MCA Sem-4

Data Science and AnalyticsSyllabus

Course Code: LMC0401 Course Title:Data Science and Analytics (4-Credits)
Course Objectives : –

Ø  To gain the basic knowledge of Data Analytics.

Ø  To understand the roleof data analysis in Data Science.

Ø  To get a knowledge of Visualization.

Ø  To master in data Visualization using different tools and software’s.

Ø  The course focuses on concepts and uses of charts, diagrams, and other graphic elements in data visualization are to be studied in this subject.
*BTL- Bloom’s Taxonomy Level. 1- Understand, 2- Remember, 3- Apply, 4- Analyze, 5- Evaluate, 6- Create

Course Contents

Sr. No Unit No./ Unit description Unit Outcome
1

Unit 1– Introduction to Data Science:Data Science: Benefits and uses – facets of data – Data Science Process: Overview – Defining research goals – Retrieving data – Data preparation – Exploratory Data analysis – Build the model,Advantages of data science, Disadvantages of data science.

 This module has been designed as per BTL 1.
2 Unit 2-Real World Data : Relational Data , Transactional Data, Multi-Dimensional Data, Distributed Data, Spatial Data, Data Streams ,Time Serial Data, Text and Web Data, Multimedia Data. This module has been designed as per BTL 1&2.
3 Unit 3-Data Warehouse: Fundamentals of Data Warehouse and Data Marts, Data Warehouse Characteristics, Components , Approaches to build Data Marts and Data Warehouse. This module has been designed as per BTL 3.
4 Unit4-OLAP– Introduction to OLAP , OLAP  Applications, OLAP operations – Drill Down, Roll Up, Dice  and Slice. This module has been designed as per BTL 4.
5 Unit 5-OLTP– Introduction to OLTP , Examples of OLTP , Drawbacks and Benefits of OLTP , Difference between OLAP and OLTP. This module has been designed as per BTL 1&6.
6 Unit 6-Frequent Pattern Mining – Basic Problem Definition, Mining Association Rules , Apriori Algorithm , FIM Algo (Frequent Item set Mining  Algorithm),Frequent Pattern Growth Algorithm, ECLAT Algorithm, This module has been designed as per BTL 3&6.
7 Unit 7- Regression-Concept of Supervised and Unsupervised Machine Learning , Regression- Linear Regression, Multiple Linear Regression , Polynomial Regression This module has been designed as per BTL 3& 6.
8 Unit 8- Classification- Introduction to classification , Evaluation of Classifiers , Linear Model- Logistic Regression , Support Vector Machine, Non-Linear  Model- K-Nearest Neighbor (KNN) This module has been designed as per BTL 3&4.
9 Unit 9-Python Libraries for Data Wrangling– NumPy– Creating Arrays, Array Indexing, Array Iterating, Joining Array, Array Split, Array Search , Array Sort This module has been designed as per BTL 6.
10 Unit 10– Python Libraries for Data Wrangling -Pandas– Series, DataFrames , Read CSV, Analyzing DataFrames, Cleaning Data, Data Correlations. Matplotlib – Pyplot , Plotting, Subplot, Scatter, Bars, Histograms This module has been designed as per BTL 6.
11 Unit 11-Query Processing – Data Warehouse  with Materialized Views, Loading Data into Data Warehouses, Materialization View in DBMS, Query Optimization in DBMS, Query processing in DBMS This module has been designed as per BTL 4&5.
12 Unit 12–Power BI – Introduction to power BI , Power BI as Data Analytics powerhouse, Business Intelligence and Role of Power BI, Data Collection in Power BI, Data Visualizations in Power BI, Decision through Dashboards and Reports in Power BI. This module has been designed as per BTL 5 and 6.
13 Unit 13-DESCRIBING DATA– Types of Data – Types of Variables , Graphs This module has been designed as per BTL 1&5
14 Unit 14-DESCRIBING  DATA–Tables–Describing Data with Averages This module has been designed as per BTL 1&5
Text book References: –

  1. Joel Grus’s Data Science from scratch , Oreilly
  2. VikramPudi and P. RadhaKrshna , Data Mining , Oxford publication.
  3. Ian H. Witten &Eibe Frank ,Data Mining , Elsevier

Open Source References: –

  1.   https://www.geeksforgeeks.org/data-science-for-beginners/?ref=shm
  2. https://www.geeksforgeeks.org/power-bi-tutorial/
Course Code: LMC0402 Course Title:Mobile application design &development(4 Credits)
Course Objectives:

Ø  Learn Kotlin syntax, variable, function, decision making and loops.

Ø  Lear object-oriented programming concepts like Class, Object, Method, and inheritance.

Ø  Introduction to Android SDK, UI development, activities, intents, and working with various UI widgets.

Ø  Manage preferences, SQLite databases for data storage and retrieval.
BTL- Bloom’s Taxonomy Level. 1- Understand, 2- Remember, 3- Apply, 4- Analyse, 5- Evaluate, 6- Create

 

Mobile application design and development 

Course Contents:

S.no Unit No./ Unit description Learning Outcome
1 Unit 1­-Introduction to Kotlin Programming: Basics of Kotlin, Decision Making Statements in Kotlin, Loop Control Statements, Creating and Calling Functions, Advantages and Disadvantages. Student will Understand and Remember Kotlin Basics.(BTL 1,2)
2 Unit 2-Kotlin OOP Concept:Class, Object, Class Properties, Method, This keyword, Super keyword. Understand, Remember and Applythe core concepts.(BTL 1,2,3)
3 Unit 3­-Advance OOP Concept:Basics of Object-Oriented Programming, Inheritance, Sub class and super class, Abstract and Interface. Understand and Rememberconcepts of OOPin Kotlin.(BTL 1,2,3)
4 Unit 4­-Introduction to Android: History of Android, Android lifecycle, Open Handset Alliance, The Android Platform Architecture, Advantages and Disadvantages. Understand fundamental of Android Operating System. (BTL 1)
5 Unit 5-Android Studio IDE: Software Development Kit in detail, Android Virtual Design, Project Panel, Logcat Panel, Anatomy of an Android application, Creating a sample Android application. Students will Understand, Apply Android SDK features and develop application. (BTL 3,5,6)
6 Unit 6-Basics of Android: Android Terminologies like XML, View, Layout, Activity, Emulator, Manifest, Service, Broadcast Receiver, Content Providers and Intent. Students will Understand proficiency in utilizing Manifest File and Activity. (BTL 1)
7 Unit 7-Basic Android Views:TextView with properties method and listener, EditText with properties method and listener, Button with properties method and listener, ImageButton with properties method and listener. To Understand, Apply and analyseuser interfaces with various widgets. (BTL 2,3,4)
8 Unit 8-Advances Android Views: RadioGroup, RadioButton with properties method and listener, CheckBox with properties method and listener. To Apply and Analyseuser interfaces and handle user interactions. (BTL 3,4)
9 Unit 9-Date & Time Views: DatePicker with properties method and listener, TimePicker with properties method and listener. To Apply and Analyse user interfaces with widgets and handle user interactions. (BTL 3,4)
10 Unit 10-Android Layouts: Android Layouts basics, Constraint Layout with Properties, LinearLayout with Properties, RelativeLayout with Properties. To Understand, Apply and Analyse user interfaces with layouts and handle user interactions.(BTL 1,3,4)
11 Unit 11-Create Shared Preferences:Basics of Preference, Creating Preference, difference between getPreference() method and getSharedPreference() method Students will be able to analyse and implement data for storing etc.(BTL 4,6)
12 Unit 12-Shared Preferences Operations:Storing Data into Preference, Editor class, putString() method, difference between commit() and apply(), Retrieving data from preferences, use of getString() Students will be able to analyse and implement data for storing etc.(BTL 4,6)
13 Unit 13-Managing data using SQLite: SQLiteDatabase Creation using MVC Architecture, Insert Data Dynamically using MVC Architecture, retrieve data from SQLite Database using MVC Architecture. Students will be able to analyse and implement data management techniques. (BTL 4)
14 Unit 14­-Database Operations: Update Data to SQLite Database table Dynamically using MVC Architecture, Delete Data from table Dynamically using MVC Architecture. Students will be able to analyse and implement data management techniques. (BTL 4)

 

 

Textbooks:

  1. Joseph Annuzzi& Lauren darcey& ShaneConder, Advanced Android Application Development, Wesley
  2. Reto Meier. (2010), Professional Android 2 Application Development, Wiley
  3. Ian F. Darwin, Android cookbook, O’Reilly
  4. Jay A Kreibich.(2010) , Using SQLite , O′Reilly

 

Reference Books:

  1. Michael Burton.Android App Development for Dummies, Paperback.;3 edition
  2. The Android Developer’s CookBook – Building Application with Android SDK. ; 2 edition
  3. Mobile Computing using Android, Bharat & Company

Natural Language Processing Syllabus

Course Code: LMC0440 Course Title: Natural Language Processing                                     (4-Credits)
Course Objectives :

Ø  Understand and explain the basic concepts of NLP

Ø  Understand machine learning techniques to NLP.

Ø  Analyze text data for extracting meaningful insights using NLP techniques.

Ø  Using tools and libraries for real-world NLP applications

Ø  Assess and evaluate NLP models for their effectiveness and efficiency on specific tasks.

Ø  Identify real-life NLP applications and use them to solve real-world problems.
*BTL- Bloom’s Taxonomy Level. 1- Understand, 2- Remember, 3- Apply, 4- Analyze, 5- Evaluate, 6- Create

Course Contents

Sr. No Unit No. / Unit description Learning Outcome
1 Unit 1-Introduction to Natural Language Processing- Definition and Scope of NLP, Applications of NLP, Challenges in NLP, Overview of the NLP Pipeline. The students will be able to understand NLP basics. (BTL 1- Understand)
2

Unit 2: Text Preprocessing Techniques

Tokenization,  Stop words  Removal, Stemming and Lemmatization, Text Normalization , Part-of-Speech Tagging (POS), Handling Non-English Text

 The students will be able to understand and remember text preprocessing techniques. (BTL1- Understand, BTL2- Remember)
3

Unit 3: Introduction to Language Models

Definition and Importance of Language models, Types of Language models: Statistical vs. Neural, Applications of Language Models in NLP

 The students will be able to Understand Language Models in NLP. (BTL 1- Understand)
4

Unit 4: Probabilistic Language Models and Smoothing

Probabilistic Language Models, Markov Models, Smoothing Techniques (Additive Smoothing, Good-Turing Smoothing), Evaluation of Language Models

 The students will be able to Understand Language Models in NLP. (BTL 1- Understand)
5

Unit 5: Word Representations

Basics of Word representation in NLP, One-Hot Encoding, Distributed Representations: Dense vs. Sparse Vectors, Evaluation of Word Embeddings

 The students will be able to understand and remember word representation. (BTL1- Understand, BTL2- Remember
6

Unit 6: Word Embedding Techniques

Word2Vec: Skip-gram and CBOW, GloVe: Global Vectors for Word Representation, FastText: Subword Embeddings for Rare Words, Contextual Embeddings: BERT, ELMo

 The students will be able to understand and remember word representation. (BTL1- Understand, BTL2- Remember
7

Unit 7: Text Classification- Supervised Learning: Naive Bayes, Logistic Regression, SVM, Feature Extraction, and Classifiers for Text Classification, Evaluation Metrics.

 The students will be able to Analyze text data using NLP techniques. (BTL 4- Analyze )
8

Unit 8: Sentiment Analysis

Introduction to Sentiment Analysis, Lexicon-Based Approaches, Machine Learning Approaches, Feature Extraction for Sentiment Analysis, Evaluating Sentiment Models.

 The students will be able to understand and  Analyze text data .(BTL 1 – Understand ,BTL4 – Analyze )
9

Unit 9: Named Entity Recognition (NER) – Definition of Named Entities, Rule-Based NER Systems, Statistical Methods for NER, Deep Learning Methods for NER, Evaluation of NER Models

 The students will be able to Analyze text data for extracting meaningful insights.(BTL 4- Analyze )
10

Unit 10: Part-of-Speech Tagging

Basics of POS Tagging, Rule-Based POS Tagging, Statistical POS Tagging, Deep Learning Methods for POS, POS Tagging Evaluation (Accuracy, F-Score).

 The students will be able to understand and remember POS Tagging.(BTL1- Understanding, BTL 2 – Remembering)
11

Unit 11: Machine Translation –  Overview of Machine Translation (MT), Rule-Based and Statistical Machine Translation, Neural Machine Translation, Challenges in MT, Evaluation of Machine Translation.

 The students will be able to understand and use Machine Translations(BTL1 – Understanding, BTL3-Applying)
12

Unit 12: Information Retrieval and Question Answering- Introduction to Information Retrieval (IR), Indexing and Searching Techniques, QA Systems, Evaluating IR and QA Systems (Precision, Recall)

 The students will be able to use the applications of NLP in real-world scenarios OF QA. ( BTL3- Apply)
13

Unit 13: Real-Life Applications of NLP- NLP in Healthcare, NLP in Legal Systems, NLP for Social Media Analysis, NLP for Chatbots and Virtual Assistants, Ethical Considerations and Bias in NLP Applications

 The students will be able to Analyze the importance of the applications of NLP in healthcare and business applications. (BTL4- Analyze)
14

Unit 14: Future Trends and Challenges in NLP -Transformer Models,  Pretrained Language Models and Fine-Tuning, Transfer Learning in NLP, Multimodal NLP, NLP for Low-Resource Languages

 Identify real-life NLP applications and use them to solve real-world problems.(BTL- 3Apply)
Textbooks:

  1. “Speech and Language Processing” by Daniel Jurafsky and James H. Martin
  2. “Natural Language Processing with Python” by Steven Bird, Ewan Klein, and Edward Loper

Reference Books:

  1. “Foundations of Statistical Natural Language Processing” by Christopher Manning and Hinrich Schütze
  2. “Deep Learning for Natural Language Processing” by Palash Goyal, Sumit Pandey, Karan Jain
Course Code:  LMC0441 Course Title:Cloud Security Management(4 Credits)
Course Objectives:

Ø  Understand the fundamentals of cloud computing and deployment models.

Ø  Learn cloud security management strategies and risk mitigation techniques.

Ø  Explore the tools and technologies used in cloud security.

Ø  Understand how to design, implement, and manage secure cloud infrastructure.

Ø  Evaluate security best practices for different cloud environments.
BTL- Bloom’s Taxonomy Level.1-Understand, 2- Remember, 3- Apply, 4- Analyze, 5- Evaluate, 6- Create

 

Cloud Security Management Syllabus(4 Credits)

Course Contents:

S.no Unit No./ Unit description Learning Outcome
1 Unit 1-Introduction to Cloud Computing: Importance and Evolution, Cloud Computing Models (IAAS, PAAS, SAAS) & their distinctions, Deployment Models: Private , Public and Hybrid . Students will gain a  understanding of cloud computing concepts (BTL 1).
2 Unit 2-Cloud Service Providers & Marketplace: Overview of AWS, Azure, Google Cloud, Comparing Cloud Service Offerings, Understanding Cloud Market Trends. Students will develop an understanding of cloud service providers(BTL1& 2).
3 Unit 3-Cloud Architecture and Components: Virtualization, Elasticity, and Resource Pooling, Cloud Infrastructure: Compute, Storage, and Network components, Service Level Agreements (SLAs) and Cloud Management. Students will learn how cloud architectures function and are managed (BTL 2).
4 Unit 4-Designing Secure Cloud Architectures: Security-focused cloud design principles, Implementing secure cloud infrastructure, Key security configurations. Students will understand secure cloud architecture principles (BTL 3).
5 Unit 5 –Cloud Security Fundamentals: Basic Principles of Cloud Security, Security Challenges, SharedResponsibility Model, Data Privacy, and Compliance Requirements. Students will analyze cloud security principles and risk factors (BTL 3 & 4).
6 Unit 6-Identity and Access Management (IAM) in Cloud: Authentication, Authorization, and Auditing, Multi-Factor Authentication (MFA), Access Control Models (RBAC, ABAC). Students will gain insights into IAM strategies (BTL 3)
7 Unit 7-Cloud Data Security and Encryption: Data Security Risks, Cryptographic Principles (Sym vs. Asy Encryption), Encryption in Transit and at Rest, Key Management Systems, Cloud Data Loss Prevention. Students will learn about encryption and data security (BTL 3 & 4).
8 Unit 8-Cloud Network Security: Network Security Risks, Virtual Private Networks (VPNs), Virtual Private Clouds (VPCs), Firewalls, Load Balancers, Intrusion Detection and Prevention Systems (IDPS). Students will analyze network security in cloud environments (BTL 3 & 4).
9 Unit 9-Risk Management and Compliance in Cloud: Identifying Risks, Risk Assessment, Compliance Frameworks (GDPR, HIPAA, PCI-DSS), Third-Party Audits, Certifications. Students will develop risk assessment and compliance evaluation skills (BTL 4 & 5).
10 Unit 1­0-Cloud Security Monitoring and Incident Response: Security Monitoring Tools, Incident Response Plans, Breach Detection & Mitigation, CloudForensics. Students will understand incident response and forensic analysis (BTL 4).
11 Unit 11-Application Security in Cloud: Secure Software Development Lifecycle (SDLC), Application Security Tools (WAF, DDoS Protection), Security Testing for Cloud Apps. Students will acquire cloud application security expertise (BTL 4 & 5).
12 Unit 12-Securing Hybrid and Multi-Cloud Environments: Multi-Cloud Security Challenges, Best Practices, Data Integration Risks, Cross-Cloud Communication Security. Students will understand hybrid and multi-cloud security concerns (BTL 4).
13 Unit 13­-Advanced Cloud Security Techniques: AI & ML in Cloud Security, Blockchain for Cloud Security, Zero Trust Architecture, Cloud Automation. Students will explore cutting-edge cloud security technologies (BTL 4 & 5).
14 Unit 1­4-Cloud Security Best Practices & Future Trends: DevSecOps, Future Trends (AI, Quantum Computing), Cloud Security Certifications (ISO, SOC2, NIST). Students will be aware of the latest cloud security advancements (BTL 4 & 5).

 

 

Textbooks:

  1. Cloud Computing Implementation, Management, and Security , “By John W.Ritting House and James F.Ransome,”Taylor and Francis Group.
  2. White Book of Security.

 

Reference Books:

  1. Cloud Computing: Concepts, Technology & Architecture written by Zaigham Mahmood, Ricardo Puttini, and Thomas Erl.
  2. Cloud Computing: Methodology, Systems, and Applications by Lizhe Wang, Rajiv Ranjan.
Course Code:  LMC0442 Course Title:Descriptive Analytics for IoT(4 Credits)
Course Objectives: –

Ø  Understand the fundamental concepts of the Internet of Things, including its architecture, components, and applications.

Ø  Analyse and evaluate IoT communication protocols, focusing on their role in the IoT ecosystem.

Ø  Design and implement IoT systems, incorporating hardware, software, and networking components.

Ø  Examine security and privacy issues in IoT systems and propose methods for mitigating risks.

Ø  Explore emerging IoT trends and technologies, and assess their potential impact on industries and society.
BTL- Bloom’s Taxonomy Level. 1- Understand, 2- Remember, 3- Apply, 4- Analyse, 5- Evaluate, 6- Create

 

Descriptive Analytics for IoT (4 Credits)

 

Course Contents:

Unit Unit Description Learning Outcome
1

Unit 1: Introduction to IoT-Definition and evolution of IoT,IoT application domains,IoT architecture: Sensors, communication, processing, and actuation

 Students will gain a foundational understanding.  (BTL 1).
2

Unit 2: IoT Architecture and Design Principles–Layers of IoT architecture: Perception, network, edge, and application layers,Design principles and considerations for IoT systems.

 Students will understand the IoT arch.and design (BTL 1).
3

Unit 3: IoT Communication Protocols – Overview of IoT communication protocols: Wi-Fi, Zigbee, Bluetooth, MQTT, CoAP, Protocols for different IoT use cases

 Students will understand Protocols.(BTL 3).
4

Unit 4: IoT Devices and Sensors – Types of IoT devices (sensors, actuators, controllers), Types of sensors used in IoT systems (temperature, motion, pressure, etc.), Integration of sensors with IoT systems

 Students will acquire IoT devices knowledge and use the devices.  (BTL  3).
5

Unit 5: Data Processing and Management in IoT – Big data in IoT, Data acquisition, storage, and processing techniques, Cloud computing in IoT systems

 Students will gain skills in analysing data and will manage it.(BTL 4).
6

Unit 6: Networking and IoT Communication Technologies-IoT networking protocols (LPWAN, 5G, etc.), Role of networking in IoT scalability and efficiency, Challenges in IoT networking

 Students will able analyse the networking in IoT.(BTL 4).
7

Unit 7: Cloud and Edge Computing in IoT- Cloud computing architecture in IoT, Benefits and challenges of edge computing in IoT, IoT cloud platforms: AWS IoT, Azure IoT, Google Cloud IoT

 Students will evaluate the different cloud platforms and there benefits.(BTL 5).
8

Unit 8: Security in IoT- IoT security challenges: Authentication, confidentiality, and integrity, Security protocols for IoT devices, IoT security standards (e.g., ISO/IEC)

 Students will understand and evaluate security challenges (BTL 1& 5).
9

Unit 9: Privacy Issues in IoT-Privacy risks in IoT ecosystems, Data protection and anonymization techniques, Legal and ethical considerations of IoT data collection.

 Students will understand Privacy Issues and create data protection ecosystem. (BTL 1& 6).
10

Unit 10: IoT Standards and Frameworks- Role of standards in IoT development, Overview of IoT standards (IEEE, ITU, IETF), Industry frameworks and IoT ecosystems.

 Students will understand theIoT Standard and Frameworks (BTL 4).
11

Unit 11: Real-World Applications of IoT- IoT in healthcare, agriculture, smart cities, and industrial applications,Integration of IoT in existing infrastructures

 Students will be apply and evaluate IoT in Real Life Applications.(BTL 4 & 5).
12

Unit 12: Future Trends in IoT- Emerging trends in IoT: AI, machine learning, block chain integration, Challenges and opportunities in the future of IoT.

 Students will be aware of the latest trends and innovations in research (BTL 5& 6 ).

 

Textbooks:

  1. Internet of Things by Simone Cirani, Gianluigi Ferrari, Marco Picone, Luca Veltri
  2. Internet of Things Architecture and Design Principles by Raj Kamal

Reference Books:

  1. Dieter Uckelmann, Mark Harrison, Florian Michahelles, Architecting the Internet of Things
  2. ArshdeepBahga and Vijay Madisetti, Internet of Things: A Hands-on Approach

 

Course Code:  LMC0443 Course Title:Full Stack Development(4 Credits)
Course Objectives:

Ø  Gain a thorough understanding of front-end, back-end, and database technologies.

Ø  Develop proficiency in front-end technologies like HTML, CSS, and JavaScript.

Ø  Learn to work with relational databases.

Ø  Implement effective database design and perform CRUD operations.

Ø  Understanding frameworks involves recognizing their role in simplifying development, exploring their features and benefits.
BTL- Bloom’s Taxonomy Level. 1- Understand, 2- Remember, 3- Apply, 4- Analyze, 5- Evaluate, 6- Create

 

Full Stack Development Syllabus(4 Credits)

Course Contents:

S.no Unit No./ Unit description Learning Outcome
1 Unit 1-Introduction to Full Stack Development: Overview of Frontend and Backend developer, Languages for Front-end development, Languages for Back-end development, Role of Frontend Back End Development, Frontend frameworks, Backend frameworks. Students will gain a foundational understanding of research concepts (BTL 1).
2 Unit 2-HTML Fundamentals: Structure of a Web Page, HTML basics, Simple HTML document, Elements and attributes, Formatting Elements, Comments, Colors and image syntax, Advantages and disadvantages, Applications Students will learn the skills necessary to progress to more advanced web development topics.(BTL 2& 3).
3 Unit 3-CSS Fundamentals: CSS Basics, Syntax, Types of CSS: Inline, Internal and External CSS, Selectors, Properties, Box model, Website layout, Advantages and disadvantages, Applications Students will learn how to structure, and enhance web pages for further exploration.  (BTL 2 &3).
4 Unit 4- JavaScript: JS basics, Syntax, Output, Comments, Variables and datatypes, Operators, Statements, Loops, Objects, Functions, Arrays, Advantages and disadvantages, Applications. Students will learn skills to create dynamic & interactive web apps.(BTL 2 & 3).
5 Unit 5-Bootstrap: A frontend framework: Containers, Grid System, Text/Typography, Colors, Tables, Images, Alerts, Buttons, Cards, Advantages and disadvantages, Applications Students will learn a front-end framework for designing responsive and mobile-first websites (BTL 3 & 4).
6 Unit 6- Python: Indentation, Comments, Variables and data types, Numbers, Strings, Operators, List, Set, Tuples, Dictionary, Loops, Functions, Arrays, Classes, Inheritance, Polymorphism, Advantages and disadvantages, Applications Students will gain skills in back-end development (BTL 2 &3).
7 Unit 7-PHP: Introduction, Syntax, Case Sensitivity, Comments, Variables, Echo/print, Datatypes, Numbers, Math, Operators, Loops, Function, Arrays, Forms, Date and time, File handling, Advantages and disadvantages, Applications Students will understand the sever-side languages (BTL 3 & 4).
8 Unit 8- C#: Syntax, Comments, Variables, Datatypes, Math, Operators, Loops, Arrays, Methods, Classes and members, Inheritance, Polymorphism, Advantages and disadvantages, Applications. Students will develop skills(BTL 2 & 3).
9 Unit 9- Django: Backend framework-Django: Introduction, Installation, Create project, Create app, URLs, Templates, Models, Data insertion, updation and deletion. Students will learn how to use backend frameworks to build web applications (BTL 3& 4).
10 Unit 10-Express.js: Introduction, Setup, Middleware, Request and response. Routing, Templating, JavaScript in EJS. Students will learn how to use backend frameworks to build web apps BTL 3& 4).
11 Unit 11–MySQL Databases: Basic commands, Operators, Keywords, Updation and deletion, Math, Join, Union, Statements, Constraints, Keys, Functions, Advantages and disadvantages, Applications. Students will learn how to design, manage, and query MySQL databases, focusing on data storage and retrieval(BTL 3)
12 Unit 1­2-MongoDB: A NOSQL database- NOSQL databases, Datatypes,MongoDB: A NoSQL database, Data modelling, Operators (Query, Projection and Update), Aggregation pipeline operators, Aggregation Stages in MongoDBQuery modifiers. Students will learn how to store and query data using collections and documents in a NoSQL database (BTL 2& 3)
13 Unit 13-Advanced MongoDB Operations– Aggregation and geospatial commands,Query & Write Operation Commands, Query Plan Cache and Authentication Commands, User Management Commands, Role Management Commands, Replication AND Session commands, CRUD: document,  MongoDB Text Search,  Shell, MongoDB Atlas and Compass,Applications. Students will learn how to store and query data using collections and documents in a NoSQL database (BTL 2& 3)
14 Unit 14-Full stack security: Threat Modelling for Full-Stack Applications, Risks in Full Stack Application Environments, Mitigating Risks in Full Stack Application Environments, Role of Cryptography in Full Stack Security, Common Vulnerabilities and Best Practices in Full Stack Development, Database Security and Data Protection. Students will learn about security measures in both frontend and backend, focusing on authentication, authorization, and data protection (BTL 1 & 2).

Textbooks:

  1. Full Stack Web Development For Beginners by Riaz Ahmed.
  2. Bootstrap in 24 Hours, Sams Teach Yourself by Jennifer Kyrni
  3. SQL Quickstart Guide: The Simplified Beginner’s Guide to SQL by Clydebank Technology.
  4. MongoDB Basics by Peter Membrey, David Hows and EelcoPlugge.
  5. Full Stack Developer by Chris Northwood.

Reference Books:

  1. Programming basics with C# by Dr.SvetlinNakov.
  2. Programming basics with Javascript by Dr.SvetlinNakov.
  3. Programming basics with Python by Dr.SvetlinNakov.
  4. Full-Stack React Projects by Shama Hoque.