.

Introduction - Components of CNN Architecture - Rectified Linear Unit (ReLU) Layer - -Architectures of CNN - Applications of CNN 

Introduction- The Architecture of Recurrent Neural Network- Echo-State Networks- Long Short-Term Memory (LSTM) - Applications of RNN

Introduction - Features of Auto encoder Types of Autoencoder Restricted Boltzmann Machine- Boltzmann Machine

[2] Dr. S Lovelyn Rose, Dr. L Ashok Kumar, Dr. D Karthika Renuka, Deep Learning Using Python, Wiley-India, 1st Edition, 2019.

[2] Francois Chollet, Deep Learning with Python, Manning Publications; 1st edition, 2017

[3] John D. Kelleher, Deep Learning (MIT Press Essential Knowledge series), The MIT Press, 2019

The goal is to make familiar with the concepts of the study of human language from a computational perspective. It  covers syntactic, semantic and discourse processing models, emphasizing machine learning concepts.

Introduction to NLP- Background and overview- NLP Applications -NLP hard AmbiguityAlgorithms and models, Knowledge Bottlenecks in NLPIntroduction to NLTK, Case study.

Word Level Analysis: Regular Expressions, Text Normalization, Edit Distance, Parsing and Syntax- Spelling, Error Detection and Correction-Words and Word classesPart-of Speech Tagging, Naive Bayes and Sentiment Classification: Case study.

N-gram Language Models: N-Grams, Evaluating Language Models -The language modeling problem

Semantic Analysis: Meaning Representation-Lexical Semantics- Ambiguity-Word Sense Disambiguation. Discourse Processing: cohesion-Reference Resolution- Discourse Coherence and Structure.

Natural Language Generation: Architecture of NLG Systems, Applications.

MachineTranslation: Problems in Machine Translation-Machine Translation Approaches- Evaluation of Machine  Translation systems.

Case study: Characteristics of Indian Languages.

Information Retrieval: Design features of Information Retrieval Systems-Classical, Non-classical, Alternative Models of Information Retrieval – valuation Lexical 

Resources: Word Embeddings - Word2vec-Glove.

Unsupervised Methods In NLP Graphical Models for Sequence Labelling in NL

[2] Daniel Jurafsky and James H., Speech and Language Processing, 2nd Edition, Martin Prentice Hall, 2013.

[2] Steven Bird, Ewan Klein and Edward Loper, Natural Language Processing with Python, O’Reilly Media; First edition, 2009

The Effective Data Driven Decision Science program is designed to equip students with the skills necessary to leverage data and analytics in decision making. Through this program, students will develop a deep understanding of data collection, analysis, and visualization techniques, as well as an understanding of how to develop compelling data narratives that drive effective decision-making.

Introduction to data-driven decision making and the role of  data in decision making - Key concepts and terminology in data science, including.

Descriptive, predictive, and prescriptive analytics - Overview of the data science process and how it can be applied to decision making - BUS framework for analytics story framing.

Techniques for collecting and cleaning data to ensure accuracy and completeness - Data preprocessing techniques such as data normalization, feature scaling, and dimensionality reduction - Exploratory data analysis techniques to identify patterns and trends in data. 

Techniques for developing predictive models using machine learning algorithms - Supervised learning techniques such as regression and classification - Unsupervised learning techniques such as clustering and anomaly detection.

Overview of prescriptive analytics and its role in decision making - Techniques for developing prescriptive models using optimization and simulation techniques - Overview of decision support systems and how they can be used to facilitate decision making. 

Techniques for implementing data-driven decisions in organizations - Effective communication strategies for presenting data and insights to decision makers - Ethical  considerations in data-driven decision making and the importance of data privacy and security 

[2] Cole Nussbaumer Knaflic, “Storytelling with Data: A Data Visualization Guide for Business Professionals", Wiley; 1st edition (20 November 2015) 

The course covers the topics of Applied AI in Business, BFSI, Healthcare, Bioinformatics and Manufacturing

Overview of AI in Business - Importance of AI in BFSI, Healthcare, Bioinformatics, and Manufacturing - Impact of AI on Business Processes and Decision Making - Case Studies of Successful Implementation of AI in Business

Introduction to BFSI Industry - Applications of AI in BFSI including Fraud Detection, Risk Management, Customer Service, and Investment Management - Regulatory and Ethical Considerations in AI Applications in BFSI - Realworld Case Studies of AI Implementation in BFSI.

Introduction to Healthcare Industry - Applications of AI in Healthcare including Diagnostics, Drug Discovery, Personalized Medicine, and Medical Imaging - Ethical and Regulatory Considerations in AI Applications in Healthcare - Real-world Case Studies of AI Implementation in Healthcare

Introduction to Bioinformatics - Applications of AI in Bioinformatics including DNA Sequencing, Protein Folding, and Drug Design - Challenges in AI Applications in Bioinformatics - Real-world Case Studies of AI Implementation in Bioinformatics 

Introduction to Manufacturing Industry - Applications of AI in Manufacturing including Predictive Maintenance, Quality Control, and Supply Chain Management - Ethical and Regulatory Considerations in AI Applications in Manufacturing - Real-world Case Studies of AI Implementation in Manufacturing.

[2] Alex Castrounis, “AI for People and Business: A Framework for Better Human Experiences and Business Success”, O′Reilly (23 July 2019)

[3] Raj Singh, “Artificial Intelligence In Banking & Finance: How AI is Impacting the Dynamics of Financial Services”, Adhyyan Books (14 January 2019)

The explosive growth of the “Internet of Things” is changing our world and the rapid growth of IoT components is allowing people to innovate new designs and products at home. Explore the basics of cloud analytics and the major cloud solutions and learn how to analyze extremely large data sets, and to create visual representations of that data

Introduction to IoT - Definition and Characteristics, Physical Design Things- Protocols, Logical Design- Functional Blocks, Communication Models- Communication APIsIntroductiontomeasurethephysicalquantities, IoT Enabling Technologies - WirelessSensor Networks, Cloud Computing Big Data Analytics, Communication Protocols- Embedded System- IoT Levels and Deployment Templates. 

Introduction to Smart Systems using IoT - IoT Design Methodology- IoT Boards (Raspberry Pi, Arduino) and IDE.

CaseStudy: Weather Monitoring - Logical Design using Python, Data types & Data Structures- Control Flow, Functions Modules- Packages, File Handling - Date/Time Operations,

Sensing and Sensors-Wireless Sensor Networks, Challenges and Constraints-Applications: Structural Health Monitoring,

Traffic Control, Health Care - Node Architecture - Operating system. Introduction – Fundamentals of MAC Protocols – MAC protocols for WSN – Sensor MAC Case Study –Routing Challenges and Design Issues – Routing Strategies– Transport Control Protocols–Transport Protocol Design Issues– Performance of Transport Protocols

Major benefits of cloud computing - Cloud computing deployment models - Types of cloud computing services - Emerging cloud technologies and services - Risks and challenges with the cloud - Parameters before adopting cloud strategy - Technologies utilized by cloud computing. Amazon Relational Data Store - Amazon DynamoDB - Google Cloud SQL - Google Cloud Datastore - Windows Azure SQL Database - Windows Azure Table Service

Cloud Dataflow - The Dataflow programming model - Cloud Pub/Sub - Cloud storage - Cloud SQL - Cloud BigTable - Cloud Spanner - Cloud Datastore - Persistent disks . Cloud Natural Language API – TensorFlow - Cloud Speech API - Cloud Translation API - Cloud Vision API - Cloud Video Intelligence – Dialogflow – AutoML

[2] Kazem Sohraby, Daniel Minoli and TaiebZnati, Wireless Sensor Networks: Technology. Protocols and Application, Wiley Publications, 2010.

[3] Waltenegus Dargie and Christian Poellabauer, Fundamentals of Wireless Sensor Networks: Theory and Practice, A John Wiley and Sons Ltd., 2010. 

[4] Snket Thodge, Cloud Analytics with Google Cloud Platform, Packt Publishing, 2018. 

[5] Arshdeep Bahga and Vijay Madisetti, Cloud computing - A Hands-On Approach, Create Space Independent Publishing Platform, 2014.

[2] Michael Miller, The Internet of Things, Pearson Education, 2015. 

[3] Holger Karl and Andreas Willig, Protocols and Architectures for Wireless Sensor Networks, John Wiley & Sons Inc., 2005.

[4] Erdal Çayırcı and Chunming Rong, SecurityinWirelessAdHocandSensorNetworks, John Wiley and Sons, 2009.

[5] Carlos De Morais Cordeiro and Dharma Prakash Agrawal, Ad Hoc and Sensor Networks: Theory and Applications, World Scientific Publishing, 2011.

[6] Adrian Perrig and J.D.Tygar, Secure Broadcast Communication: In Wired and Wireless Networks, Springer, 2006.

[7] Deven Shah, Kailash Jayaswal, Donald J. Houde, Jagannath Kallakurchi, Cloud Computing - Black Book, Wiley, 2014.

[8] Thomas Erl, Ricardo Puttini, Zaigham Mahmood, Cloud Computing: Concepts, Technology & Architecture, Prentice Hall, 2014