Big Data Specialization Course Syllabus

Overview: This specialization provides a beginner-friendly introduction to big data concepts, tools, and techniques, designed to equip learners with practical skills for managing and analyzing large-scale datasets. The course spans approximately 70 hours across six modules, combining hands-on labs, real-world projects, and foundational knowledge in big data technologies including Hadoop, Spark, Pig, Hive, and NoSQL databases. Learners will gain experience in data modeling, management, analysis, and predictive modeling, culminating in a capstone project in partnership with Splunk that applies all acquired skills to realistic big data scenarios.

Module 1: Introduction to Big Data

Estimated time: 18 hours

• Understand the Big Data landscape and key concepts (Volume, Velocity, Variety, Veracity, Valence, Value)
• Learn Hadoop architecture, HDFS, YARN, and MapReduce programming
• Hands-on exercises to install and run Hadoop programs
• Explore use cases and business applications of big data

Module 2: Big Data Modeling and Management Systems

Estimated time: 14 hours

• Learn data collection, storage, and organization for big data
• Hands-on experience with management tools and data infrastructure
• Explore evolving platforms for large-scale data management
• Understand schema design and data integration challenges

Module 3: Big Data Analysis with Spark

Estimated time: 12 hours

• Introduction to Apache Spark and its ecosystem
• Perform exploratory data analysis using Spark
• Implement data transformations and distributed processing
• Compare Spark with MapReduce for large-scale data processing

Module 4: NoSQL Databases

Estimated time: 10 hours

• Understand types and use cases of NoSQL databases
• Work with key-value, document, columnar, and graph databases
• Design and query NoSQL databases for scalability
• Integrate NoSQL with big data processing frameworks

Module 5: Data Mining and Applied Machine Learning

Estimated time: 15 hours

• Apply statistical analysis and regression techniques
• Build predictive models using real-world datasets
• Explore data mining methods for pattern discovery
• Introduction to graph analytics for problem modeling

Module 6: Final Project

Estimated time: 20 hours

• Capstone project in partnership with Splunk
• Design and execute a big data analysis pipeline
• Apply tools and techniques from all modules to a real-world scenario

Prerequisites

• Familiarity with basic programming concepts (e.g., Python or Java)
• Basic understanding of databases and data structures
• Willingness to install software and set up virtual machines

What You'll Be Able to Do After

• Understand how big data is organized, analyzed, and interpreted to drive business decisions
• Gain hands-on experience with Hadoop, Spark, Pig, Hive, and NoSQL databases
• Design data integration, management, and pipeline systems for large datasets
• Apply statistical analysis, regression, and predictive modeling to real-world problems
• Complete a capstone project demonstrating end-to-end big data analysis skills