NICF- Big Data Engineering for Analytics

Overview

This 5-day course helps data engineers focus on essential design and architecture while building a data lake and relevant processing platform. 

Participants will learn various aspects of data engineering while building resilient distributed datasets. Participants will learn to apply key practices, identify multiple data sources appraised against their business value, design the right storage, and implement proper access model(s).  Finally, participants will build a scalable data pipeline solution composed of pluggable component architecture, based on the combination of requirements in a vendor/technology agnostic manner.  Participants will familiarize themselves on working with Spark platform along with additional focus on query and streaming libraries. 

This course is part of the Analytics and Intelligent Systems series offered by NUS-ISS.
 

 
  • NICF- Big Data Engineering for Analytics
  • NICF- Big Data Engineering for Analytics

Enquiry

Please contact Ms. Elizabeth EE at tel: 65165409 or email for more details.

Facts

Class Time: 9:00am - 5:00pm

Upcoming Classes

Class 115 Oct 2018 to 19 Oct 2018 (Full Time)

Duration: 5 days

Time:
9.00am - 5.00pm

Course Details

  • Key Takeaways
  • Who Should Attend
  • What Will Be Covered
  • Fees & Funding
  • Certificate
  • Preparing for Your Course
Upon effective completion of the course, participants will be able to:
  • Understand the growth of big data and need for a scalable processing framework. Understand the fundamental characteristics, storage, analysis techniques and the relevant distributions
  •  Understand the distributed storage essentials, storage needs, and relevant architectural mechanism in processing large amounts of structured, semi-structured and unstructured data. 
  • Gain expertise with the fault-tolerant computing framework (E.g. YARN) by setting up pseudo cluster nodes or cloud based nodes for processing big data. . 
  • Construct configurable and executable tasks using the In Memory Processing frameworks (E.g. Spark Core). Understand the nuances of writing functional programs and use the core libraries to manipulate the large corpse of unstructured data residing as Resilient Distributed Datasets. 
  • Organize, store and manipulate the collected data using processing libraries. For example, using special statistical operation and stream processing data tools (E.g. Spark Special Libraries). 
  • Understand various data processing, querying and persistence (E.g. Spark QL APIs) available for usage in RDD’s context. Perform tasks such as filtering, selection and categorization. 
     
This is an intermediate course, suitable for professionals with some experience in any programming language and data design. If the participants have some business exposure, they can appreciate the case studies discussed better. 

This course targets analytics professional including:

  • Business and IT professionals seeking analytical skills to handle large amounts of unstructured data (Data lake e.g. customer feedbacks, product reviews on social media, phone call recordings, etc.) for insights to improve business process and decision-making.
  • Individuals who have no knowledge or experience in data engineering for analytics and would like to gain some practical skills in this area so that they may explore work opportunities in data engineering.
  • Data analysts and Data Engineers, who want to move from the structured to large amounts of unstructured data engineering.
Pre-requisites

This is an intensive, intermediate course. Our proposed course targets the higher value chain professionals such as data engineers, data application architects, integration architects, software engineers working on data pipeline processing and key technology decision makers.   

Participants with experience in programming languages such as Python or Java or Scala will benefit more from the course.  Participants also need to have a strong interest in building functional pipelines and be comfortable working with Hadoop platform and Spark framework. 

NUS-ISS also offers a range of other basic courses in analytics for participants new to analytics
 
 

 
 
The course objective is to explore the engineering aspects of big data storage, querying and processing techniques. The course aims to teach the students to apply the newly acquired proficiencies by developing data intensive applications using distributed compute platform (e.g. using the Hadoop platform, Spark Framework and relevant tools).

A brief module description is provided below:

Agenda

Module 1: Introduction to Data Science, Data Engineering and Big Data

Module 2: Understand Big Data from an Analytics Perspective

Module 3: Architectural Viewpoints in Big Data

Module 4: The Hadoop Ecosystem for Big Data

Module 5: Distributed File Storage

Module 6: NoSQL Databases for Big Data

Module 7: Spark and Functional Programming for Big Data

Module 8: Spark and Resilient Distributed Data Sets

Module 9: Spark QL for Big Data

Module 10: Spark and Real Time Stream Processing

Module 11: Management of Big Data initiatives

Discussion and Project Requirement Elaboration

Project and Assessment

Project Demonstration, Report Submission and Presentations. Each team will work on a practical case study and submit/present their work done regarding the assigned Big Data project.

Closing Remarks


Self-sponsored

International Participants

S'poreans and PRs 
(aged 21 and above)

SkillsFuture Mid-Career Enhanced Subsidy1 
(S’poreans aged 40 and above)

Workfare Training Support2
(S’poreans aged 35 and above, and earn ≤ $2,000 per month)

Full course fee

S$4500

S$4500

S$4500

S$4500

SSG grant

-

(S$3150)

(S$3150)

(S$3150)

Nett course fee

S$4500

S$1350

S$1350

S$1350

7% GST on nett course fee

S$315

S$94.50

S$94.50

S$94.50

Total nett course fee payable, including GST

S$4815

S$1444.50

S$1444.50

S$1444.50

Less additional funding if eligible under various schemes

-

-

(S$900)

(S$1125)

Total nett course fee payable, including GST, after additional funding from the various funding schemes

S$4815

S$1444.50

S$544.50

S$319.50


Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees, apart from government subsidies. For more information, click here.

Company-sponsored

International Participants

S'poreans and PRs 
(aged 21 and above)

SkillsFuture Mid-Career Enhanced Subsidy1 
(S’poreans aged 40 and above)

Workfare Training Support2
(S’poreans aged 35 and above, and earn ≤ $2,000 per month)

Enhanced Training Support for SMEs3

Notes

Full course fee

S$4500

S$4500

S$4500

S$4500

S$4500

SSG grant

-

(S$3150)

(S$3150)

(S$3150)

(S$3150)

Nett course fee

S$4500

S$1350

S$1350

S$1350

S$1350

7% GST on nett course fee

S$315

S$94.50

S$94.50

S$94.50

S$94.50

Total nett course fee payable, including GST

S$4815

S$1444.50

S$1444.50

S$1444.50

S$1444.50

Fee payable to NUS-ISS

Less additional funding if eligible under various schemes#
(company needs to submit training grant and claim via Skillsconnect)

-

-

(S$900)

(S$1125)

(S$900)

Total nett course fee payable, including GST, after additional funding from the various funding schemes

-

-

S$544.50

S$319.50

S$544.50

Actual financial outlay by company

Various Funding Schemes

1Mid-Career Enhanced Subsidy

  • Singaporeans aged 40 and above may enjoy subsidies up to 90% of the course fees.


2
Workfare Training Support (WTS)

  • Singaporeans aged 35 and above (13 years and above for Persons With Disabilities) and earn not more than $2,000 per month, may enjoy subsidies up to 95% of the course fees.


3
Enhanced Training Support for SMEs (ETSS)

  • SME-sponsored employees (Singaporean Citizens and PRs) may enjoy subsidies up to 90% of the course fees. For more details, click on Enhanced Training Support for SMEs.


Course attendee is eligible for only one funding scheme.

#For company-sponsored participants, companies would need to pay upfront 30% of the course fee to NUS-ISS and submit a training grant application for the remaining eligible subsidies, and subsequently a claim in Skillsconnect. For details, please refer to Skillsconnect guide 4.1 & 5.1.

This course is aligned to the National Infocomm Competency Framework (NICF) and accredited by SSG. Absentee payroll and up to 70% SSG funding of the course fee is available for eligible participants (Singapore Citizens and Permanent Residents). Absentee payroll subsidy is available for eligible companies and companies on a short work week system will receive the absentee payroll subsidy based on their employees' current income. Please visit www.ssg.gov.sg for full details.

Certificate of Completion

The ISS Certificate of Completion will be issued to participants who have attended at least 75% of the course.

WSQ Assessment

  • Participants will be assessed throughout the duration of the course.
  • Assessment Method -
  1. Project Group Assessment
  2. Individual written assessment (open-book)
  • Passing Criteria – 
  1. Project Group Assessment – Participants will be posed with a simulated real life case study project. They will be formed into groups and after the 4-day lecture, they take the project work and complete it offline and complete in a period of three weeks. Participants are to make active contribution to the group project, and achieve objectives. They will present their work for evaluation on the last contact day of the course. 
  2. Individual Assessment - Participants are to answer all questions accurately in the individual written assessment.

Upon passing the assessment, Statement of Attainment (SOAs) will be issued to certify that the participant has passed the following Competency Unit:

  • IT-BDA-501S-1 Develop Big Data Analytics Plan

Participants may need to attend additional coaching sessions and re-assessments if they do not pass the required competency units. ISS reserves the right not to disclose any information on the course assessment process.

NUS-ISS Course Registration Terms and Conditions

Find out more.

NUS-ISS and Learner’s Commitment and Responsibilities

Find out more.

WIFI Access

WIFI access will be made available. User ID and password will be provided in the participant’s kit.

Venue

Institute of Systems Science, NUS
25 Heng Mui Keng Terrace
Singapore 119615 

Click here for directions to NUS-ISS.

In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.

Course Confirmation

All classes are subject to confirmation and ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.

General Enquiry

Please feel free to write to isstraining@nus.edu.sg or call 6516 2093 if you have any enquiry or feedback.

Course Resources

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Training Roadmap(s)

Please click on the discipline(s) to view the training roadmap of related courses to assess your training needs and goals.

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