CHRIST (Deemed to University), Bangalore

DEPARTMENT OF STATISTICS_AND _DATA_SCIENCE

School of Business and Management

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

 
1 Semester - 2023 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCAI131 STATISTICAL METHODS Core Courses 4 4 100
MCAI132 SCRIPTING LANGUAGES Core Courses 4 4 100
MCAI133 VISUAL ANALYTICS Core Courses 4 4 100
MCAI134 PRESCRIPTIVE AND OPERATIONAL MODELS Core Courses 4 4 100
MCAI135 DATA ENGINEERING AND DATA PIPELINES Core Courses 4 4 100
2 Semester - 2023 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCAI231 STATISTICAL LEARNING MODELS Core Courses 4 4 100
MCAI232 STATISTICAL NLP AND NLG Core Courses 4 4 100
MCAI233 DATA DRIVEN DECISION SCIENCE Core Courses 4 4 100
MCAI234 APPLIED AI IN BUSINESS Core Courses 4 4 100
MCAI235 IOT AND CLOUD ANALYTICS Core Courses 4 4 100
    

    

Introduction to Program:

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:

PO1: To engage in continuous learning in the domains of statistics and data science.

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.

PO5: To acquire AI driven leadership and managerial skills.

Assesment Pattern

CIA: 60% and ESE 40%

Examination And Assesments

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

MCAI131 - STATISTICAL METHODS (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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.

Course Outcome

CO1: Describe basic probability and identify various discrete and continuous distributions and their usage

CO2: Analyze and explore data for a given problem

CO3: Draw scientific inference from data

CO4: Demonstrate basic principles and characterization properties of various designs of the experiment.

CO5: Infer the concept of correlation and regression for relating two or more related variables.

Unit-1
Teaching Hours:12
Basic Probability Theory and Distributions
 

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.

Unit-2
Teaching Hours:12
Descriptive Statistics and Inference
 

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,

Unit-3
Teaching Hours:12
Statistical Inference-II
 

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

Unit-4
Teaching Hours:12
Analysis of Variance
 

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,

Unit-5
Teaching Hours:12
Correlation and Regression
 

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.

Text Books And Reference Books:

Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11th edition, Sultan Chand & Sons, New Delhi, 2014.

Essential Reading / Recommended Reading

[1]. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015. [2]. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017.

[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.

Evaluation Pattern

CIA - 60%

ESE - 40%

MCAI132 - SCRIPTING LANGUAGES (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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.

Course Outcome

CO1: Readily use the Python programming language

CO2: Understand and implement JavaScript fundamentals

CO3: Implement numerical programming, data handling, and visualization

Unit-1
Teaching Hours:12
Introduction
 

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

Unit-2
Teaching Hours:12
Datatypes and Object-Oriented Programming
 

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.

Unit-3
Teaching Hours:12
Data Manipulation with Pandas
 

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

Unit-4
Teaching Hours:12
Java Script
 

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

Unit-5
Teaching Hours:12
Julia
 

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

Text Books And Reference Books:

“JavaScript for Data Science”, Maya Gans, Toby Hodges, Greg Wilson,CRC Press, ISBN 9781000028591, Edition 1

Essential Reading / Recommended Reading

[1] “Julia for Data Analysis”,Bogumił Kamiński, Manning Publications, 2022

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

Evaluation Pattern

CIA - 60%

ESE - 40%

MCAI133 - VISUAL ANALYTICS (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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.

Course Outcome

CO1: Understand the impact of data visualization for business decision-making and strategic planning

CO2: Design develop and evaluate effective visual interfaces to analyze tabular, textual, temporal, and spatial data

CO3: Identify appropriate ways to couple automated data transformation and modeling methods with interactive user interfaces and visualizations

Unit-1
Teaching Hours:12
Introduction
 

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

Unit-2
Teaching Hours:12
Visual representation
 

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

Unit-3
Teaching Hours:12
Dashboards
 

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

Unit-4
Teaching Hours:12
Visual Analytics
 

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

Unit-5
Teaching Hours:12
Visualization Application
 

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

Text Books And Reference Books:

Ward, Grinstein Keim, Interactive Data Visualization: Foundations, Techniques, and Applications. Natick: A K Peters Ltd, 2015

Essential Reading / Recommended Reading

[1] Data Visualization: A Practical Introduction, Kieran Healy, 2018

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

Evaluation Pattern

CIA - 60%

ESE - 40%

MCAI134 - PRESCRIPTIVE AND OPERATIONAL MODELS (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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.

Course Outcome

CO1: Understand the Prescriptive and Operational Models in Data Science.

CO2: Apply optimization and decision analysis on operational models

CO3: Implement forecasting, simulation, and risk analysis for real time applications

Unit-1
Teaching Hours:12
Introduction and Linear Programming
 

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

Unit-2
Teaching Hours:12
Non-linear Programming and Decision Analysis
 

Non-linear Programming-Decision Analysis -Decision Tree Modeling

Unit-3
Teaching Hours:12
Forecasting and Time Series Analysis
 

Forecasting-Time Series Analysis-ARIMA and Seasonal ARIMA models

Unit-4
Teaching Hours:12
Simulation and Risk Analysis
 

Simulation-Monte Carlo Simulation-Risk Analysis

Unit-5
Teaching Hours:12
Optimization Techniques in Operational Models and Case Studies
 

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

Text Books And Reference Books:

[1] Foster Provost, Tom Fawcett, “Data Science for Business: What You Need to Know about Data Mining and Data- Analytic Thinking”, O'Reilly Media; 1st edition (27 July 2013).

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

Essential Reading / Recommended Reading

[1] Foster Provost, Tom Fawcett, “Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking”, O'Reilly Media; 1st edition (27 July 2013). 22

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

Evaluation Pattern

CIA - 60%

ESE - 40%

MCAI135 - DATA ENGINEERING AND DATA PIPELINES (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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.

Course Outcome

CO1: Understand the concepts of Data modeling, Data architecture and Data Processing

CO2: Apply Data Pipeline Tools such as Apache Airflow, Luigi, or AWS Glue on real time data.

CO3: Implement Big Data Technologies such as Hadoop, Waddle, Spark, and Kafka.

Unit-1
Teaching Hours:12
Introduction to Data Engineering
 

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

Unit-2
Teaching Hours:12
Databases and Data Models
 

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

Unit-3
Teaching Hours:12
Data Pipelines and Processing
 

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

Unit-4
Teaching Hours:12
Big Data Technologies
 

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

Unit-5
Teaching Hours:12
Data Pipeline Tools and Deployment
 

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

Text Books And Reference Books:

Aurélien Géron, “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems", 3rd Edition, 2022.

Essential Reading / Recommended Reading

Aurélien Géron, “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems", 3rd Edition, 2022

Evaluation Pattern

CIA - 60%

ESE - 40%

MCAI231 - STATISTICAL LEARNING MODELS (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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.

 

 

Course Outcome

CO1: Understand the major technology trends in neural networks and deep learning

CO2: Build, train and apply neural networks and fully connected deep neural networks Global

CO3: Implement efficient (vectorized) neural networks for real-time application

Unit-1
Teaching Hours:12
Introduction To Artificial Neural Networks
 

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

Unit-2
Teaching Hours:12
Supervised Learning Network
 

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

Unit-3
Teaching Hours:12
Convolutional Neural Network
 

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

Unit-4
Teaching Hours:12
Recurrent Neural Network
 

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

Unit-5
Teaching Hours:12
Auto Encoder And Restricted Boltzmann Machine
 

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

Text Books And Reference Books:

[1] S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition, 2018.

 

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

Essential Reading / Recommended Reading

[1] Charu C. Aggarwal, Neural Networks and Deep Learning, Springer, September 2018.

 

[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

Evaluation Pattern

CIA: 60% and ESE: 40%

MCAI232 - STATISTICAL NLP AND NLG (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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.

Course Outcome

CO1: Understand various approaches on syntax and semantics in NLP

CO2: Apply various methods to discourse, generation, dialogue and summarization Global

CO3: Analyze various methodologies used in Machine Translation, machine learning techniques used in NLP including unsupervised models and to analyze real time applications

Unit-1
Teaching Hours:12
Introduction
 

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

Unit-2
Teaching Hours:12
Parsing And Syntax
 

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.

Unit-3
Teaching Hours:12
Smoothed Estimation And Language Modelling
 

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

Unit-3
Teaching Hours:12
Semantic Analysis And Discourse Processing
 

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

 

Unit-4
Teaching Hours:12
Natural Language Generation And Machine Translation
 

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.

Unit-5
Teaching Hours:12
Information Retrieval And Lexical Resources
 

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

Text Books And Reference Books:

[1] Dwight Gunning, “Natural Language Processing Fundamentals”Pakt,2019.

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

Essential Reading / Recommended Reading

[1] Roland R. Hausser, Foundations of Computational Linguistics: Human-computer Communication in Natural Language, Springer, 2014. 

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

Evaluation Pattern

CIA: 60% and ESE: 40%

MCAI233 - DATA DRIVEN DECISION SCIENCE (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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.

Course Outcome

CO1: Understand the fundamental concepts and terminology in data science, including descriptive, predictive, and prescriptive analytics, and their role in decision-making.

CO2: Implement exploratory data analysis techniques to identify patterns and trends in data.

CO3: Apply supervised and unsupervised machine learning algorithms to develop predictive models.

Unit-1
Teaching Hours:12
Foundations of Data Driven Decision Science
 

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.

Unit-2
Teaching Hours:12
Data Collection, Cleaning, and Preprocessing
 

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. 

Unit-3
Teaching Hours:12
Predictive Modeling and Machine Learning
 

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.

Unit-4
Teaching Hours:12
Prescriptive Analytics and Decision Support
 

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. 

Unit-5
Teaching Hours:12
Implementation and Communication of Data Driven Decisions
 

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 

Text Books And Reference Books:

[1] DJ Patil and Hilary Mason, “Data-Driven: Creating a Data Culture”, O'Reilly Media; 1st edition (5 January 2015)

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

 

Essential Reading / Recommended Reading

Jenny Dearborn and David Swanson, “Data-Driven: How Performance Analytics Delivers Extraordinary Sales Results", Wiley; 1st edition (2 February 2015)

Evaluation Pattern

CIA: 60% and ESE: 40%

MCAI234 - APPLIED AI IN BUSINESS (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand the importance of AI in various business sectors such as BFSI, Healthcare, Bioinformatics, and Manufacturing.

CO2: Implement AI applications in BFSI, Healthcare, Bioinformatics, and Manufacturing.

CO3: Apply the ethical and regulatory considerations for AI applications in BFSI, Healthcare, Bioinformatics, and Manufacturing.

Unit-1
Teaching Hours:12
Introduction to Applied AI in Business
 

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

Unit-2
Teaching Hours:12
AI Applications in BFSI
 

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.

Unit-3
Teaching Hours:12
AI Applications in Healthcare
 

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

Unit-4
Teaching Hours:12
AI Applications in Bioinformatics
 

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 

Unit-5
Teaching Hours:12
AI Applications in Manufacturing
 

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.

Text Books And Reference Books:

[1] Anirudh Koul, Siddha Ganju, Mehere Kasam, “Practical Deep Learning for Cloud, Mobile and Edge: Real-World AI & Computer Vision Projects Using Python, Keras & TensorFlow”, O′Reilly (1 November 2019)

 

[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)

Essential Reading / Recommended Reading

[1] Adelyn Zhou, “Applied Artificial Intelligence: A Handbook For Business Leaders”, Topbots Publisher (30 April 2018)

Evaluation Pattern

CIA: 60% and ESE: 40%

MCAI235 - IOT AND CLOUD ANALYTICS (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand the concepts of IoT and IoT enabling technologies

CO2: Develop an understanding of sensor network architectures from a design and performance perspective

CO3: Gain knowledge on IoT programming and able to develop IoT applications

CO4: Interpret the deployment and service models of cloud applications.

CO5: Ingest, store, and secure data.

Unit-1
Teaching Hours:12
Introduction to IOT
 

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. 

Unit-2
Teaching Hours:12
IOT Programming
 

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,

Unit-3
Teaching Hours:12
Network of wireless sensor nodes
 

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

Unit-4
Teaching Hours:12
Introduction to cloud computing
 

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

Unit-5
Teaching Hours:12
DATA INGESTION AND STORING
 

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

Text Books And Reference Books:

[1] Arshdeep Bahgaand, Vijay Madisetti, Internet of Things: Hands-on Approach, Hyderabad University Press, 2015.

[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.

Essential Reading / Recommended Reading

[1] Edgar Callaway, Wireless Sensor Networks: Architecture and Protocols, Auerbach Publications, 2003.

[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

Evaluation Pattern

CIA: 60% and ESE: 40%