MSBTE K Scheme – 315326 Data Analytics
MSBTE K Scheme – Data
Analytics Notes PDF
The MSBTE K
Scheme Data Analytics subject is an advanced course introduced in the final
year of the Computer Engineering diploma. This subject plays a key role in
preparing students for the modern IT industry, where data is
considered the new oil.
Data Analytics
helps students understand how raw data is collected, cleaned, processed, and
transformed into meaningful insights that guide business decisions,
innovation, and problem-solving.
With the rise
of Big Data, Artificial Intelligence, and Machine Learning, Data
Analytics has become one of the most important skills for computer engineers.
The MSBTE K Scheme ensures students not only learn theory but also perform practical
experiments using real-world data analysis techniques.
What is Data Analytics?
- Data Analytics refers to the systematic process
of examining data sets to identify useful patterns, trends, and
information.
- It involves techniques like data cleaning,
statistical analysis, visualization, and interpretation.
- In simple words, Data Analytics converts raw
data into knowledge, which helps organizations in decision-making,
forecasting, and strategy building.
Example: Companies like Amazon
analyze customer buying patterns to recommend products, while Netflix
uses data analytics to suggest movies and series.
Importance of Data Analytics in MSBTE K Scheme
- Industry Demand: Every IT company, from
startups to MNCs, uses data analytics for business growth.
- Practical Application: Students gain
exposure to tools like Excel, Python, R, and visualization platforms.
- Career Growth: Opens doors to roles like Data
Analyst, Business Analyst, and Data Scientist.
- Integration with Modern Tech: Closely linked
with AI, Cloud Computing, IoT, and Cybersecurity.
- Exam & Practical Relevance: Questions
test conceptual knowledge as well as applied understanding.
MSBTE K Scheme – Data Analytics Syllabus (Overview)
The syllabus is designed to make
students job-ready by teaching fundamentals, tools, and real-life
applications of analytics. Major topics include:
1. Introduction to Data
Analytics
- Basics of data and information
- Types of data: structured, semi-structured,
unstructured
- Difference between Data, Information, and
Knowledge
- Importance of analytics in business and technology
2. Data Collection and
Preprocessing
- Sources of data: sensors, logs, databases, web,
social media
- Data cleaning: removing duplicates, missing values,
outliers
- Data integration and transformation
- Normalization and data reduction techniques
3. Types of Data Analytics
- Descriptive Analytics – What happened?
(reports, dashboards)
- Diagnostic Analytics – Why did it happen?
(root cause analysis)
- Predictive Analytics – What will happen?
(machine learning models)
- Prescriptive Analytics – What should we do?
(decision-making strategies)
4. Tools and Technologies for
Data Analytics
- Excel & SQL – Basic data analysis and
queries
- Python & R – Advanced programming for
data analytics
- Tableau / Power BI – Visualization and
dashboards
- Hadoop & Spark – Big Data analytics
5. Data Visualization
- Importance of visual representation of data
- Charts, graphs, histograms, scatter plots
- Dashboards for decision-making
6. Big Data and Analytics
- Characteristics of Big Data (Volume, Variety,
Velocity, Veracity)
- Big Data architecture
- Hadoop ecosystem basics (HDFS, MapReduce)
- Cloud-based data analytics platforms
7. Case Studies and
Applications
- Data analytics in e-commerce (Amazon, Flipkart)
- Data analytics in entertainment (Netflix, Spotify)
- Data analytics in healthcare (disease prediction,
patient monitoring)
- Data analytics in finance (fraud detection, stock
predictions)
8. Data Security and Ethics
- Challenges in data privacy
- Data security threats and solutions
- Ethical use of analytics
- Legal frameworks like GDPR
Practical Work in Data Analytics (MSBTE K Scheme)
Students perform hands-on
activities such as:
- Cleaning and preprocessing a dataset
- Writing SQL queries for data analysis
- Performing basic analytics in Python (Pandas,
NumPy)
- Creating charts and graphs using Excel or Python
- Building dashboards using Tableau or Power BI
- Preparing reports from case studies
Importance in MSBTE Exams and Career
In Exams
- Definition and concept-based questions
(types of analytics, Big Data features).
- Case study questions (applying analytics in
different sectors).
- Short notes (data preprocessing,
visualization, tools).
- Diagram-based questions (data analytics
process, Big Data architecture).
In Career
- Data Analyst – Collect, process, and analyze
data.
- Business Analyst – Use analytics to improve
business processes.
- Data Scientist – Apply machine learning for
predictive analytics.
- Big Data Engineer – Work with large-scale
distributed data systems.
- Visualization Specialist – Build dashboards
and reports.
Study Tips for MSBTE Students
- Understand the four types of analytics with
real-life examples.
- Practice data cleaning and visualization tasks
regularly.
- Revise Big Data concepts (Hadoop, Spark)
with simple diagrams.
- Learn basic Python libraries (Pandas,
Matplotlib, Seaborn) for analytics.
- Study case studies to connect theory with
practical use.
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