MSBTE K Scheme – 315327 Cloud Computing for Data Science
MSBTE K Scheme – Cloud
Computing for Data Science Notes PDF
The subject “Cloud
Computing for Data Science” under the MSBTE K Scheme for Computer
Engineering diploma is an advanced and highly industry-relevant subject. It
connects two powerful domains:
- Cloud Computing – On-demand access to
computing power, storage, and services over the internet.
- Data Science – Extracting meaningful
insights from large datasets using statistical, mathematical, and
computational techniques.
When combined, Cloud Computing
and Data Science enable students to handle Big Data, Machine Learning,
and AI applications more efficiently. This subject helps learners
understand how massive data is processed and stored in the cloud, making
it an essential skill for modern IT industries.
Why Study Cloud Computing for Data Science in MSBTE K
Scheme?
- Scalability – Cloud provides elastic
resources to process large datasets without hardware limitations.
- Cost-Efficiency – Students learn
pay-as-you-go models instead of expensive local infrastructure.
- Data Science Integration – Cloud offers
ready-to-use tools for machine learning, analytics, and visualization.
- Industry Demand – Skills in AWS, Google
Cloud, Microsoft Azure are in high demand for data science jobs.
- Future-Proof Career – Cloud + Data Science
is the backbone of AI, IoT, Blockchain, and Big Data Analytics.
MSBTE K Scheme Syllabus Overview – Cloud Computing for
Data Science
The syllabus is designed to teach
both theoretical foundations and practical applications.
Unit 1: Introduction to Cloud
Computing
- Definition, characteristics, and evolution of cloud
computing
- Cloud service models: IaaS, PaaS, SaaS
- Deployment models: Public, Private, Hybrid,
Community Cloud
- Advantages & limitations of cloud computing
Unit 2: Cloud Infrastructure
for Data Science
- Virtualization concepts: virtual machines,
containers, hypervisors
- Distributed computing and cluster-based processing
- Cloud storage types: object storage, block storage,
file storage
- Cloud networking basics
Unit 3: Data Science
Fundamentals on Cloud
- Role of cloud in data science lifecycle
- Data collection, cleaning, and preprocessing using
cloud platforms
- Cloud-based databases (NoSQL, SQL on cloud)
- Importance of cloud scalability for large datasets
Unit 4: Big Data and Cloud Platforms
- Introduction to Big Data (Volume, Velocity,
Variety, Veracity, Value)
- Hadoop ecosystem and Spark for cloud-based
analytics
- Cloud-based tools for Big Data: AWS EMR, Google
BigQuery, Azure HDInsight
- Data pipelines on cloud
Unit 5: Machine Learning and AI
on Cloud
- Machine Learning as a Service (MLaaS)
- Cloud-based ML tools: AWS SageMaker, Google AI
Platform, Azure ML Studio
- Model training and deployment on cloud
- Real-life use cases: recommendation systems, fraud
detection, predictive analytics
Unit 6: Security, Ethics, and
Future Trends
- Cloud security challenges and solutions
(encryption, access control, compliance)
- Ethical concerns in cloud-based data science
(privacy, bias, transparency)
- Future trends: Serverless computing, Edge AI,
Cloud-native data science applications
Practical Work in MSBTE Subject
Students practice real-world
scenarios using Python and Cloud Platforms:
- Setting up virtual machines and storage in cloud
environment
- Uploading, storing, and retrieving datasets on
cloud storage
- Performing analytics using Google Colab, AWS
SageMaker, or Azure ML
- Implementing Big Data processing pipelines
- Training and deploying ML models on cloud
infrastructure
Importance of Cloud Computing for Data Science
- For Exams – Direct questions on cloud
models, cloud-based analytics, and ML services.
- For Career – Almost every Data Scientist
today uses cloud platforms to store and process data.
- For Projects – IoT, AI, and big data
projects are impossible without cloud resources.
- For Future Learning – Acts as a stepping
stone for Deep Learning, AI, and Cloud Security specialization.
Real-Life Applications
- Healthcare – Cloud-based predictive
analytics for disease detection.
- Finance – Fraud detection using ML on cloud.
- E-commerce – Recommendation systems powered
by cloud ML tools.
- Social Media – Analyzing user behavior with
cloud-based Big Data.
- Smart Cities – IoT data storage and
analytics in the cloud.
Study Tips for MSBTE Students
- Understand differences between service models
(IaaS, PaaS, SaaS) and deployment models.
- Learn cloud-based tools like Google Colab
(free and student-friendly).
- Revise Big Data and Machine Learning concepts
with cloud examples.
- Practice diagrams of cloud architecture and
data pipelines.
- Solve previous years’ MSBTE question papers.
Career Opportunities
Students who master this subject
can pursue roles such as:
- Cloud Data Scientist
- Cloud Engineer
- Big Data Analyst
- AI/ML Engineer
- Cloud Solutions Architect
- Business Intelligence Analyst
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