Data and Event Prediction

Someone emailed me asking about the results of flood data and the prediction of the disaster event after reading my published proceeding. He was asking me either that prediction formula can be utilized for his work in calculating the flood event in his country particularly around some specific location in the Africa continent. I was skeptical to answer not knowing of his nature of work and academic background. However, I did found out that he was doing some research for his advanced degree and he has a qualification in some sort of Satellite Engineering. Interesting!

I have advised him to do the following things:

  1. Collect data of hydrology/ water/ flood/ drainage from the agency or authority that manages the flood disaster management. (Or related samplings to the scope of your study).
  2. Collect data from global sources such as NASA rainfall, cloud movements, and other equivalent international data portals.
  3. Collect data from any other department or agency that may have direct or indirect roles in flood events such as meteorology, geospatial mapping, welfare, etc.

Because he was also interested in the scope of study of Big Data, so I have told him that by obtaining those data, it was indeed had contributed to the abundance of his data sets.

Days later, he replied to my email telling me that he had got the data as what I have suggested. Then he repeated his question, “Can I use the prediction formula like you have suggested in the paper this time?”

Nope. He still didn’t get the gist of big data and event prediction method that I have suggested in my paper.

The truth is, when you got your data sets and you wanted to make a prediction on the probability of the event to occur, it is advisable for you to follow the statistical hypothesis and the steps of deducing or inducing your statement(s) before coming to conclusion on either an event is feasible to return and recur. The prediction should be per case matter.

big data and method of predictions.png

By the generic steps, I mean you should undergo the process of data collection and the method of finding the conclusion out of the available data by means of proving on either your objectives are working or not. This is as illustrated in the above diagram: Big data and event prediction via hypothesis testing. This is one of the many approaches that you can use when you are dealing with your big data, you got your problems and you have the objectives of the project that you are aiming. But you were unsure of the direction that you should navigate from this point.

From the above diagram, it is clearly shown that you cannot avoid the tedious steps of research methods which include the following listing:

  1. Identify your research objectives,
  2. State your problem statements,
  3. Identify your hypotheses statements, which eventually will lead you to identify your research problem and decide on the structure of your research design,
  4. Draw your scope of research,
  5. Do data collection, which eventually you need to process on the behavior and properties of the data by using the approach of statistical analysis and hypothesis testing. At the end of this process, if the data is tested normal, you could formulate the prediction formula for the event to occur, and lastly
  6. Make the conclusion out of the above-mentioned steps.

When identifying the hypotheses statements, it is wise if you could: (1) write the list of contributing factors of your thematic problem, (2) the major problem to study and (3) the consequences that might repeatedly incur if the problem is not solved.

Simultaneously, during the process of statistical analysis and hypothesis testing, it is of advisable if you could test its normality so that the data and the problem that you are dealing with are feasible and that you could conclude on either to accept or to reject the null hypothesis that you have drawn. Consequently, whichever the hypothesis that you have accepted, the data should also be feasible to be tested upon its correlation and regression. This is to determine that the strengths and weaknesses of the relationship for your chosen parameters and domain are within the scope of study and formulating the prediction equation would be much significant.

The approach is in fact works for both the social sciences and physical engineering. Though you might come up with different kind of approaches of manipulating the big data to formulate the event prediction, be it by using the machine learning, artificial intelligent, or any other semantic of sciences, nothing beat to the preciseness of testing the normality of the event by distribution curve and its statistical properties and behavior.


Navidi, William Cyrus. Statistics for engineers and scientists. Vol. 2. New York: McGraw-Hill, 2006.

Sekaran, Uma, and Roger Bougie. Research methods for business: A skill building approach. John Wiley & Sons, 2016.

The Differences of Certificate, Diploma, Bachelor, Master and Doctor of Philosophy in Engineering

Basically, in learning experience I like to refer its growth in terms of the knowledge, skills and attitude (KSA) that were gained during the process. The three parameters are qualitative in nature and normally the measurement would involve some pre-defined scales or ratios.

As a matter of fact, I came to conclusion that the certification of a person after studying and experiencing the different levels of academic and skills qualifications would achieve some amount of skills and knowledge as the following diagram would suggest:


Generally, the knowledge should be proportionate with the skills. The more skills obtained, the more knowledge is gained. However, as a formal education might suggest, sometimes this skills wouldn’t be developed sufficiently when the degree-certification orientation is highlighted. Hence suggesting that people who are learning for the level of Certificate would be exposed to more skills-content-based learning modules in comparison to the level of a Doctor of Philosophy which enhanced more on the knowledge behind the gist of the study instead of the skills.

In addition, I believe that ideally, attitude should be in exponent to the skills and knowledge. The more skilful a person is the more knowledge he should sought. Though practically this is hard to find in our society. The best attitude is when there is a balanced amount of knowledge and skills in a person and perhaps can be found from a person who had experienced the learning concepts from the certificate level to the doctorate degree.

As a reference, the above diagram is adapted from the diagram of Malaysian Occupational and Skills Qualification Framework (MOSQF) that was introduced by the Department of Skills Development, Ministry of Human Resources Malaysia as the following figure is showing:

source: National Occupational Skills Standards (NOSS), Department of Skills Development (JPK), Ministry of Human Resources Malaysia.

Hour of Code and Computer Coding for Kids Campaign 2017

It started last year (2016) when I thought I wanted to set up a trial on an hour of coding for my friends’ kids and neighbours. This is when I discovered that is organising  “Hour Of Code” campaign since 4 years ago. I did the “Hour of Code” sessions in small scale for my friends’ kids in the area of Kota Bharu, Kelantan, Malaysia and Putrajaya, Malaysia. Simultaneously, I did shared the news on the social media that has left people wondering, what was I doing with such an activity. What is coding? They have wondered.

This year, out of requests, I have conducted three more sessions of Computer Coding for Kids which were conducted in the month of December in conjunction of the global “Hour of Code” campaign. The Computer Coding for Kids that I have conducted this year are as the following details:

  • December 5th, 2017 in Kota Bharu, Kelantan, Malaysia.
  • December 8th, 2017 in Putrajaya, Malaysia.
  • December 28th, 2017 in Johor Bahru, Johor, Malaysia.

I have received tremendous responses and positive feedbacks on the interests that parents have shown on sending their children to these sessions. I was enjoying myself too meeting these enthusiastic coders and computer savvy children. As a matter of fact, I have divided the children into 2 main categories, i.e. Category of 6-9 years old and Category of 10-13 years old. However, coding has no age limit and there were a number of children that were not bounded to these age categories that I have accepted for the sessions.

As a result, the children from the Category of 6-9 years old had followed the modules and activities from selected section code of the and web portals. Where as, the children from the Category of 10-13 years old had followed the modules of Basic Scratch programming with the task given to them was to set a game named “FishBall” (Vorderman, 2015). The children had successfully completed the task and to my surprise, it was done in less than 2 hours.

Screen Shot 2017-12-30 at 7.25.34 PM

The following lists are the the links of the games from the children of the 3 sessions and can be run on most web application platform by using desktop computers (it does not work on mobile platforms). Pick one and try to play it. I am pleased to learn that they have grabbed the gists of the lesson and had enjoyed their coding sessions:

  1. Fishball game by Fazz204:
  2. Fishball game by Invoker27:
  3. Fishball game by FoxGamerPlays:
  4. Fishball game by Aimaan651:
  5. Fishball game by alyanadhirah_:
  6. Fishball game by CaptTurtle:
  7. Fishball game by imanezz:
  8. Fishball game by MLPRONOOOB:
  9. Fishball game by TAFFY20:
  10. Fishball game by hirinsgame:
  11. Fishball game by YT43:
  12. Fishball game by wawa235:
  13. Fishball game by muazpro9407:


  1. Vorderman, C. (2014). Computer Coding for Kids. Dorling Kindersley Ltd., UK.
  2. Vorderman, C. (2015). Coding With Scratch Made Easy. Dorling Kindersley Ltd., UK.

Can I Get Boys Who Code?

You have probably heard about this encouraging curriculum of getting the girls as young as a ‘kindy’ to be involved with the Science, Technology, Engineering and Mathematics or better known as STEM. It is a worldwide educational strategy towards instilling the young girls to appreciate the technical contents of their learning experience and simultaneously to close the gap between the two genders.

Recently, there is another exciting movement for the girls that should be given further incentives which is known as “Girls Who Code“. This movement was originally initiated by Reshma Saujani and currently there are a number of books have been published and about to be published under this motion.


Quite exciting to learn about this. But, it’s irony enough that one day I had someone asking me this instead, “Don’t they have the books on Boys Who Code too?” Which have left me perplexed to answer. The question was not wrong. But there is no exact answer too to satisfy the question. Yup. It’s true that in the field of STEM, girls need more boost than their male counterparts. But, how convinced are we that the boys need less in this case?

With recent number of psychological studies on the learning difficulties among the millennials, challenged by the emergence of intelligent gadgets that becoming parts of our life; perhaps some people saw that both gender types of students need help and encouragement in their learning journey. When a person asked me on why they don’t have the books for the boys on how to code, I was just giving an easy answer out of that, “Normally, the boys got their natural interest in the studies of science and technology. Maybe that’s why we saw them less priority to create this kind of movement. Boys can automatically appreciate the nature contents of the science and technology subjects.” On contrary, I was not sure myself on why do we see them less important in this exercise.

Of course, I am in full support for the Girls Who Code. I even hope that I can practice this in Malaysia too. And even after this movement has started, the implementation on its activities might be promising to the communities of the developed nations, but the Third Countries are still wary of its importance. The traditions and cultural values among these countries are still substantial to them. It’s hard for them to embrace the paradigm shifts.

I have heard one of the teachers who is involved with the STEM educational committees in Malaysia saying that “… It’s really a hard work to practice this kind of curriculum in our country. So much work to be done, yet the pupils are not performing well to the level that they are expecting…” Yes, of course. I can relate that. And the teacher never mentioned it was either the boys or the girls who were having the troubles. Her emphasis was like both genders need help in STEM and not necessarily the girls.

On the other hand, I am really looking forward to the release to all of the 13 book series of the Girls Who Code. It is indeed an exciting movement that I took it here to pen it. Though the movement might be a jargon to the girls in this country, I hope one day the gradual change is for good and both genders will benefit from this kind of movement. Otherwise, the boys can still read the books on Girls Who Code. No limit to that.

ASEAN Qualification Framework: A Comparison

ASEAN is an Association of South East Asia Nations. It is made up of 11 countries namely, (1) Brunei 🇧🇳 , (2)  Cambodia 🇰🇭 , (3) Indonesia 🇮🇩 , (4) Laos 🇱🇦 , (5) Malaysia 🇲🇾 , (6) Myanmar 🇲🇲 , (7) Philippines 🇵🇭 , (8) Singapore 🇸🇬 , (9) Thailand 🇹🇭 , (10) Timor-Leste 🇹🇱 , and (11) Vietnam 🇻🇳 . Most of the times, these countries are regarded as the Third Countries or better known as Developing Countries.

These countries strive hard to move forward towards building developed nations. One of their most important agenda include improving the area of human capital development. Skillful workforce is one of their priorities towards ensuring the nations to achieve better future for the next generations.

For that matter, each ASEAN country has her own qualification framework as a pivot point for their human capital development. This is especially for preparing the people with the latest technology and temporary social progress.

Hence, the following tables and figures are illustrating on the summary of the qualification contents for each of the ASEAN countries.

1) Brunei 🇧🇳 

Table 1. Brunei Darussalam Qualifications Framework (BDQF).

2) Cambodia 🇰🇭 

Table 2. The Cambodia Qualification Framework (CQF).

3) Indonesia 🇮🇩 

Figure 1. The Indonesian Qualification Framework (IQF).

4) Laos 🇱🇦 

Table 3. Asian Development Bank Support to Technical and Vocational Education and Training Project in Laos.

5) Malaysia 🇲🇾 

Figure 2. Sector-wise breakdown of the Malaysia Qualification Framework (MQF).

6) Myanmar 🇲🇲 

Table 4. Descriptors for certificate levels under the Myanmar National Qualification Framework.

7) Philippines 🇵🇭 

Figure 3. The Philippines Qualification Framework.

8) Singapore 🇸🇬 

Figure 4. The Singapore Workforce Skills Qualifications (WSQ) Framework.

9) Thailand 🇹🇭 

Table 5. Levels within the National Qualification Framework of Thailand.

10) Vietnam 🇻🇳 

Table 6. Breakdown of Vietnam National Occupation Skills Levels.

(Credit to ASEAN Triangle Project. Report was prepared by Mr. David Lythe and ILO team. The document is downloadable at—asia/—ro-bangkok/—sro-bangkok/documents/publication/wcms_310231.pdf )

Other reference:

ANSSR: Enhancing the Quality and Relevance of Technical and Vocational Education and Training (TVET) for Current and Future Industry Needs-Phase 1 at

Ten Best Infographics of Big Data – Descriptive, Predictive and Prescriptive Methods

For the past three (3) years of doing a research on Big Data, it was intriguing to find that there is no exact definition for this technology and there is no limit to its innovations. Therefore, I am gathering among the best infographics on describing what is the Big Data Analytics on this post of which I have found them useful to represent the values of this technology and its adaptation to the Information World.


The following ten (10) infographics were prepared to deliberate on the methodologies of big data analytics found by major inventors and innovators for the next design and development of machine languages and technologies which were proven practical in our industrial world. The infographics were created either by Data Science companies, scholars, engineers and scientists.


The original resource locations of the graphics were included in the following listing.

  1. Big Data from Descriptive to Prescriptive by Gartner Analytics


2. The Evolution of Big Data Analytics by AYATA

AYATA info graphic v10 no registration mark.jpg

3. Analytics Approaches Abound by Intel


4. Emerging Trends in Data Analytics by Business Analytics Management (BAM!)


5. Big Data Analytics by Jars Services


6. The Analytics Value Chain by Andrew Stein


7. Big  Data and Analytics, Other Perspectives by Andrew Stein


8. Big Data Science for CODATA by Semantic Community


9. Big Data Analytics from Descriptive to Preemptive Analytics by Kirk Borne on Twitter Media


10. From Data Analytics to Deep Learning by ADATAO


Credits to all the webpages and hyperlinks that I have referred to on this post. May you find this information as useful too.