170 Seats
Basic Information
Course Description
Big Data and Analytics Market: Worldwide revenues for big data and business analytics will grow to $203 B in 2020. Enable students to explore the fundamentals of Big Data Analytics, to provide them with a base from where they can upskill themselves for specific Big Data Analytics job roles
Who
- University students enrolled in streams such as Engineering, Computer Science, Statistics, Sciences or Mathematics
- Employed professionals who wish to explore their career options and interests with regards to Big Data Analytics
- Enthusiasts curious about understanding the hype behind Big Data Analytics
- Interactive product demo artist
- Virtual production developer 3D
- VR background artist 3D VR developer
- AR/VR unity developer
- E-learning designer Modeling & texturing artist
Pre Requisites
Knowledge of the fundamentals of programming including data sequences such as stacks, queues, strings, arrays, linked lists, trees, maps and the concepts of Object-Oriented Programming .
Key Learning Outcomes:
- Evaluate trends in Big Data and discuss how Big Data is transforming businesses
- Evaluate the different platforms used for processing Big Data
- Evaluate the features of Databases
- Write Map and Reduce codes for distributed Processing of Data
- Understand key concepts behind Big Data Modelling and Management and gain practical skills needed for modelling Big Data Projects
- Select appropriate data models that suit the requirements of data
- Differentiate between a traditional Database Management System and a Big Data Management System
- Retrieve data from Big Data Management Systems
- Execute simple Big Data Integration and Processing Operations
Course Syllabus
The topics that would be covered in this section include the basic knowledge and skills that an individual must possess before being trained on the core concepts of Big Data Analytics.
1. Key Learning Outcomes
At the end of the module, the student will be able to:
- Evaluate the technological trends which have led to Big Data Analytics
- Appreciate the potential of Big Data Analytics in transforming businesses and customer use cases
- Explain business applications of Big Data including its use in analytics, marketing, retail, hospitality, consumer goods, etc.
2. Topics Covered
- Evolution of Big Data technologies
- Characteristics of Big Data and the six Vs: Volume, Variety, Velocity, Veracity, Valence and Volume
- Introduction to Data Science and how it helps in getting value out of Big Data
- Big Data use cases such as fraud prevention, security intelligence, price optimization, recommendation engines, preventive maintenance and operational efficiency
- Foundations for Big Data Systems and Programming including Distributed File Systems, scalable computing, cloud computing and cloud service models
3. Practical Activity
Identify use cases within five various industries of your choice and elaborate on how Big Data analytics can be used to transform those use cases.
The topics that would be covered in this section include the basic skills that an individual must possess to operate various Big Data systems and platforms.
1. Key Learning Outcomes
At the end of the module, the student will be able to:
- Develop foundations in data structures and algorithms that form the basis of Big Data systems
- Select and implement appropriate data structures to solve Big Data problems
- Be exposed to different platforms used for processing Big Data
- Recognize the fundamentals of databases and Big Data platforms such as Hadoop
- Write Map and Reduce codes for distributed processing of data
2. Topics Covered
- Fundamentals of databases including topics such as relational databases, tables, data types and SQL and NoSQL databases
- Features of popular Big Data systems and platforms (such as Hadoop)
- Algorithm Design using MapReduce
- Data Storage and batch processing operations
3. Tools and Technologies (Indicative List) Java, Hadoop, Apache Pig, Apache Spark & Dynamo DB
- Perform introductory Big Data tasks: Run Map and Reduce codes, perform data storage and retrieval operations and perform batch processing operations on Big Data platforms such as Hadoop
