{"id":29434,"date":"2025-04-14T17:50:58","date_gmt":"2025-04-14T12:20:58","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/315327-cloud-computing-for-data-science-syllabus-for-data-science-5th-sem-k-scheme-msbte-pdf\/"},"modified":"2025-04-14T17:50:58","modified_gmt":"2025-04-14T12:20:58","slug":"315327-cloud-computing-for-data-science-syllabus-for-data-science-5th-sem-k-scheme-msbte-pdf","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/315327-cloud-computing-for-data-science-syllabus-for-data-science-5th-sem-k-scheme-msbte-pdf\/","title":{"rendered":"315327: Cloud Computing for Data Science Syllabus for Data Science 5th Sem K Scheme MSBTE PDF"},"content":{"rendered":"

Cloud Computing for Data Science detailed Syllabus for Data Science (DS), K scheme PDF has been taken from the MSBTE<\/a> 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. <\/p>\n

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

Rationale<\/h4>\n

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
\"Get<\/a>.<\/p>\n

Course Outcomes:<\/h4>\n

Students will be able to achieve & demonstrate the following COs on completion of course based learning<\/p>\n

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

    Unit I<\/h4>\n

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

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Flipped Classroom Presentations\n<\/p>\n

    Unit II<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    Unit III<\/h4>\n

    Cloud Storage 3.1\tCloud 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\tKey-Value Databases: Introduction, features, limitations 3.4\tBatch data and Streaming data in Machine learning 3.5\tCloud Data Warehouse- AWS Redshift 3.6\tVarious Cloud-based tools used for data science in ML- GCP BigQuery\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Presentations Demonstration\n<\/p>\n

    Unit IV<\/h4>\n

    Cloud Computing for Data Science 4.1\tMachine Learning in the Cloud: Benefits and Limitations 4.2\tTypes of Cloud-Based Machine Learning Services: Artificial Intelligence as a Service (AIaaS), GPU as a Service (GPUaaS) 4.3\tIntroduction to various ML systems and benefits of using Managed ML platforms 4.4\tCloud Machine Learning Platforms: AWS SageMaker, Azure Machine Learning studio, Google Cloud AutoML\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Presentations Demonstration\n<\/p>\n

    Unit V<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    List of Experiments:<\/h4>\n
      \n
    1. * Use Google Services to create Doc\/Sheet\/Keep\/Forms\t2<\/li>\n
    2. *Create Virtual Machine using VMware workstation for Windows\/Linux\t2<\/li>\n
    3. Create Web server (WAMP\/XAMP\/APACHE) on Virtual Machine\t2<\/li>\n
    4. *Create an account on AWS or Azure, or Google Cloud Platform\t2<\/li>\n
    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\t2<\/li>\n
    6. Launch an EC2 instance with a specified configuration and configure Block-Based storage\t2<\/li>\n
    7. *Create and Configure File-Based storage on EC2 instance\t2<\/li>\n
    8. Create and Configure Object-Based storage on EC2 instance\t2<\/li>\n
    9. Write a script in Python to upload a dataset to an S3 bucket, list the files in the bucket, and download a file\t2<\/li>\n
    10. *Create instance of Amazon Sagemaker notebook\t2<\/li>\n
    11. * Build, test, tune, train, deploy and validate a model using Amazon Sagemaker<\/li>\n<\/ol>\n

      Self Learning<\/h4>\n<\/p>\n

      Micro Project<\/i><\/p>\n