Big Data Mining

DS2401 Machine Learning for Data Science

3 hours Two requisites

DS3302 Data Visualization

3 hours One requisite

1 Week 1: Introduction to Big Data / Definition and characteristics of big data (Volume, Velocity, Variety, Veracity, Value) / Importance and applications of big data across industries / Challenges in big data processing

2 Week 2: Big Data Architecture / Overview of big data ecosystems / Hadoop Distributed File System (HDFS) architecture / YARN: Resource management in big data

3 Week 3: Data Collection and Storage / Data acquisition techniques / NoSQL databases (MongoDB, Cassandra, HBase) / Data preprocessing for big data

4 Week 4: Data Mining Foundations / Review of traditional data mining techniques / Differences between data mining and big data mining / Exploratory data analysis for big datasets

5 Week 5: Big Data Frameworks / Introduction to Apache Hadoop and Apache Spark / Comparison of batch and real-time data processing / Setting up a big data environment

6 Week 6: Big Data Analytics / Descriptive, predictive, and prescriptive analytics in big data / Data visualization for large datasets / Tools for data visualization (e.g., Tableau, Power BI)

7 Week 7: Machine Learning for Big Data / Introduction to scalable machine learning techniques / Distributed machine learning with MLlib in Spark / Examples of classification and clustering with big data

8 Week 8: Association Rule Mining in Big Data / Market Basket Analysis for big datasets / Apriori and FP-Growth algorithms in a big data context

9 Week 9: Text Mining and Natural Language Processing (NLP) / Mining unstructured text data / Sentiment analysis and topic modeling / Tools for text mining in big data (e.g., NLP libraries, Spark NLP)

10 Week 10: Graph Analytics in Big Data / Introduction to graph databases (e.g., Neo4j) / Analyzing social networks and relationships / Use cases of graph mining in big data

11 Week 11: Big Data Streaming and Real-Time Processing / Concepts of data streams and event-driven architecture / Tools for stream processing (e.g., Apache Kafka, Spark Streaming) / Real-world applications of streaming analytics

12 Week 12: Ethical and Legal Considerations / Data privacy, security, and governance in big data / Ethical issues in big data mining / Case studies of ethical challenges in big data analytics

13 Week 13: Advanced Topics and Trends / Edge computing and IoT in big data / AI integration with big data / Future directions in big data mining

14 Week 14: Project Work and Applications / Presentations of student projects on big data mining / Case studies of successful big data projects / Discussion of challenges faced and lessons learned

15 Review , Quizzes and midterm

1.1 Mapped to: K1

Explain the characteristics, challenges, and applications of big data and the tools used to process it. |