2. WHO ARE WE?
• We are CADS, aims to make the world
more self-sustainable through
technology, insights and
intelligence.
• With that aim in mind, we educate
our clients on data management,
integration and analysis. We put
customers first, and aim for our
customers complete independence
from us and our services at the
earliest possible window. We
empower clients so they can be
robust, agile and ready to face
tomorrow’s complex, volatile world.
3. • As ASEAN’s first and only, comprehensive
Data Science training institution, we integrate
learning, networking and professional growth
• Our rigorous programme, endorsed by MDEC
and HRDF, aims to train participants who
already have the right set of skills to become
an effective data scientist, effectively
cultivating the next generation of Data
Professionals who can not only meet but
exceed the needs of a digitally disrupted
world
4. 90% of the data in the
world today has been
created in the last 2 years
The potential value from
data could be more than
USD$300 Billion
in value every year
Shortage of 1.5 million
analysts capable of analyzing
big data in the U.S. alone, by
2018
Globally, demand for data
scientists is projected to exceed
supply by more than
50% by 2018
5. Global Gender Gap Index
ASEAN Country 2014 Ranking 2015 Ranking 2016 Ranking
Brunei Darussalam 98 88 103
Cambodia 108 109 112
Indonesia 97 92 88
Lao PDR 60 52 43
Malaysia 107 111 106
Myanmar - - -
Philippines 9 7 7
Singapore 59 54 55
Thailand 61 60 71
Vietnam 76 83 65
Source: Global Gender Gap Report - World Economic Forum
- in promoting gender equality in economy, education, health, and politics
6. Job title Average Salaries for APAC
Data Scientists roles
Chief Data Scientist RM460,500 (62% increment)
Senior Data Scientist RM284,344 (50% increment)
Data Scientist RM188,652 (15% increment)
Junior Data Scientist RM164,080
Data Analysts roles
Data Analyst (Experience) RM253,750 (37% increment)
Data Analyst (Entry) RM185,600
Data Engineers roles
Senior Data Engineer RM161,100 (42% increment)
Data Engineer RM113,000
In a new field like big data analytics, promotions are based more on
qualifications and proven problem-solving rather than seniority. The
more you learn, the more qualifications & accreditations you have, the
quicker you will move up the ladder.
7. Job title Salaries in Malaysia per Annum (2018)
Data Science Management roles
Chief Technology Officer RM384,000 – RM636,000
IT Director RM288,000 – RM444,000
Project Manager RM216,000 – RM360,000
Data Science Professional roles
Senior Financial/Business Analyst RM96,000 - RM132,000
Financial/Business Analyst RM66,000 - RM108,000
Software Development roles
Chief Technology Officer RM384,000 - RM636,000
IT Director RM288,000 - RM444,000
Project Manager RM216,000 - RM360,000
In a new field like big data analytics, promotions are based more on
qualifications and proven problem-solving rather than seniority. The
more you learn, the more qualifications & accreditations you have, the
quicker you will move up the ladder.
8. Dubbed “The Sexiest Job of the 21st Century” by Harvard Business
Review and the “Best Job in America” by Glassdoor, data scientists
are in high demand as organizations desperately need professionals
who can organize the astonishing amount of data being generated
as well as prepare data for analysis.
Despite the tempting opportunities in data science, it can be
difficult to transition to data science. Fortunately, there are ways to
make the transition much easier.
So You Want To Be A Data Scientist
9. Develop any missing hard skills, which may include
statistics and analysis, machine learning techniques
and algorithms, data visualization tools and data
science toolkits.
But you’ll also need to possess excellent critical
thinking, persuasive communication, and problem-
solving skills.
As a data scientist, you must continuously develop
new skills and always be on the lookout for new
challenges and opportunities.
11. Data Scientist
Online Community
Manager
IOS Developer
Social Media Manager
Cloud Computing
Specialist
Big Data Analyst
Youtube Content Creators
Drone Operators
Driverless Car Engineer
GRAB Driver
10 JOB TITLES
THAT DIDN’T EXIST 10 YEARS AGO
12. Data Scientists are
specialists that apply
their expertise to make
predictions and answer
key business questions.
Data Engineers build
and optimize the
systems that allow data
scientists and analysts
to perform their work.
Data Analysts deliver
value by taking data,
using it to answer
questions, and
communicating the
results to help make
decisions.
13. What
Can
Big
Data
Do?
CURE DISEASE AND
PREVENT CANCER
FEED THE HUNGRY EXPLORE DISTANT PLANETS
PREDICT AND RESPOND TO
NATURAL AND MAN-MADE
DISASTERS
PREVENT CRIME
MAKE OUR EVERYDAY LIVES
EASIER AND MORE
CONVENIENT
14.
15. “We can't solve
problems by using
the same kind of
thinking we used
when we created
them.” - Albert
Einstein
The Secret to
Lifelong Success is
Lifelong Learning
Data science is booming, and with it, unprecedented opportunities for women. The field is more important than ever – with major corporations and startups alike pushing hard to leverage data to make better decisions, build better systems, and ultimately, create more profitable companies. Entrepreneurs especially are focused on using data science to disrupt existing markets and create new ones.
In the World Economic Forum Gender Gap Report in 2016, the Philippines led other ASEAN member states in promoting gender equality in economy, education, health, and politics, where the country ranked seventh among 144 countries surveyed. Other ASEAN member states ranked as follows: Lao PDR, 43rd; Singapore, 55th; Vietnam, 65th; Thailand, 71st; Indonesia, 88th; Brunei Darussalam, 103rd; Malaysia, 106th; and Cambodia, 112th.
According to Glassdoor, for three years in a row starting in 2016, data science is the highest paid field to get into.
Of course, this follows the basic laws of economics - supply and demand. The demand for data science is very high, while the supply is too low.
Think about computer science years ago. The internet was becoming a thing and people were making a lot of money on it. Everybody wanted to become a programmer, a web-designer or anything, just to be in the CS industry. Salaries were super high and it was exceptional to be there. As time passed by, the salaries got lower as the supply of CS guys (and girls) started to catch up with the demand. That said, the industry is still above average in terms of pay.
The same thing is happening to the data science industry right now. Demand is really high and supply is really low, so the salaries are still very high and people are very much willing to get into data science.
Demand:
What are some examples of data science?
Google. They are the definition of data science. Everything they do is data driven from their search engine (google.com), through their YouTube efforts, maximization of ad revenue, etc. Even their HR team is using the scientific method to evaluate strategies that make the employees feel better at work so they can be more productive. Google is not the best place to work just by chance.
Amazon. Each product recommendation that you get comes from Amazon’s sophisticated data science algorithms. Actually, Amazon has implemented an algorithm that can predict with some certainty if you are going to buy a certain product. If the probability is high enough, they move it to the storage unit closest to you so when you actually purchase it, it could be delivered the same day.
Facebook. Facebook is generating ad revenue like crazy since it has all that personal data for all its users. Since you interact with the platform, they know if you prefer cat videos or dog videos, so they know if you are a cat person or a dog person. They know what sports you are into, what food you prefer, the amount of money that you are willing to spend online. In this way, they can target their users in extraordinary ways, thus companies just love to use it as a medium.
That being said, not only huge companies have a data science division. Small businesses, blogs, local businesses,etc. use Google analytics for their needs and have seen huge gains from it. This is also a part of data science. You don’t need to be doing machine learning to monetize on data science.
Now, if your competitors are relying on data-driven decision making and you aren’t, they will surpass you and steal your market share. Therefore, you must either adapt and employ data science tools and techniques, or you will simply be forced out of business.
Supply:
Data science was driven by technology change, thus it was impossible to exist 20 years ago (slow computers, low computational power, primitive programming languages, etc.)
However, when it came about, traditional education was not ready, so there are still very, very few programs that educate aspiring data scientists. That said, there are still not enough people exploiting the opportunities in this industry. Having a low supply of labor, salaries will remain high. Thus, this is a good field to get into.
Conclusion:
Keeping in mind that the demand will continue to grow, I expect that the result would be something like the CS field - demand will grow faster than the supply for a long time.
So, yes, data science is on the rise, both from a company’s perspective and from an employee’s perspective. This makes data science a great field to get into at the moment.
In order to become a data scientist, you don’t necessarily need to pursue Bachelors in Data Science. You could certainly go for a Bachelor degree in Computer Science, Engineering, Economics, Mathematics, Statistics, Actuarial Science, Finance, or Natural Sciences (Physics, Chemistry, or Biology). Even Liberal Arts (including Social Sciences) could be very handy as well at the undergraduate level.
Start cultivating the right skills before you make a change
Typically, when we think of data scientists, we think of people who excel at crunching numbers and can handle large sets of data. But a data scientist is also someone that genuinely loves learning and helping organizations improve through data-driven decisions.
To get started, you’ll need to develop any missing hard skills, which may include statistics and analysis, machine learning, and understanding Hadoop. But you’ll also need to possess excellent critical thinking, persuasive communication, and problem-solving skills. There are many resources around the web to help you get started, as well as online courses and data science bootcamps that can help boost your skills in no time.
The important thing to remember is that, as a data scientist, you must continuously develop new skills and always be on the lookout for new challenges and opportunities. Proving that you are someone who can constantly teach yourself new skills will also come across when the time comes to interview for a data science position and send a positive signal to the company interested in hiring you that you have what it takes to succeed in a constantly evolving role.
What Is Digital Disruption?
Digital disruption is a transformation that is caused by emerging digital technologies and business models. These innovative new technologies and models can impact the value of existing products and services offered in the industry. This is why the term ‘disruption’ is used, as the emergence of these new digital products/services/businesses disrupts the current market and causes the need for re-evaluation.
An Example of Digital Disruption: Kodak Cameras Fail To Capture Future Markets
Kodak were one of the first to introduce cameras to the mainstream market. They monopolised the markets for the majority of the 20th century, but unfortunately failed to keep up with the changing identities of their customers and the changing needs and expectations that came along with them.
Digital cameras made the move from being a just piece of photographic equipment to being a much more life-friendly, fun gadget. And where as Kodak originally had their target consumer pegged as female, the male digital camera market opened up thanks to the ‘gadget’ culture. Some clever marketing from other digital technology brands led to changes in consumer perceptions and created a new ‘need’ for photographic gadgets.
This allowed brands such as Sony and Canon to swoop in and steal the hearts of the consumers with their new technologies and approaches, while Kodak stuck to their guns and fought the change for as long as they could. Despite rapidly losing market share, they refused to succumb to the inevitable force of digital disruption and in 2012 they eventually declared bankruptcy.
In 2006, Facebook was in its infancy, Twitter was being launched, and nobody had iPhones. Over ten years on, the world is a very different place, and so is the workplace.
Jobs exist now that we’d never heard of a decade ago. One estimate suggests that 65% of children entering primary school today will ultimately end up working in completely new job types that aren’t on our radar yet.
This pace of change is only going to get faster thanks to rapid advances in the fields of robotics, driverless transport, artificial intelligence, biotechnology, advanced materials and genomics, according to the World Economic Forum’s latest annual Human Capital Index.
Big data analyst/data scientist
With volumes of data growing at a rate of 40% per year, it’s no wonder that people who can analyse and process all this information are in high demand.
App developer
The iPhone arrived in 2007 and the Android shortly after, and now nearly half the world’s adults have a smartphone. This has generated a huge appetite for apps: in July 2015, Android’s Google Play and Apple’s App Store had 1.6 million and 1.5 million apps respectively. As a result, there is a booming market for app developers.
Social media manager
Back in 2006, there was no need for social media managers as most platforms had yet to be created. Today Facebook has more than 2.2 billion monthly active users worldwide and, alongside other platforms such as Twitter and Instagram, has become an indispensable marketing tool with which brands can engage with consumers. In 2018, the number of Facebook users in Malaysia is expected to reach 12.75 million, up from 11.9 million in 2017.
Uber driver
The app-based ride-hailing company was only founded in 2009, but has already grown to become the world’s most valuable start-up at $62 billion. In 2015, Uber doubled the number of active drivers on its US platform, and the company is announcing new services in cities around the globe. However, it seems that in the not-too-distant future the Uber driver may become a thing of the past – the company is eyeing up self-driving cars.
Driverless car engineer
While driverless cars look set to wipe out the roles of taxi drivers and couriers, they are also beginning to create some new jobs as well. Driverless cars won’t be able to mend themselves, so engineers, mechanics and software developers who work on vehicles will be increasingly in demand in the not-too-distant future.
What is a data engineer?
Data engineers build and optimize the systems that allow data scientists and analysts to perform their work. Every company depends on its data to be accurate and accessible to individuals who need to work with it. The data engineer ensures that any data is properly received, transformed, stored, and made acessible to other users.
What is a data analyst?
Data Analysts deliver value to their companies by taking data, using it to answer questions, and communicating the results to help make business decisions.
What is a data scientist?
A data scientist is a specialist that applies their expertise in statistics and building machine learning models to make predictions and answer key business questions.
A data scientist still needs to be able to clean, analyze, and visualize data, just like a data analyst. However, a data scientist will have more depth and expertise in these skills, and will also be able to train and optimize machine learning models.
Right now, Big Data projects are helping to:
Cure disease and prevent cancer – Data-driven medicine involves analyzing vast numbers of medical records and images for patterns which can help spot disease early and develop new medicines.
Feed the hungry – Agriculture is being revolutionized by data which can be used to maximize crop yields, minimize the amount of pollutants released into the ecosystem and optimize the use of machines and equipment
Explore distant planets – NASA analyzes millions of data points and uses them to model every eventuality to land its Rovers on the surface of Mars and plan future missions.
Predict and respond to natural and man-made disasters – Sensor data can be analyzed to predict where earthquakes are likely to strike next, and patterns of human behavior give clues which help aid organizations give relief to survivors. Big Data technology is also used to monitor and safeguard the flow of refugees away from war zones around the world.
Prevent crime – Police forces are increasingly adopting data-driven strategies based on their own intelligence and public data sets in order to deploy resources more efficiently and act as a deterrent where one is needed.
Make our everyday lives easier and more convenient – Shopping online, crowdsourcing a ride or a place to stay on holiday, choosing the best time to book flights and deciding what movie to watch next are all easier thanks to Big Data.
Since 2000 digital disruption has demolished 52% of the Fortune 500, with tech disrupting many industries such as music, publishing and retail. There are many cases already of established players who failed to ignore customer demands and reacted too slowly. Remember Blockbuster? We now have Netflix. Other examples abound:
Companies like Amazon, Volkswagen and McDonalds are all at the top of their game through fostering and leveraging innovative, even disruptive, supply chains built around strategic relationships and mutual trust
In four years, Airbnb has completely disrupted the hotel industry and today has more than 100 million users
Robotic process automation helped an international insurer cut down reporting times from 90 to 12 minutes, with 100% accuracy
Electric carmaker Tesla, which produces a fraction of vehicles compared with major US automakers, has achieved a higher market capitalisation than any — based on its prospects, not profits. It uses personalized digital marketing, as opposed to a dealer network, to drive sales.
What Is Learnability Anyway And Why Should You Care?
Learnability – or the desire and capability to develop in-demand skills to be employable for the long-term – is fueled by an individual's eagerness to learn and capacity to change based on acquiring and acting on new learnings. Some of us are born with it, some of us acquire it, and all of us are capable of it. To find success on our terms and live a rewarding life requires learnability. Without it, we risk becoming obsolete and losing our way in our fast-paced, changing world.
In the business world 4.0 the need for new skills grows as fast as the need for others decreases. According to a study by ManpowerGroup, employers believe that 65% of people born after 1995 will be employed in jobs that don’t even exist yet, and up to 45 % of people's activities can be automated. This doesn’t necessarily mean that there will be less jobs, rather there will be new jobs requiring different skills. In such a world, curiosity and learnability are required of the employee of the future to be able to adapt to new business conditions. The so-called learnability, which means the willingness and ability to learn and adapt new skills during working life, will become the key to success for both employees and entrepreneurs.
The following four steps are recommended to incorporate learnability successfully:
Look beyond the curriculum vitae: the skills that students are taught in university are not necessarily the ones they need on the job market today. Recruiters should look for candidates and employees that show enthusiasm and thirst for knowledge.
Choose carefully: the best further education options should be reserved for employees who have proven their ability to learn fast and are highly motivated.
Give it time: if you want to establish a habit of learning, you have to create a space where the mind is challenged - for example by looking at a business case from an unusual angle.
Motivate learners: you can reward employees who initiate actions to promote learnability. Such actions are, for example, inviting external speakers or organizing discussion groups. The best employees want to expand their competencies, says Swan: “Give them the opportunity to challenge themselves”.
From an individual standpoint, learnability is enhanced when you:
Find areas of interest. Go long, go broad, go differently.
Stretch yourself. Convert your coffee habit into a learning habit.
Learn that one thing you have been putting off
Find a friend/ buddy/ partner to learn with, sharing the investment and the community of learning.
Make it social. Online learning communities abound.
Reward yourself. Seek increased pay or monetary rewards if it’s a vocation as much as an exploration.
Nurture yourself through new knowledge, new skills and the accomplishment of positive change.
Don't Follow the Big Data & Data Science Craze Blindly
All the business organizations, including management consulting firms, banks & financial services, and tech companies, are indeed looking for big data talent. In fact, a recent IDC forecast shows that 2018 will see a six-time growth in the big data & analytics job market. Social media platforms are getting bombarded with blog posts and videos on data science, big data, and analytics. With a lot of hullabaloo going around, students and professionals are going crazy after data science and business analytics programs.
Since data science combines analytics with business acumen, much can be gained by targeting employees with domain expertise, in addition to technical prowess. For many organizations, the best use case for data science to add business value remains marketing and technology platforms with high activity levels.
So, do take caution and self-evaluate yourself. Don't mix enthusiasm/trends with passion. Trends keep changing. Don’t follow the data science (or big data) hype blindly. Besides, passion is not always good enough. You must possess the talent and need to be good at particular tasks. If you don’t possess a strong aptitude, quantitative background, and programming skills, Data Science & Analytics might not be your cup of tea.
