Big Data Analytics Syllabus

The following syllabus is representative. Universities and training providers use different subjects and technologies.

First-year BTech subjects

• Engineering Mathematics
• Discrete Mathematics
• Engineering Physics
• Programming in C
• Python Programming
• Basic Electrical and Electronics Engineering
• Engineering Graphics
• Communication Skills
• Environmental Studies
• Introduction to Computer Science
• Web Fundamentals
• Professional Ethics

Second-year subjects

• Data Structures
• Design and Analysis of Algorithms
• Object-Oriented Programming
• Database Management Systems
• Operating Systems
• Computer Networks
• Computer Organisation
• Probability and Statistics
• Software Engineering
• Java Programming
• Data Warehousing
• Linux Fundamentals
• Numerical Methods

These subjects provide the core Computer Science foundation necessary for scalable data systems.

Third-year subjects

• Introduction to Big Data
• Distributed Computing
• Hadoop Ecosystem
• Apache Spark
• NoSQL Databases
• Cloud Computing
• Data Mining
• Machine Learning
• Data Visualisation
• Data Engineering
• Statistical Computing
• Business Intelligence
• Data Security
• Big Data Laboratory
• Cloud Laboratory

Fourth-year subjects

• Stream Processing
• Advanced Analytics
• Scalable Machine Learning
• Natural Language Processing
• Deep Learning
• Data Governance
• Data Privacy
• Data Lake and Lakehouse Architecture
• Cloud Data Engineering
• Internet of Things Analytics
• MLOps Fundamentals
• Electives
• Internship
• Major Project

Not every institution teaches modern lakehouse or streaming technologies. Some programmes still centre heavily on older Hadoop tools. Students should inspect when the curriculum was last revised.

Important Big Data Analytics subjects

Programming

Programming is essential for data collection, transformation, analysis and automation. Common languages include:

• Python
• Java
• Scala
• SQL
• R

Python is widely used in analytics and machine learning. Java and Scala are relevant to distributed-data ecosystems. SQL remains fundamental because much organisational data is queried through relational or analytical systems.

Data structures and algorithms

Students learn how data is organised and processed efficiently. Arrays, trees, hash tables, graphs, sorting and searching remain important even when higher-level frameworks perform much of the execution.

Database Management Systems

DBMS subjects cover data modelling, relational databases, transactions, indexing, normalisation and query processing.

A student cannot understand modern data warehouses or distributed databases well without database fundamentals.

SQL

SQL is used to retrieve, transform and aggregate structured data. Students should learn:

• SELECT statements
• Joins
• Subqueries
• Aggregations
• Window functions
• Common table expressions
• Data-definition commands
• Query optimisation concepts

NoSQL databases

NoSQL refers to non-relational database approaches designed for different data models and scaling needs. Categories include:

• Document databases
• Key-value stores
• Column-family databases
• Graph databases

NoSQL is not automatically superior to SQL. The correct choice depends on data structure, consistency, access patterns and operational requirements.

Data warehousing

A data warehouse stores integrated, historical data for reporting and analysis. Students may study:

• Dimensional modelling
• Fact and dimension tables
• Star and snowflake schemas
• Extract, transform and load processes
• Online analytical processing
• Data marts
• Slowly changing dimensions

Hadoop ecosystem

Hadoop introduced widely used concepts for distributed storage and batch processing. The ecosystem has included:

• Hadoop Distributed File System
• MapReduce
• YARN
• Hive
• HBase
• Sqoop
• Flume
• Other related tools

Some older tools are less central in modern cloud environments, but understanding Hadoop can help students learn distributed-storage and processing principles.

The syllabus should not imply that learning Hadoop alone is sufficient for a modern Big Data career.

Apache Spark

Spark is a distributed processing engine used for batch processing, SQL, streaming and machine learning workflows.

Students may study:

• Resilient distributed datasets
• DataFrames
• Spark SQL
• Transformations and actions
• Partitioning
• Caching
• Structured Streaming
• Cluster execution
• Performance tuning

Tool syntax changes, but concepts such as partitions, shuffling, memory use and fault recovery remain important.

Distributed systems

Distributed systems use multiple networked computers to perform work. Students may learn:

• Scalability
• Partitioning
• Replication
• Consistency
• Fault tolerance
• Distributed storage
• Coordination
• Resource management
• Parallel processing
• Network failures

This subject helps students understand why large-scale systems behave differently from programs running on a single laptop.

Cloud Computing

Cloud platforms provide scalable computing and storage. Course content may include:

• Virtual machines
• Containers
• Object storage
• Managed databases
• Serverless computing
• Identity and access management
• Monitoring
• Cost management
• Managed analytics services

Students should understand general architecture concepts in addition to provider-specific tools.

Data lakes

A data lake stores large quantities of raw or processed data, often in object storage. It can support analytics, machine learning and archival requirements.

A poorly governed data lake can become difficult to search, trust or control. Metadata, access management, quality and lifecycle policies are essential.

Lakehouse architecture

A lakehouse aims to combine flexible data-lake storage with management features traditionally associated with warehouses. Students may study transaction layers, table formats, schema management and analytical performance.

Technology names may change, so architecture concepts should receive priority.

Data engineering

Data Engineering covers the systems that make data usable. It includes:

• Source integration
• Batch pipelines
• Streaming pipelines
• Data transformation
• Orchestration
• Storage
• Data quality
• Monitoring
• Metadata
• Reliability

ETL and ELT

ETL means extract, transform and load. ELT means extract, load and then transform within the destination platform.

The choice depends on platform capabilities, governance, data volume and analytical requirements.

Stream processing

Stream processing handles continuously arriving data. Students may learn:

• Events
• Topics
• Producers and consumers
• Message brokers
• Windows
• Event time
• Processing time
• Late data
• State
• Delivery guarantees

Apache Kafka and Spark Structured Streaming may be introduced, although alternatives exist.

Statistics

Statistical knowledge supports reliable analysis. Common topics include:

• Descriptive statistics
• Probability
• Sampling
• Distributions
• Estimation
• Hypothesis testing
• Correlation
• Regression
• Experimental design
• Time-series analysis

Large datasets do not eliminate the need for sound statistical reasoning. A biased dataset can produce precisely calculated but misleading results.

Machine Learning

Machine Learning enables systems to learn patterns from data. A Big Data Analytics course may cover:

• Regression
• Classification
• Clustering
• Feature engineering
• Model evaluation
• Recommendation systems
• Dimensionality reduction
• Scalable learning

Students should understand that data preparation, validation and monitoring are often more time-consuming than model selection.

Data visualisation

Visualisation communicates patterns and findings. Tools may include:

• Tableau
• Power BI
• Python libraries
• R visualisation libraries
• Web-based dashboards

Good visualisation requires clear questions, appropriate charts and honest representation.

Business Intelligence

BI turns organisational data into reports, dashboards and performance indicators. Students learn how data models and metrics support business decisions.

Data governance

Governance establishes responsibility and rules for data. It may include:

• Ownership
• Quality
• Access
• Privacy
• Metadata
• Retention
• Classification
• Lineage
• Compliance

Data privacy and security

Students should learn:

• Access control
• Encryption concepts
• Data masking
• Anonymisation
• Secure pipelines
• Audit logs
• Privacy principles
• Responsible data use

Legal and regulatory requirements change and must be verified for specific applications.

Data quality

Quality dimensions can include accuracy, completeness, consistency, timeliness, validity and uniqueness. Data pipelines should detect and report quality problems.

Data modelling

Data models define how information is organised. Students may learn relational, dimensional, document, graph and event-oriented modelling.

Data orchestration

Orchestration tools schedule and coordinate pipeline tasks. Students should learn dependency management, retries, monitoring and failure handling rather than only a particular interface.

MLOps and DataOps

MLOps supports the deployment, monitoring and maintenance of machine-learning systems. DataOps applies automation and quality practices to data workflows.

These are often advanced or elective topics.

Big Data Analytics laboratories

A relevant programme may include:

• Programming laboratory
• Database laboratory
• Linux laboratory
• Big Data laboratory
• Cloud Computing laboratory
• Machine Learning laboratory
• Data visualisation laboratory
• Web and API laboratory
• Data Engineering laboratory
• Statistical Computing laboratory

Because many systems are cloud-based, students should ask whether the college provides practical accounts, credits or cluster access.

Elective subjects

Possible electives include:

• Natural Language Processing
• Deep Learning
• Graph Analytics
• Time-Series Analytics
• Financial Analytics
• Healthcare Analytics
• Social-Media Analytics
• Geospatial Analytics
• Internet of Things
• Cloud Security
• Data Privacy
• Information Retrieval
• Search Engines
• Recommendation Systems
• Advanced Databases
• Distributed Machine Learning
• Data Product Management
• Responsible AI
• MLOps

Project ideas

Students may develop:

• Real-time fraud detection
• E-commerce recommendation system
• Log-analysis platform
• Social-media sentiment pipeline
• Smart-city sensor dashboard
• Predictive-maintenance system
• Healthcare data platform using synthetic data
• Financial-risk analysis
• Traffic-flow analytics
• Energy-consumption forecasting
• Retail-demand prediction
• Customer-churn analysis
• Network-intrusion analytics
• News-processing pipeline
• Product-review analysis
• Streaming stock-market dashboard
• Data-quality monitoring system
• Cloud data warehouse
• Lakehouse prototype
• Distributed document-processing system
• Graph-based recommendation engine
• Supply-chain analytics
• Educational-performance analysis
• Weather-data pipeline
• Data-lineage tracker

Projects involving personal, medical or financial data should use properly authorised, anonymised or synthetic datasets.