GICT Certified Big Data Science Analyst (CBDSA) Course

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Certification in
AI/ML, Big Data, Cloud Computing & IoT

Up to 90% funding for SMEs and professionals (conditions apply)
Special course fee for Tertiary Student/NSF

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GICT Certified Big Data Science Analyst course (CBDSA) helps individuals to understand the complete Big Data Technologies stack from Data Storage, Data Processing, Data Visualization to Data Analytics.

Big Data Science Analyst course
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few.”
-IBM

Business Analytics is the emerging and fastest growing technology which every organization is embracing. As per Gartner’s prediction by 2014, 30 percent of analytic applications will use proactive, predictive and forecasting capabilities, and the software market for business intelligence, analytics and corporate performance management grew by 13.4% in 2010 to $10.5 billion and would continue to grow. On the other hand, modern day businesses accumulate an astonishing amount of digital data, which can be leveraged to unlock new sources of economic value or to provide fresh insights into business trends.

Big Data Analytics delivers competitive advantage in two ways compared to the traditional analytical model. First, Big Data Analytics describes the efficient use of a simple model applied to volumes of data that would be too large for the traditional analytical environment. Research suggests that a simple algorithm with a large volume of data is more accurate than a sophisticated algorithm with little data. The algorithm is not the competitive advantage; the ability to apply it to huge amounts of data—without compromising performance—generates the competitive edge. Second, Big Data Analytics refers to the sophistication of the model itself. Increasingly, analysis algorithms are provided directly by database management system (DBMS) vendors. To pull away from the pack, companies must go well beyond what is provided and innovate by using newer, more sophisticated statistical analysis.

What to expect at our Big Data Science Analyst course?

This course covers the concept of Business Analytics and Big Data technologies with its strategic importance to any organization. Participants will be introduced to the concept of business analytics with big data technologies: Hadoop, Hive and HBase. The course deals with basic principles, concepts, and techniques/tools used for big data and business analytics, which includes data mining, Hadoop, HDFS & MapReduce, Apache HBase and Apache Hive. Also, this course covers different types of business analytics with real life use cases including association rule mining and regression models. Participants will get good picture of all these concepts and how they all are interconnected to each other in organizational context.

Course Outline
  • Unit 1: Introduction to Business Analytics
  • Unit 2: Data/Information Architecture for Business Analytics
  • Unit 3: Data Mining Tool
  • Unit 4: Data Mining Techniques
  • Unit 5: Introduction to Big Data
  • Unit 6: Introduction to Hadoop
  • Unit 7: Hadoop HDFS & MapReduce
  • Unit 8: Apache HBase
  • Unit 9: Apache Hive

Tools/Software used: Hadoop, HBase, Hive, RapidMiner

Course Outcome
  • Acquire knowledge of complete Big Data Technologies stack from Data Storage, Data Processing, Data Visualisation to Data Analytics
  • Learn necessary skills to manage and analyse big data
  • Implement key predictive modelling Algorithms on RapidMiner Tool
  • Perform Exploratory data analysis and data pre-processing techniques
  • Identify the right tool for solving real life big data problems
  • Gain hands on experience in using Big Data Technologies Like Hadoop, Map Reduce, HBase and Hive
Duration
  • 40 Hours (5 Days) Classroom Training
Pre-Requisites
  • Participants are recommended to have preferably 1 year of experience in software development with Java/Unix/Linux environment and good understanding on data/business analytics. However, if you do not have any experiences, you can still consider taking up the course and we will advise / assist you accordingly.
Funding

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