Course Proposition and Executive Summary
The need for the World to make sense of big data has never been more pressing. According to computer Giant International Business Machines (IBM), it was reported that 2.5 billion gigabytes of data was being generated on a daily basis in 2012 alone, where amongst the volume of these data generated, 75% of these comes in unstructured form of videos, voice and textual information. Still, data researchers tell us that 90% of the World’s data currently was said to be generated in the last 2 years. Never in history have we witness such a rise in terms of the creation of data and it can only get bigger -- exponentially.
The US bureau of Labor recently released a report projecting a 22% increase in demand for professionals in the analytics arena with Harvard Business Review projecting yet another increase in demand of professionals who are able to make sense of these data running into the hundreds of thousands in the next decade or so. The World is short of data professionals who could not just make sense of these veracious amounts of data, but also able to draw insights and apply them in decision-making whether is it in the public service of public safety, education or health services, or the private sector in achieving tangible business impacts like increases in return on investments (ROI).
This course therefore aims to equip individuals with the ability to make sense of both structured and unstructured data. Using analytical methodologies and frameworks, participants will learn not just about important concepts in big data analytics but will also get a chance to apply what they have learnt in the classroom through a practicum where they will work on live data in using big data analytics in addressing pressing business issues. Conducted interactively with case studies and real data, participants who successfully complete the course will take away with them the skills and knowledge to running a full cycle of a big data project.
Key Credentials of Trainer:
• B.Sc (Statistics and Applied Probability), National University of Singapore (NUS)
• M.Sc (Knowledge Management), Nanyang Technological University (NTU)
• IBM Business Analytics Certified Specialist in IBM SPSS Modeler (Professional) and IBM SPSS Statistics
• SAS Certified Predictive Modeler using SAS Enterprise Miner
• SAS Certified Business Analyst using SAS 9: Regression and Modeling
• Certified Associate in Project Management (CAPM)
• Advanced Certificate in Training and Assessment (ACTA)
• Associate Adult Educator, Adult Educators’ Professionalisation, Institute of Adult Learning (IAL)
At the end of this course, Participants will be able to:
1. Describe the rise of big data and big data technology and the role predictive models play in terms of big data development
2. Design and propose the use of relevant analytical methodologies to formulate a big data solution in addressing organizational challenges
3. Design, create, evaluate and assess the strengths and weaknesses of various big data modelling techniques
4. Plan, execute and implement big data analytics solution
5. Assess and evaluate analytical model performance in addressing business objective(s)
6. Select champion models to draw conclusion in answering business challenges
7. Describe best practices of big data analytics and analytical pitfalls to avoid
1. Introduction to Big Data Analytics: The rise of big data and analytical tools
2. Data warehousing: Databases and its role in Big Data Analytics
3. Programming and data manipulation in Big Data Analytics
4. Frameworks and methodologies of Big Data Analytics
5. Introduction to statistics and its role in Big Data Analytics
6. Applications of Big Data Analytics:
- Predictive Analytics
- Segmentation Modeling
- Association Rule Mining
7. Introduction to unstructured data analysis: Sentiment analysis
8. Champion model comparison and evaluation
9. Big Data Analytics project management and implementation
10. Case studies and applications of Big Data Analytics
11. Practicum: Using live data to implement a Big Data Analytics project
This course is specially designed for data analysts, researchers and executives who are interested to learn more about big data analytics and analytics project management skills. This 4-day workshop conducted over 4 consecutive weekends or 4 consecutive weekdays (please kindly check the course schedule for more information), comes with a practicum aimed to equip attendees with real world data analytics exposure and therefore also caters to those who may want to learn about applications and to gain hands-on experiences in the big data analytics arena.
Participants who may not have the relevant training will find this course useful and enriching, while those who may have some training or working experience in this area will find this course offering fresh insights into the big data analytics space.
Participants should ideally possessed a tertiary qualification and be generally comfortable with quantitative discussions, applications and working with models and algorithms.
1. Productivity & Innovation Credit (PIC)
Companies sponsoring their staff for this course may claim for Productivity & Innovation Credit (PIC) Funding Scheme under IRAS. PIC Funding is applicable to only company sponsored applicants.
Please visit the web link given below for more information on the PIC Scheme.
2. UTAP (Union Training Assistance Programme)
NTUC union members enjoy 50% (unfunded) course fee support for up to $250 each year when you enrol for courses supported under UTAP (Union Training Assistance Programme). Conditions apply.
For more information, please call NTUC Membership hotline at 6213 8008 or email [email protected] Website: http://skillsupgrade.ntuc.org.sg
3. SkillsFuture Credit (SFC)
With effect from January 2016, Singaporeans aged 25 years and above who received their SkillsFuture Credit account activation letter will be eligible for an initial credit of $500 which can be used to pay for course fees for a range of eligible skills-related courses. The credits can be used on top of existing course fee subsidies/funding.
This is only applicable for self-sponsored applicants. Application via SkillsFuture Portal can only be made starting from 30 days before the course commencement date.
Minimum Entry Requirements
Participants should ideally possessed a tertiary qualification and be generally comfortable with programming, quantitative discussions, applications and working with models and algorithms.
Mode of Assessment
Participants are required to participate in a big data practicum of which details will be made known to the participant. Practicum is an assignment given to participants to work on live data applications where participants will be graded in accordance to the course objectives to ensure they are met in a practical way.
Certificate of Performance Awarded by the Singapore Polytechnic, subjected to fulfilment of the course requirements in terms of practicum assessment.
1. All applications must be made via Online Registration at www.pace.sp.edu.sg
Course fees can be paid by the following payment modes:
Credit Cards, Internet Banking, NETS (Not Applicable for company sponsored)
a) For e-payment using Visa/Master cards and Internet Banking, please click on the ‘Make e-Payment’ button on the acknowledgement page to proceed.
b) For NETS payment, you can pay at:
Blk T1A, Level 1
Mon-Fri: 8:30am to 7:30pm
c) For cheque payment:
Please make cheques payable to “Singapore Polytechnic”. Please cross the cheque and write the Registration Reference ID, Applicant Name and NRIC/FIN number on the back of the cheque. Mail the cheque to:
500 Dover Road
Blk T1A, Level 1
Please note that an administrative charge of $15 will be imposed for any returned cheques from the bank or financial institution.
Cash (Not Applicable for company sponsored)
d) For cash payment, you can pay at:
Admin Building, Level 2
Mon-Fri: 8:30am to 5:00pm
2. All successful applicants will be notified with a letter of confirmation via email.
3. Withdrawal and Deferment
Withdrawal and deferment notice must be made in writing to the Professional & Adult Continuing Education (PACE) Academy via email to [email protected] For withdrawal cases, the portion of course fee to be refunded is based on the date of notice as follows:
• 2 weeks before the commencement of the course - Full refund
• Less than 2 weeks before commencement of the course - 70% refund
• On or after date of commencement - No refund
The Singapore Polytechnic reserves the right to cancel or postpone any of the courses. Applicants will be duly notified and where applicable, the full fees will be refunded.
The Singapore Polytechnic also reserves the right to amend the fees charged or the period and duration of the courses.
The data provided to Singapore Polytechnic will be kept strictly confidential and will be used for the purpose of course administration. The data may be passed on to the relevant organisations that require the information related to the course.