Campus Affiliate | Syllabus
Certificate in Big Data Associate (CBDA)
Certificate in Big Data Associateis designed to provide students with a well-rounded understanding of big data. It covers the potential data sources that can be used for solving real business problems and an overview of data mining and the tools used in it.
This is a fundamental course with practical, hands-on, exercises to experience using two of the most popular technologies in big data processing – Hadoop and MongoDB. Students will get the opportunity to practice installing two technologies through lab exercises. The course exposes students to real-life big data technologies with the purpose of obtaining results from real datasets, including major social media platforms.
Program Details
Durations
This program is a 3 days of intensive training class.
Method of Delivery
The program provided by Multimatics will be delivered through interactive presentation by professional instructor(s), group debriefs, individual and team exercises, behavior modelling and roleplays, one-to-one and group discussion, case studies, and projects.
Who Should Attend?
Big Data Foundation program is designed for:
- Software Engineer
- Application Developers
- IT Architects
- System Administration
- Data Analysts and Scientist
Program Objectives
At the end of the program, the participants will be able to:
- Will be equipped with fundamental big data knowledge
- To development environment containing Hadoop and MongoDB
- As a starting point in the journey into big data
Examination Details
There will be two types of examination in this program:
- Multiple choice “closed book” exam where the participants are not authorized to use anything but the exam paper and a pen.
- Essay type “open book” exam where the candidates are only authorized to use the following reference materials:
- A copy of the standard in paper hardcopy
- Programs notes from the Participant Handout
- Any personal notes made by the student during the programs
Program Modules
Big Data Fundamentals
Topic Covered
- History, overview, and characteristics
- Technologies overview
- Success stories
- Privacy and Ethnics
- Data projects