Semester- III

Data Mining and Visualization

Course Code MMCA311A CIE Marks 50
Teaching Hours/Week (L:P:SDA) 3:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Cred its 03 Exam Hours 3
Course Learning objecti ves:
 Understand foundational concepts of data mining, including data preprocessing, pattern discovery, and
classification techniques.
 Apply data min ing algorith ms to extract useful patterns, trends, and insights fro m large datasets.
 Analyse and interpret mined data using appropriate visualizat ion techniques and tools.
 Develop skills to evaluate the performance of various data mining models and choose suitable techniques based on
problem context.
 Use data visualization tools and lib raries to present complex data and mining results in an intuitive and meaningful
way.

Module-1
Foundati ons of Data Mini ng and Data Preprocessing
Introduction to Data Mining & Preprocessing Techni ques: Introduction to data mining: Motivation, architecture, KDD
process. Types of data: Structured, semi-structured, unstructured.
Data preprocessing: Cleaning, integration, reduction, transformation, Missing Values and Noisy Data. Data summarization
and visualization techniques for preprocessing analysis. Imp lementation using Python: Pandas, NumPy for basic
preprocessing.
Module-2
Data Mini ng Techni ques and Algorithms
Mi ning Techni ques: Classification, Clustering & Association: Classification: Decision Trees, k-NN, Naive Bayes –
concepts and implementation. Clustering: k-Means, Hierarchical clustering.
Association rule mining: Market basket analysis, Apriori algorith m, FP-Gro wth. Evaluation methods: Confusion matrix,
precision, recall, ROC.

Module-3
Data Visualization Techni ques
Static and Interacti ve Data Visualizati on with Python: Principles of effective data vis ualizat ion. Visualization tools and
lib raries: Matplotlib, Seaborn, Plotly, Bokeh. Histograms, bar charts, scatter plots, heatmaps, and pair plots.
Dashboard creation using Jupyter notebooks and interactive widgets. Case studies and real-world examp les using mult i-
dimensional data.

Module-4
Visualizing Streaming and Real-Ti me Data
Real-Ti me Analytics and Streaming Data Visualization Overview of streaming data: Sources, characteristics, and tools.
Real-time processing with Apache Kafka, PySpark Streaming (introductory overview).
Visualizat ion strategies for streaming data. Tools: Dash by Plotly, Streamlit , Grafana.
Case studies: Sensor data, web server logs.
Module-5
Advanced Data Mi ning Applications and Trends
Emerging Trends and Applicati ons in Data Mining social media and text data. Sentiment analysis and NLP basics using
Python. Time series analysis and visualizat ion. Anomaly detection and predictive analytics. Ethical issues and future trends
in data min ing.

1
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%. The minimu m
passing mark for the CIE is 50% of the maximu m marks. Minimu m passing marks in SEE is 40% of the maximu m marks of
SEE. A student shall be deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 50% (50 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examinat ion) taken together.
Continuous Internal Evaluation:
1.
Two Un it Tests each of 25 Marks
2.
Two assignments each of 25 Marks or one Skill Development Acti vity of 50 marks
to attain the COs and POs
The sum of two tests, two assignments/skill Develop ment Activities, will be scaled down to 50 marks
CIE methods / question paper is designed to attain the di fferent levels of Bloom’s taxonomy as per the outcome
defined for the course.

Semester-End Examination:
1. The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 50.
2. The question paper will have ten full questions carrying equal marks.
3. Each full question is for 20 marks. There will be two full questions (with a maximu m of four sub-questions) from each
module.
4. Each fu ll question will have a sub-question covering all the topics under a module.
5. The students will have to answer five full questions, selecting one full question from each module
.

Suggested Learning Resources:

Books
1. Jiawei Han, Micheline Kamber, Jian Pei, Data Mining Concepts and Techniques, Morgan Kaufmann.

2. Wes McKi nney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Python , O’Reilly Media.

3. Ti m Grobmann, Mario Dobler, Data Visualization with Python, O’Reilly Media.

Web links and Vi de o Lectures (e-Resources):

 NPTEL – Data Mining by IIT Kharag pur (Prof. Pabitra M itra)

https://nptel.ac.in/courses/106105174
 Data Mini ng Full Course by Great Learning (YouTube)
https://www.youtube.com/watch?v=RlD5q_pIWkM
 Data Visualization using Python (Edureka)
https://www.youtube.com/watch?v=UB3DE5Bgfx4
 Harvard Data Science: Visualization (edX)
https://cs50.harvard.edu/
 Tableau for Data Visualizati on (Simplilearn)
https://www.youtube.com/watch?v=IFM 03Nis2dg

2
Skill Development Acti vities Suggested
 Hands -on Data Mini ng Projects
 Work on real-worl d datasets (e.g., Kaggle, UCI M L Repository).
 Implement data preprocessing, cleaning, and feature engineering.
 Apply classification, clustering, and association rule mi ning techniques.
 Learning and Using Data Visualization Tools
 Get hands-on with Tableau, Power B I, and Matpl otli b/Seaborn in Python.
 Use SQL for data extraction and processing.

Course outcome (Course Skill Set)

At the end of the course the student will be able to:
Sl. No. Descripti on Blooms
Level
CO1 Understand foundational concepts of data mining and apply preprocessing L2
techniques using Python.

CO2 Implement key data min ing techniques such as classification, clustering, and association rule L3
mining.
CO3 Design and develop effective static and interactive data visualizations using Python libraries. L3
CO4 Apply real-t ime v isualization strategies for streaming data using tools like Dash, Streamlit, L3
and Grafana.

CO5 Analyse advanced data min ing applications including sentiment analysis, time series, and L2
anomaly detection.

Mapping of COS and POs

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8
CO1 3 2 1
CO2 3 3 2 2
CO3 2 2 3 2 1
CO4 2 3 3 3 2
CO5 3 2 3 3 2 1

3
Semester- III
Big Data Analytics
Course Code MMCA311B CIE Marks 50
Teaching Hours/Week (L:P:SDA) 3:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Cred its 03 Exam Hours 3
Course Learning objecti ves:
 Understand Big Data Concepts – Gain a co mp rehensive understanding of Big Data, its characteristics, and its
significance in modern co mputing.
 Exp lore Big Data Technologies – Learn about various Big Data tools and frameworks such as Hadoop, Spark,
and NoSQL databases.
 Perform Data Processing & Analysis – Develop skills in processing, storing, and analysing large-scale data using
distributed computing techniques.
Module-1
Big Data Fundamentals and Ecosystem Overview
Introduction to Big Data: Concepts and Ecosystem: Defin ition and Evolution of Big Data. Characteristics of Big Data
(Volu me, Velocity, Variety, Veracity, Value). Traditional vs Big Data Systems,
Introduction to Hadoop Ecosystem: HDFS, YA RN, MapReduce. Architecture and components of Hadoop. Limitations of
Hadoop and the shift to Spark
Module-2
Hadoop Archi tecture and MapReduce Programming
Distributed Data Processing using Hadoop: Hadoop Distributed File System (HDFS): Design and operations. Hadoop
MapReduce: Programming model, job execution flow. Writing MapReduce programs (Word Count, Sorting, Joins).
Advanced Hadoop: Co mbiners, Partit ioners, Counters. Hadoop Streaming and int egration with Python.

Module-3
Apache S park for Big Data Analytics
In-Memory Big Data Processing with Spark: Spark arch itecture and components: RDDs, DA G, Executors.
Transformat ions and Actions on RDDs. Introduction to Data Frames and Spark SQL.
Introduction to Spark MLlib for machine learn ing. PySpark: Setting up and running Spark jobs using Python.
Module-4
NoSQL and Big Data Storage Systems
Scalable Data Storage wi th NoSQL Databases : Need for NoSQL: Limitations of RDBM S in Big Data. Types of
NoSQL Databases: Key-Value, Document, Co lu mn, Graph. Introduction to HBase: Architecture and CRUD operations.
Working with Cassandra and MongoDB. Data modelling for scalability and performance.
Module-5
Big Data Tools and Industry Applications
Real-Worl d Big Data Applicati ons and Tools: Overv iew of Big Data Tools: Hive, Pig, Sqoop, Flu me, Oo zie. Data
ingestion with Flu me and Sqoop. Use cases in healthcare, finance, e-commerce, IoT, social media. Real-t ime analytics
introduction using Kafka and Spark Streaming. Ethics and challenges in Big Data (p rivacy, bias, governance).

1
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%. The minimu m
passing mark for the CIE is 50% of the maximu m marks. Minimu m passing marks in SEE is 40% of the maximu m marks
of SEE. A student shall be deemed to have satisfied the academic requirements and earned the credits allotted to each
subject/ course if the student secures not less than 50% (50 marks out of 100) in the sum total of the CIE (Continuous
Internal Evaluation) and SEE (Semester End Examination) taken together.
Continuous Internal Evaluation:
1.
Two Un it Tests each of 25 Marks
2.
Two assignments each of 25 Marksor oneSkill Development Acti vity of 50 marks
to attain the COs and POs
The sum of two tests, two assignments/skill Develop ment Activities , will be scaled down to 50 marks
CIE methods / question paper is designed to attain the different levels of Bloom’s taxonomy as per the outcome
defined for the course.

Semester-End Examination:
1. The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 50.
2. The question paper will have ten full questions carrying equal marks.
3. Each full question is for 20 marks. There will be two full questions (with a maximu m of four sub-questions) fro m
each module.
4. Each fu ll question will have a sub-question covering all the topics under a module.
5. The students will have to answer five full questions, selecting one full qu estion from each module
.

Suggested Learning Resources:

Books
1. . Tom Whi te – Hadoop: The Definitive Guide, O’Reilly Media.

2. Vignesh Prajapati – Big Data Analytics with R and Hadoop, Packt Publishing.

3. Jure Leskovec, Anand Rajaraman, Jeff Ull man – Mining of Massive Datasets, Cambridge University Press.

4. Venkat Ankam – Big Data Analytics with Spark , Packt Publishing.

Web links and Vi deo Lectures (e-Resources):

 NPTEL Big Data Analytics Course – https://nptel.ac.in/courses/106/104/106104189/
 Simplilearn Big Data Tutorial (YouTube) – https://www.youtube.com/watch?v=-FrXAKGthF8
 Detailed explanation of Big Data concepts and tools.
 Big Data Analytics Using Python (YouTube - Great Learning) –
https://www.youtube.com/watch?v=ZkZclIFmg VY

Skill Development Acti vities Suggested

 Hands-on with Hadoop and S park – Work on real-t ime data processing using Hadoop (HDFS, Map Reduce)
and Apache Spark. Set up a small Hadoop cluster and practice writ ing Spark applications.
 Data Processing and SQL – Master SQL-based tools like Hive, Impala, and Presto. Work with large datasets to
optimize queries and imp rove performance.
 Machine Learning wi th Big Data – Imp lement mach ine learn ing algorithms using libraries like M Llib (Spark)
and TensorFlow with large datasets.

2
Course outcome (Course Skill Set)

At the end of the course the student will be able to :

Sl. No. Description Blooms Level
CO1 Understand the fundamental concepts, evolution, and architecture of Big Data, including L1
the Hadoop ecosystem.
CO2 L2
Develop and execute distributed data processing tasks using HDFS and MapReduce p rogramming techniques.
CO3 Analyze and imp lement in -memory data processing using Apache Spark and perform L3
mach ine learning tasks with Spark M Llib.
CO4 Co mpare and evaluate NoSQL data models (Key-Value, Docu ment, Colu mn, Graph) and L4
perform operations on HBase, MongoDB, and Cassandra.
CO5 Apply big data tools (Hive, Pig, Sqoop, Flu me, Kafka) in real-world do mains and L5
understand ethical issues related to Big Data analytics.

Mapping of COS and POs

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8
CO1 1 2
CO2 1 2 3
CO3 2 3 3 3
CO4 2 3
CO5 2 3 3 3

3
Semester- III
Business Data Analytics
Course Code MMCA311C CIE Marks 50
Teaching Hours/Week (L:P:SDA) 3:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Cred its 03 Exam Hours 3
Course Learning objecti ves:
 Understand the Fundamentals of Business Analytics – Learn the core concepts, tools, and techniques
used in data-driven decision-making for businesses.
 Data Collection and Processing – Gain knowledge of data acquisition, cleaning, transformation, and
management techniques for business applications.
 Exploratory Data Analysis & Visualization – Develop skills to analyze and visualize business data
using statistical and graphical methods.
 Predictive Analytics & Machine Learning – Apply statistical models and machine learning techniques
to predict business trends and customer behavior.
Module-1
Foundati ons of Business Data Analytics
Introduction to Business Analytics and Data-Dri ven Decision Making: Introduction to Business Analytics: Scope,
Types (Descriptive, Predict ive, Prescriptive). Data in Business: Structured vs Unstructured, Sources of Data. Business
Intelligence vs Business Analytics.
Analytics Life Cycle: CRISP-DM methodology. Role of Business Analyst: Tools, Skills, and Case Examples
Module-2
Data Preprocessing and Exploratory Analysis
Data Wrangling, Cleaning, and Expl oration for B usiness Insights:
Data Preparation: Cleaning, Integration, Transformat ion. Handling Missing Data, Outliers, and Noise. Descript ive
Statistics: Mean, Median, Mode, Variance, Skewness. Correlation and Covariance.
Visualizat ion for Exp loration : Histograms, Bo xp lots, Heatmaps. Business Case: Customer Seg mentation and Sales Data
Analysis.
Module-3
Predicti ve Analytics in Business
Forecasting and Predicti ve Modelling for B usiness Decision Making:
Introduction to Regression: Simp le & Multiple Linear Regression. Logistic Regression: Applications in classificat ion.
Time Series Analysis and Forecasting Techniques. Model Evaluation: RMSE, MA E, Accuracy, Precision, Recall.
Business Applications: Sales Forecasting, Customer Churn Predict ion.

Module-4
Prescripti ve Analytics and Opti mization
Opti mization and Decision-Making Techni ques:
Introduction to Prescriptive Analytics. Linear Programming and So lver in Excel. Optimization Models: Object ive
functions, Constraints. Sensitivity and Scenario Analysis. Decision Trees and Business Rules. Case Study: Resource
Allocation, Pricing Models, Supply Chain Optimization.

Module-5
Data Visualization and Business Intelligence Tools
Storytelling and Visualization for B usiness Insights
Principles of Data Visualization and Dashboards. BI Tools: Introduction to Power BI, Tab leau. Designing Interactive
Dashboards. KPI Defin ition and Visualizat ion. Business Reporting and Data-Driven Story telling. Final Capstone:
Co mplete Business Analytics Solution

1
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%. The minimu m
passing mark for the CIE is 50% of the maximu m marks. Minimu m passing marks in SEE is 40% of the maximu m marks
of SEE. A student shall be deemed to have satisfied the academic requirements and earned the credits allotted to each
subject/ course if the student secures not less than 50% (50 marks out of 100) in the sum total of the CIE (Continuous
Internal Evaluation) and SEE (Semester End Examination) taken together.
Continuous Internal Evaluation:
1. Two Un it Tests each of 25 Marks
2. Two assignments each of 25 Marks or one Skill Development Acti vity of 50 marks
to attain the COs and POs
The sum of two tests, two assignments/skill Develop ment Activities , will be scaled down to 50 marks
CIE methods / question paper is designed to attain the different levels of Bloom’s ta xonomy as per the outcome
defined for the course.

Semester-End Examination:
1. The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 50.
2. The question paper will have ten full questions carrying equal marks.
3. Each full question is for 20 marks. There will be two full questions (with a maximu m of four sub-questions) fro m
each module.
4. Each fu ll question will have a sub-question covering all the topics under a module.
5. The students will have to answer five full questions, selecting one full question from each module
.
Suggested Learning Resources:
Books
1. Abdulhamit Subasi, Practical Machine Learning for Data Analysis Using Python , Academic Press.

2. U. Dinesh Kumar, Business Analytics: The Science of Data-Driven Decision Making, Wiley.

3. Wes McKi nney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython , O’Reilly.

4. Wayne Winston, Microsoft Excel Data Analysis and Business Modeling, Microsoft Press.

Web links and Vi deo Lectures (e-Resources):

 Introducti on to Business Analytics – NPTEL Course
 Harvard Data Science and Business Anal ytics Lectures – YouTube Playlist
 Coursera: Business Analytics by Wharton – Coursera

Skill Development Acti vities Suggested

 Hands -on Experience wi th Data Tools
 Practice using Excel, Power B I, and Tableau for data visualization.
 Work with SQL and NoSQL databases (MySQL, MongoDB).
 Learn Python and R for data analysis.
 Real-worl d Data Projects
 Analyse publicly available datasets (Kaggle, UCI Machine Learning Repository).
 Work on case studies in business analytics (sales forecasting, customer segmentation).
 Implement predicti ve anal ytics models using machine learning.

2
Course outcome (Course Skill Set)
At the end of the course the student will be able to :
Sl. No. Descripti on Blooms Level
CO1 Understand the foundational concepts of business analytics, types of analytics, and the L1
data analytics lifecycle.
CO2 Apply data preprocessing techniques and perform exp loratory data analysis to extract L2
mean ingful business insights.
CO3 Develop and evaluate predictive models using regression and time series forecasting for L3
business decision making.
CO4 Implement prescript ive analytics using optimizat ion techniques to support data -driven L4
decisions in a business context.
CO4 Design interactive dashboards using BI tools and present data-driven stories for effective L5
communicat ion of business insights.

Mapping of COS and POs

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8
CO1 1 2
CO2 2 2 2
CO3 2 3 3
CO4 3 3 3
CO5

3
Semester- III
Enterprise Resource Planning
Course Code MMCA311D CIE Marks 50
Teaching Hours/Week (L:P:SDA) 3:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Cred its 03 Exam Hours 3
Course Learning objecti ves:
 Understand ERP Concepts – Explain the fundamentals of ERP, its evolution, and its significance in
modern businesses.
 Analyze ERP Modules – Explore core ERP modules like Finance, HR, Supply Chain, and Customer
Relationship Management (CRM).
 ERP Implementation Strategies – Understand the phases of ERP implementation, challenges, and best
practices.
 ERP Technologies & Trends – Examine emerging trends in ERP, such as cloud-based solutions, AI
integration, and analytics.
 Real-world Applications – Analyse case studies of ERP implementations in various industries to
understand its impact on business efficiency.
Module-1
ERP Systems and Business Process Integrati on
Fundamentals of ERP and B usiness Process Mappi ng Evolution of ERP – MRP, M RP II to ERP. Business Functions
and Business Processes. Need for Integration and ERP as an Integrator, Benefits, Risks, and Misconceptions of ERP.
Overview of Functional Modules: Finance, HR, Production, Sales. Case Example: Business Process before and after ERP

Module-2
ERP Architecture and Technologies
ERP System Architecture and Technological Infrastructure Client/Server Architecture, Serv ice-Oriented Architecture
(SOA ), Cloud-based ERP vs On-Premise ERP, ERP Platforms and Databases, ERP and Web Integration, Security,
Customization, and Interoperability in ERP Systems.
Overview of leading ERP systems: SA P, Oracle, M icrosoft Dynamics, Odoo

Module-3
ERP Modules and Functi onal Features
Core ERP Modules and Org anizational Applications Finance and Accounting Module, Manufacturing and Production
Planning, Sales and Distribution, Hu man Resource Management (HRM ).
Supply Chain Management (SCM ), CRM and Business Intelligence Features, Industry Examp les: ERP use in Retail,
Healthcare, Log istics.

Module-4
ERP Implementati on and Project Management
ERP Life Cycle and Implementation Strategies Phases of ERP Imp lementation Life Cycle, Business Process
Reengineering (BPR) and Change Management, Imp lementation Methodologies (ASAP, AIM), ERP Pro ject Planning,
Testing, Training, Go-Live & Support, Cost-Benefit Analysis, Vendor Select ion, Risk Management, Failure Cases and
Lessons Learned.

Module-5
Emerging Trends in ERP and Industry Practices
Future Directi ons and ERP in the Digital Era ERP with AI, ML, IoT, and Blockchain, ERP and Digital
Transformat ion, Mobile ERP and UX Design, ERP in SM Es and Cloud ERPs.
ERP Data Analytics and Reporting, Future Trends: Lo w-Code ERP, Industry 4.0 Integration, Capstone: Evaluation of
ERP for a case enterprise.

1
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%. The minimu m
passing mark for the CIE is 50% of the maximu m marks. Minimu m passing marks in SEE is 40% of the maximu m marks
of SEE. A student shall be deemed to have satisfied the academic requirements and earned the credits allotted to each
subject/ course if the student secures not less than 50% (50 marks out of 100) in the sum total of the CIE (Continuous
Internal Evaluation) and SEE (Semester End Examination) taken together.
Continuous Internal Evaluation:
1.
Two Un it Tests each of 25 Marks
2.
Two assignments each of 25 Marks or one Skill Development Acti vity of 50 marks
to attain the COs and POs
The sum of two tests, two assignments/skill Develop ment Activities , will be scaled down to 50 marks
CIE methods / question paper is designed to attain the different levels of Bloom’s taxonomy as per the outcome
defined for the course.

Semester-End Examination:
1. The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 50.
2. The question paper will have ten full questions carrying equal marks.
3. Each full question is for 20 marks. There will be two full questions (with a maximu m of four sub-questions) fro m
each module.
4. Each fu ll question will have a sub-question covering all the topics under a module.
5. The students will have to answer five full questions, selecting one full question from each module
.

Suggested Learning Resources:

Books
1. Alexis Leon, Enterprise Resource Planning, McGraw Hill Education.

2. Mary Sumner, Enterprise Resource Planning, Pearson Education.

3. Mahadeo Jais wal & Ganesh Vanapalli, Enterprise Resource Planning, Macmillan India.

4. Ellen Monk, Bret Wagner , Concepts in Enterprise Resource Planning, Cengage Learn ing.

Web links and Vi deo Lectures (e-Resources):

 https://www.youtube.com/watch?v=qgHlU_ll6mk
 https://www.youtube.com/watch?v=pSttK5Op1rI&ut m_source=chatgpt.com
 https://www.youtube.com/watch?v=JnSrp4k1gJw&utm_source=chatgpt.com
 https://www.youtube.com/watch?v=ppfBvofxCM 0&ut m_source=chatg pt.com
 https://www.youtube.com/watch?v=cblNqNEThcE&ut m_source=chatgpt.com

Skill Development Acti vities Suggested

 Map business processes of a small business (e.g., order-to-cash or procure-to-pay) using a
flowchart or BPMN tool (e.g., Draw.io or Lucidchart).
 Group discussion/debate on ERP benefits vs. risks in real- world businesses.
 Create a basic ERP system architecture diagram using PowerPoint or any diagramming tool.
 Create a module-wise feature matrix comparing SAP, Oracle, and Odoo ERP.

2
Course outcome (Course Skill Set)
At the end of the course the student will be able to:
Sl. No. Descripti on Blooms Level
CO1 Understand the fundamentals of ERP systems, their evolution, business L1
processes, and the need for integration.
CO2 Analyze ERP architectures, technologies, and distinguish between various L2
deployment models and ERP solutions.
CO3 Examine ERP functional modules and their applications across different L3
business domains.
CO4 Apply ERP implementation strategies, project management techniques, L4
and evaluate risk and success factors.
CO5 Explore and assess emerging trends in ERP such as AI, IoT, Blockchain, L5
and digital transformation practices.

Mapping of COS and POs

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8
CO1 1 2
CO2 1 2 3
CO3 2 3 3
CO4 3 3
CO5 3 3 3

3
Semester- III
Exploratory Data Analysis

Course Code MMCA311 E CIE Marks 50

Teaching Hours/Week (L:P:SDA) 3:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Cred its 03 Exam Hours 03
Course Learning objecti ves:
 To introduce the fundamental concepts and principles of exploratory data analysis.
 To equip students with skills to summarize and visualize both univariate and multivariate data
effectively.
 To develop the ability to clean, pre-process, and transform raw data for analysis.
 To expose learners to current trends and tools used in the field of EDA.

Module-1
Introduction to Exploratory Data Analysis: Historical background and role of EDA in data
science, Philosophy and goals of EDA, Comparison with classical statistical methods, Types of data
and scales of measurement, Importance of visual summaries before formal modelling.
Module-2
Univariate Data Exploration: Distribution shape: symmetry, skewness, kurtosis
Summary statistics: mean, median, mode, variance, standard deviation, range, IQR
Graphical techniques: histograms, dot plots, stem-and- leaf plots, boxplots, Identifying outliers and
anomalies.
Module-3
Bivariate and Multivariate Data Exploration: Scatter plots, trend analysis, Correlation vs.
causation,Crosstabs and pivot tables, Pair plots and heatmaps, Data smoothing (moving averages,
LOESS).
Module-4
Data Transformation and Cleaning: Motivation for data transformation (e.g., to achieve normality
or reduce skew) Log, square root, and other transformations, Handling missing values and duplicates,
Introduction to resistant statistics (median, trimmed mean).
Module-5
Emerging Trends and case studies: EDA as a storytelling tool, AI-powered visualizations,
integration with big data platforms.Case studies: EDA on real- world datasets (Titanic, Iris, planet),
pitfalls in EDA.

4
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%. The minimu m
passing mark for the CIE is 50% of the maximu m marks. Minimu m passing marks in SEE is 40% of the maximu m marks
of SEE. A student shall be deemed to have satisfied the academic requirements and earned the credits allotted to each
subject/ course if the student secures not less than 50% (50 marks out of 100) in the sum total of the CIE (Continuous
Internal Evaluation) and SEE (Semester End Examination) taken together.
Continuous Internal Evaluation:
1. Two Un it Tests each of 25 Marks
2. Two assignments each of 25 Marksor oneSkill Development Acti vity of 50 marks
to attain the COs and POs
The sum of two tests, two assignments/skill Develop ment Activities, will be scaled down to 50 marks
CIE methods / question paper is designed to attain the different levels of Bloom’s taxonomy as per the outcome
defined for the course.

Semester-End Examination:
1. The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 50.
2. The question paper will have ten full questions carrying equal marks.
3. Each full question is for 20 marks. There will be t wo full questions (with a maximu m of four sub -questions) fro m
each module.
4. Each fu ll question will have a sub-question covering all the topics under a module.
5. The students will have to answer five full questions, selecting one full question from each module

Suggested Learning Resources:

Books

.1. Exploratory Data Analysis by John W. Tukey.

2. An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert
Tibshirani

3. Think Stats: Exploratory Data Analysis in Python by Allen B. Downey

Reference Books:

1. Practical Statistics for Data Scientists by Peter Bruce, Andrew Bruce, and Peter Gedeck

Web links and Vi deo Lectures (e-Resources):

 https://youtu.be/fHFOANOHwh 8?si=MFGfiOEvPQSF -g2H
 https://youtu.be/w2QVZHcJapU?si=xfacUu80VK8J4fzc
 https://youtu.be/clblk_NwEU8?si=e4O8q LB6TnuaejdQ

Skill Development Acti vities Suggested

 Hands-on labs using Python for real- world datasets (Titanic, Iris, Sales, etc.)
 Participation in Kaggle or similar online EDA competitions
 Group project: Collaborative analysis and presentation of EDA findings

5
Course outcome (Course Skill Set)
At the end of the course the student will be able to :
Sl. No. Descripti on Blooms Level
CO1 Understand the role and importance of Exploratory Data Analysis in the
L2
data science pipeline.
CO2 Analyse univariate and bivariate datasets using appropriate summary L2
statistics and visualization techniques.
CO3 Apply data analysis techniques to explore relationships between multiple
L2
variables and derive insights using Python.
CO4 Apply data transformation and cleaning methods to prepare raw data for L2
further analysis.
CO5 Interpret insights from real-world datasets and communicate findings
L2
through visual storytelling and reporting.

Mapping of COS and POs

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8
CO1 1
CO2 2
CO3 1 2 3
CO4 1 3 3
CO5 1 2

6
Semester- III
Social Media Analytics
Course Code MMCA311 F CIE Marks 50
Teaching Hours/Week (L:P:SDA) 3:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Cred its 03 Exam Hours 3
Course Learning objecti ves:
 Understand the fundamentals and evolution of social media platforms.
 Explore key concepts and techniques in social media data collection and analysis.
 Apply analytics tools to extract insights from social media data.
 Develop skills in sentiment analysis, trend prediction, and influence measurement.
 Design data-driven strategies for business and marketing using social media insights.

Module-1
Introduction to S ocial Medi a Anal ytics
Foundati ons of Social Media Data Analysis Introduction to Social Media Analytics: Definition, Applications, and
Importance, Overview of Popular Social Media Platforms: Facebook, Twitter, Instagram, LinkedIn, YouTube
Social Media Data Types: Structured vs. Unstructured Dat, Social Media Metrics and KPIs: Engagement, Reach,
Impressions, Sentiment Score, Data Co llect ion Techniques: Web Scraping, APIs (Twitter, Facebook, YouTube),
Streaming Data
Module-2
Sentiment Analysis and Text Mini ng
Natural Language Processing for Social Medi a Data Fundamentals of Sentiment Analysis: Positive, Negative,
Neutral Sentiments, Text Preprocessing: Tokenization, Stopword Removal, Stemming, Lemmat ization
Machine Learning Approaches for Sentiment Classification : Naïve Bayes, SVM, LSTM, Word Embeddings and
Sentiment Scoring: TF-IDF, Word2Vec, BERT.

Module-3
Social Network Anal ysis and Trend Detection
Graph-based Soci al Medi a Anal ytics Basics of Social Network Analysis (SNA) Key Metrics: Centrality,
Clustering Coefficients, Community Detection, Influencer Identification and User Engagement Analytic
Hashtag Analysis and Topic Modelling using LDA, Trend Detection on Social Media: Time Series Analysis,
Virality Prediction.
Module-4
Visualizing and Interpreting Social Media Insights
Data Visualization and Interpretati on for Social Media Analytics Importance of Data Visualization in Social Media
Analytic.
Visualizat ion Techniques: Word Clouds, Heatmaps, Network Graphs Sentiment Heatmaps and Hashtag Trends
Visualizat ion, Dashboard Creat ion using Tableau and Power BI.
Module-5
Applications of Social Media Anal ytics in Business and Research
Business and Industry Applications of Social Medi a Analytics
Social Media Analytics in Digital Marketing: Ad Performance and Customer Engagement, Social Media in Business
Intelligence: Brand Monitoring and Crisis Management, Ethical Considerations in Social Media Analytics: Privacy, Bias,
and Data Protection.
Future Trends in Social Media Analytics: AI-Driven Social Insights, Capstone Project: Analyzing Real-World Social
Media Data for Business Insights.

1
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%. The minimu m
passing mark for the CIE is 50% of the maximu m marks. Minimu m passing marks in SEE is 40% of the maximu m marks
of SEE. A student shall be deemed to have satisfied the academic requirements and earned the credits allotted to each
subject/ course if the student secures not less than 50% (50 marks out of 100) in the sum total of the CIE (Continuous
Internal Evaluation) and SEE (Semester End Examination) taken together.
Continuous Internal Evaluation:
1.
Two Un it Tests each of 25 Marks
2.
Two assignments each of 25 Marks or one Skill Development Acti vity of 50 marks
to attain the COs and POs
The sum of two tests, two assignments/skill Develop ment Activities , will be scaled down to 50 marks
CIE methods / question paper is designed to attain the different levels of Bloom’s taxonomy as per the outcome
defined for the course.

Semester-End Examination:
1. The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 50.
2. The question paper will have ten full questions carrying equal marks.
3. Each full question is for 20 marks. There will be two full questions (with a maximu m of four sub-questions) fro m
each module.
4. Each fu ll question will have a sub-question covering all the topics under a module.
5. The students will have to answer five full questions, selecting on e full question from each module
.

Suggested Learning Resources:

Books
1. Matthew A. Russell, Min ing the Social Web: Data Mining Facebook, Twitter, Lin kedIn, Instagram, GitHub, and
More, O’Reilly Media.
2. Wasim Ahmed, Social Media Analytics: Techniques and Insights for Extracting Business Value Out of Social Media,
Wiley
3. Reza Zafarani, Mohammad Ali Abbasi, Huan Liu, Social Media Mining: An Introduction, Cambridge University
Press.
4. Piyushimita Thakuriah, Nebiyou Tilahun, Moira Zellner, Seeing Cit ies Through Big Data: Research, Methods and
Applications in Urban Info rmatics, Sp ringer.

Web links and Vi deo Lectures (e-Resources):

 NPTEL Course on Social Media Analytics – nptel.ac.in
 IBM Social Media Analytics Tutorials – ib m.co m
 YouTube Channel: Analytics Vidhya – youtube.com/analyticsvidhya

Skill Development Acti vities Suggested

 Hands-on training with social media analytics tools (e.g., Hootsuite, Google Analytics).
 Data collection fro m various social media platforms.
 Analyzing engagement metrics and sentiment.
 Creat ing visual dashboards and reports.
 Case studies on brand performance and campaign impact.


2
Course outcome (Course Skill Set)
At the end of the course the student will be able to :
Sl. No. Descripti on Blooms Level
CO1 Understand the fundamentals of social media analytics, data types, key L2
metrics, and data collection techniques.
CO2 Apply sentiment analysis and text mining techniques to analyze social L2
media data using NLP and ML models.
CO3 Analyze social network structures and trends using graph-based analytics, L3
hashtag modeling, and virality detection.
CO4 Visualize and interpret social media insights using tools like Tableau and L4
Power BI to support decision- making.
CO5 L5
Evaluate real-world applications of social media analytics in business, digital marketing, brand
monitoring, and ethical considerations.

Mapping of COS and POs

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8
CO1 1 2
CO2 2 2 3
CO3 2 3 3
CO4 2 3 3
CO5 3 3