315327: Cloud Computing for Data Science Syllabus for Data Science 5th Sem K Scheme MSBTE PDF

Cloud Computing for Data Science detailed Syllabus for Data Science (DS), K scheme PDF has been taken from the MSBTE official website and presented for the diploma students. For Subject Code, Subject Name, Lectures, Tutorial, Practical/Drawing, Credits, Theory (Max & Min) Marks, Practical (Max & Min) Marks, Total Marks, and other information, do visit full semester subjects post given below.

For all other MSBTE Data Science 5th Sem K Scheme Syllabus PDF, do visit MSBTE Data Science 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for cloud computing for data science is as follows.

Rationale

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Course Outcomes:

Students will be able to achieve & demonstrate the following COs on completion of course based learning

1. Use Cloud-Based software services by comprehending the cloud Computing Architecture.
2. Configure Virtual Machines using Virtualization techniques.
3. Implement Virtualized storage system in Cloud.
4. Use Machine Learning algorithms in Cloud Environment.
5. Deploy Machine Learning Models on Cloud.

Unit I

Introduction to Cloud Computing 1.1 Introduction to Cloud Computing: Definition, Evolution of Cloud computing (from Mainframes to Clouds), Service – Oriented Architecture, Web Services, Grid Computing, Utility Computing 1.2 Characteristics of a Cloud computing 1.3 Cloud computing architecture: Basic components: front-end platform, back-end, platform, Networking, cloudbased delivery 1.4 Cloud Service Models: Software as a Service (SaaS), Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Continuous delivery using PaaS

Suggested Learning Pedagogie
Lecture Using Chalk-Board Flipped Classroom Presentations

Unit II

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Unit III

Cloud Storage 3.1 Cloud Storage: Introduction, Benefits of using Cloud Storage, Use cases of Cloud Storage (Backup, Archiving, Disaster recovery, Data processing, Content delivery) 3.2 Cloud storage system: Block-Based, File-Based, Object-Based Storages 3.3 Key-Value Databases: Introduction, features, limitations 3.4 Batch data and Streaming data in Machine learning 3.5 Cloud Data Warehouse- AWS Redshift 3.6 Various Cloud-based tools used for data science in ML- GCP BigQuery

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Demonstration

Unit IV

Cloud Computing for Data Science 4.1 Machine Learning in the Cloud: Benefits and Limitations 4.2 Types of Cloud-Based Machine Learning Services: Artificial Intelligence as a Service (AIaaS), GPU as a Service (GPUaaS) 4.3 Introduction to various ML systems and benefits of using Managed ML platforms 4.4 Cloud Machine Learning Platforms: AWS SageMaker, Azure Machine Learning studio, Google Cloud AutoML

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Demonstration

Unit V

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

List of Experiments:

1. * Use Google Services to create Doc/Sheet/Keep/Forms 2
2. *Create Virtual Machine using VMware workstation for Windows/Linux 2
3. Create Web server (WAMP/XAMP/APACHE) on Virtual Machine 2
4. *Create an account on AWS or Azure, or Google Cloud Platform 2
5. Create an account on Google Cloud Platform. a) Create a project and access BigQuery. b) Query data directly from Google Sheets. c) Create tables and views using BigQuery 2
6. Launch an EC2 instance with a specified configuration and configure Block-Based storage 2
7. *Create and Configure File-Based storage on EC2 instance 2
8. Create and Configure Object-Based storage on EC2 instance 2
9. Write a script in Python to upload a dataset to an S3 bucket, list the files in the bucket, and download a file 2
10. *Create instance of Amazon Sagemaker notebook 2
11. * Build, test, tune, train, deploy and validate a model using Amazon Sagemaker

Self Learning

Micro Project

• Use AWS Glue, Google Dataflow, or Azure Data Factory to create a data pipeline that ingests, transforms, and loads data from one storage service to another. Document the pipeline configuration and execution results.
• Create a Machine on AWS and make basic services available on machine like word, power point etc.
• Set up a relational database using AWS RDS, Azure SQL Database, or Google Cloud SQL. Create a database schema, insert sample data, and perform queries using SQL. Document the steps and results.
• Launch an EC2 instance with a specified configuration (e.g., type, region). Install software (e.g., Python, Jupyter Notebook) on the instance. Connect to the instance and run a basic Python script.
• Create a local cloud on Virtual Machine on VmWare workstation. Deploy a web application and make it accessible by URL.
• Write and deploy a simple AWS Lambda function or Azure Function that processes data (e.g., transforms a JSON file). Test the function with sample data and set up triggers for automatic execution.

Laboratory Equipment

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Learning Materials

1. Dr. Anand Nayyar Handbook of Cloud Computing BPB Publication First Edition (1 January 2019) ISBN-10:9388176669 ISBN-13:978-9388176668
2. Toby Velte, Anthony Velte, Robert C Cloud Computing: A Practical Approach By Toby Velte, Anthony Velte, Robert C McGraw Hill Professional ISBN-978-007-162965-8
3. Noah Gift, Alfredo Deza Cloud Computing for Data Analysis Pragmatic AI Labs (No ISBN)
4. Valliappa Lakshmanan Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines O’Reilly Media, Inc. ISBN: 9781098118952
5. Abhishek Mishra Machine Learning in the AWS Cloud: Add Intelligence to Applications with Amazon SageMaker Wiley Publication ISBN: 978-1-11955671-8

Learning Websites

1. https://www.geeksforgeeks.org/virtualization-cloud computin g-types/ – Introduction to virtualization and cloud Computing and its types
2. https://www.javatpoint.com/virtualization-in-cloud-computing Introduction to virtualization and cloud Computing
3. https://www.hostitsmart.com/blog/types-of-virtualization-in- cloud-computing-complete-overview/ Overview of cloud computing, types of Virtualization advantages and Application
4. https://aws.amazon.com/what-is/virtualization/ How can AWS help with virtualization and cloud computing?
5. https://www.run.ai/guides/machine-learning-in-the-cloud Machine Learning in the Cloud,AWS Sagemaker Service for Machine Learning

For detail Syllabus of all other subjects of Data Science, K scheme do visit Data Science 5th Sem Syllabus for K scheme.

For all Data Science results, visit MSBTE Data Science all semester results direct links.