Big Data Analytics

Course details

With the surge in data generation, traditional data processing applications are inadequate. This course introduces students to big data concepts, tools, and analytics methodologies. They'll work with tools like Hadoop, Spark, and more, performing analytics on vast datasets.

Start Date
TBD
Target Audience
5th & 7th semester undergraduates. Ideal for students eyeing roles in big data analytics in sectors like banking, e-commerce, and telecommunication.
Duration
14-15 weeks
Learning Objectives
  • Comprehend the principles of big data and its challenges.
  • Gain expertise in big data platforms and ecosystems.
  • Conduct analytics on big data using modern tools and techniques.
  • Address real-world business challenges using big data insights.
Key Features
  • Hands-on exercises on big data platforms.
  • Case studies from sectors like banking, retail, and logistics.
  • Real-world big data project work.
Learning path
  • Unit 1: Introduction to Big Data (4)
  • Unit 2: Data Storage and Retrieval (6)
  • Unit 3: Data Processing with Spark (4)
  • Unit 4: Big Data Visualization (4)
  • Unit 5: Advanced Big Data Technologies (4)
  • Unit 6: Real-world Applications (6)

Topics covered

  • Understanding Big Data: Volume, Velocity, Variety
  • Challenges and Opportunities in Big Data
  • Big Data vs. Traditional Data
  • Big Data Infrastructure and Ecosystem

  • Introduction to Hadoop and its Ecosystem
  • Hadoop Distributed File System (HDFS)
  • Basics of MapReduce
  • Introduction to Data Lakes
  • Columnar Databases and NoSQL
  • Data Retrieval Mechanisms

  • Basics of Spark and Resilient Distributed Dataset (RDD)
  • Spark Streaming for Real-time Analysis
  • DataFrames and SparkSQL
  • MLlib for Machine Learning on Big Data

  • Challenges in Visualizing Big Data
  • Tools for Big Data Visualization
  • Real-time Dashboards
  • Advanced Visualization Techniques

  • Introduction to Flink and Kafka
  • Graph Processing with GraphX
  • Big Data in Cloud: AWS, Azure, GCP
  • Data Warehousing Solutions: Redshift, BigQuery

  • Big Data in Finance
  • Big Data in Healthcare
  • Social Media Analytics
  • Big Data in Retail and E-commerce
  • Internet of Things (IoT) Data Analysis
  • Ethical Implications and Data Security

Grading:

  • Class Participation: 10%
  • Bi-weekly Assignments: 30%
  • Group Project: 20%
  • Mid-Term Examination: 20%
  • Final Examination: 20%

Reading Material: Comprehensive textbooks on Big Data technologies, paired with cutting-edge research articles. Additional resources available via the LMS.

Assignments: A combination of theoretical challenges and practical Big Data tasks.

Faculty: A seasoned academic with industry experience in Big Data solutions and applications.

Teaching Instructors: TBD

Contact Us

E-mail

arun.reddy@globuslearn.com

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