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Home / Syllabus / B.Sc. (Computer Science) / TY BSc Computer Science / Semester 6 / Computer Science Practical 6

B.Sc. (Computer Science) Computer Science Practical 6 Syllabus - Mumbai University

This is the TY BSc Computer Science syllabus under NEP 2020, in force from the academic year 2026-27. The University still sets the earlier Choice Based papers alongside it — her Summer 2026 third-year timetables name that scheme — so check which scheme your exam form names before you revise.

Computer Science Practical 6 Syllabus.pdf
Major · Semester 6 · TY BSc Computer Science · 2 credits · 50 marks

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Syllabus for Computer Science Practical 6

Major · Semester 6 · TY BSc Computer Science · 2 credits · 50 marks

Module I

  • Practical based on Data Science
  • Introduction to Excel
  • Perform conditional formatting on a dataset using various criteria.
  • Create a pivot table to analyze and summarize data.
  • Use VLOOKUP function to retrieve information from a different worksheet or table.
  • Perform what-if analysis using Goal Seek to determine input values for desired output.
  • Data Frames and Basic Data Pre-processing
  • Read data from CSV and JSON files into a data frame.
  • Perform basic data pre-processing tasks such as handling missing values and outliers.
  • Manipulate and transform data using functions like filtering, sorting, and grouping.
  • Feature Scaling and Dummification
  • Apply feature-scaling techniques like standardization and normalization to numerical features.
  • Perform feature dummification to convert categorical variables into numerical representations.
  • Hypothesis Testing
  • Formulate null and alternative hypotheses for a given problem.
  • Conduct a hypothesis test using appropriate statistical tests (e.g., t-test, chi- square test).
  • Interpret the results and draw conclusions based on the test outcomes.
  • ANOVA (Analysis of Variance)
  • Perform one-way ANOVA to compare means across multiple groups.
  • Conduct post-hoc tests to identify significant differences between group means.
  • Regression and Its Types
  • Implement simple linear regression using a dataset.
  • Explore and interpret the regression model coefficients and goodness-of-fit measures.
  • Extend the analysis to multiple linear regression and assess the impact of additional predictors.
  • Logistic Regression and Decision Tree
  • Build a logistic regression model to predict a binary outcome.
  • Evaluate the model’s performance using classification metrics (e.g., accuracy, precision, recall).
  • Construct a decision tree model and interpret the decision rules for classification.
  • K-Means Clustering
  • Apply the K-Means algorithm to group similar data points into clusters.
  • Determine the optimal number of clusters using elbow method or silhouette analysis.
  • Visualize the clustering results and analyze the cluster characteristics.
  • Principal Component Analysis (PCA)
  • Perform PCA on a dataset to reduce dimensionality.
  • Evaluate the explained variance and select the appropriate number of principal components.
  • Visualize the data in the reduced-dimensional space.
  • Data Visualization and Storytelling
  • Create meaningful visualizations using data visualization tools
  • Combine multiple visualizations to tell a compelling data story.
  • Present the findings and insights in a clear and concise manner.

Module II

  • Practical based on Cloud Computing
  • Web Service + Cross-Platform Client Calls
  • Cross-Platform Currency Conversion Service (INR to USD) with Java and .NET Clients: Design and deploy a simple web service that accepts an amount in Indian Rupees (INR) and returns the equivalent value in US Dollars (USD) using a configurable exchange rate. Implement two client applications—one in Java and one in .NET—that invoke the service, pass parameters, receive the response, and display the converted value. Include input validation, basic error handling, and logs of requests/responses.
  • SOAP Web Service Development and Deployment of a Simple SOAP-Based Web Service: Create a SOAP web service exposing at least two operations (e.g., convertCurrency(), getServiceStatus()), publish the WSDL, and test it using a SOAP client (SoapUI or programmatic client). Demonstrate request/response XML structure, handling invalid parameters, and service endpoint accessibility.
  • REST Web Service
  • Design and Implementation of a Simple RESTful Service with CRUD APIs: Develop a REST service that exposes APIs using HTTP methods (GET/POST/PUT/DELETE) for a small resource (e.g., Student, Course, or Task). Ensure responses are in JSON, implement basic validations, HTTP status codes, and test using Postman/cURL. Document endpoints and sample payloads.
  • Consuming Google Search / Google Maps REST API:
  • Integration of External REST API: Google Maps-Based Place Search and Geolocation Viewer: Build a client application that consumes Google Maps REST APIs (e.g., Geocoding / Places) to accept a user query (place/address) and display extracted results such as latitude, longitude, formatted address, and optionally show it on a map UI. Handle API key configuration, HTTP request building, JSON parsing, rate-limit/error responses, and present output in a user-friendly format.
  • KVM Virtualization Setup
  • Installation and Configuration of KVM for Virtual Machine Provisioning: Install and configure KVM virtualization on a Linux host system. Verify hardware virtualization support, configure required packages (KVM, QEMU, libvirt, virt-manager), and create at least one VM with defined CPU/RAM/disk. Demonstrate VM lifecycle operations: start/stop/snapshot, and document networking mode (NAT/bridge) used.
  • Image/Video Upload-Download Using MTOM
  • Binary Data Transfer using MTOM: Upload and Download of Multimedia Files: Implement a SOAP-based file transfer service using MTOM (Message Transmission Optimization Mechanism) to upload and download image/video files between client and server. Ensure efficient binary transfer (not base64-heavy payloads), validate file size/type, store files server-side, and verify successful retrieval with integrity (file opens correctly).
  • FOSS-Cloud VSI Functionality for IaaS + Storage
  • Implementing FOSS-Cloud IaaS: Virtual Server Infrastructure (VSI) with Storage Provisioning: Create a mini IaaS-style setup using FOSS cloud tools where users can provision a virtual server instance with selected CPU/RAM/disk and attach storage (block/object as supported by the chosen tool). Demonstrate instance creation, storage creation, attachment/mounting, and basic monitoring/usage reporting.
  • FOSS-Cloud VSI Functionality for PaaS
  • Implementing FOSS-Cloud PaaS: Deploying a Web Application on a Platform Service: Use an open-source PaaS (or PaaS-like layer) to deploy a sample application (e.g., simple web app/API). The platform should manage runtime, deployment, scaling configuration (manual/auto if possible), and service bindings (database/cache optional). Demonstrate deploy, update (version change), view logs, and service health.
  • AWS Flow Framework Workflow Application
  • AWS Flow Framework: Hello-World Workflow with Activity and Worker Programs: Develop a workflow-based application using AWS Flow Framework where a workflow triggers an activity that prints “Hello World” to the console. Implement
  • workflow and activity interfaces (contracts), their implementations, workflow coordination logic, and worker programs to host and execute them. Demonstrate workflow execution and tracking of workflow/activity invocation.
  • OpenStack Setup: User Network + Private Network Creation
  • OpenStack Networking Practical: Creating Tenant User Network and Private Network Topology: Set up (or use an existing) OpenStack environment and create a tenant configuration including user/tenant, private network, subnet, and optionally a router connected to an external network. Launch an instance in the private network, assign IP addressing, validate connectivity (ping/SSH if applicable), and document the network topology created.

Text Books

  • 1 Hands-On Programming with R, Garrett Grolemund,1st Edition, 2014
  • 2 Mastering Machine Learning with R, Cory Lesmeister, PACKT Publication,2015
  • 3 Java Web Services Up and Running 2nd edition, Martin Kalin, O’Reilly (2013)
  • 4 OpenStack Operations Guide, Tom Fifield, Diane Fleming, Anne Gentle, Lorin Hochstein, Jonathan Proulx, Everett Toews, and Joe Topjian, O’Reilly Media, Inc., 2014
  • 1 Introduction to Machine Learning with Python, Andreas C. Muller, Sarah Guido, Oreilly, First Edition
  • 2 Mastering Cloud Computing, Rajkumar Buyya, Christian Vecchiola, S Thamarai Selvi, Tata McGraw Hill Education Private Limited, 2013
  • 3 OpenStack in Action, V. K. CODY BUMGARDNER, Manning Publications Co, 2016

Reproduced from the University of Mumbai syllabus for B.Sc. (Computer Science) under NEP 2020, in force from the academic year 2026-27. Wording is as printed in that syllabus. Module numbering is as printed there too.

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