11 Feb 2019
Most companies across the world have understood that strong data analytics helps them improve the quality of decision-making drastically. As a result, data scientists are now one of the most sought-after employees. This is not just in the tech industry but even in more traditional enterprises. In fact, according to a KPMG survey, 81% of enterprises now rely on analytics to improve their understanding of customers.
Until 2-3 years back, Hong Kong was lagging way behind London, Singapore, and Silicon Valley when it came to data science positions. However, this has changed dramatically with the government’s push towards tech startups. Moreover, there is a growing demand for data scientists in sectors like supply chain, real estate, and finance. Therefore, there is a recent spurt in demand for data scientists in Hong Kong.
Data analytics is one of those areas where the quality of talent is the most important factor. The analytics department will not thrive unless a company has expert data scientists who combine technical expertise with problem-solving ability. That’s why data scientists are one of the most in-demand employees. And becoming a data scientist is one of the most sought-after tech careers.
Data scientists are “a new breed of analytical data experts who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.”
In other words, a data scientist needs to have:
The rising popularity of data scientists reflects a major shift in how organizations think about data. Companies now realize that the vast amounts of information that they have been gathering over the years can now be leveraged to gather amazing insights that can increase revenue.
Earlier, roles were restricted to statistics or basic data analysis. However, as Big Data and Big Data processing technologies like Hadoop have evolved, the role has evolved into that of a data scientist. Someone who combines high-tech with creative and problem-solving skills to generate important business outcomes.
The role of a data scientist is multi-faceted and varies from one project to another. This is part of what makes the job so interesting and challenging. Here are some of the major duties that a data scientist can be asked to do.
Being well versed with techniques like machine learning, and deep learning and being proficient in programming skills like Python, R, and SAS.
Collaborating with both business and IT teams and using technology to create solutions for business.
Being able to recognize trends in data and use them to create actionable insights that affect the companies topline or profitability.
Given that data science is such an upcoming career, it’s a great option for those who are suited for it. This section helps you understand if you have the intrinsic qualities to become a successful data scientist. If that’s the case, we will discuss all the steps you need to take to have a high-flying career in the field of data science.
This is probably the defining quality of a data scientist. 80% of a data scientist’s time is spent discovering and interpreting data. Therefore, you need the ability to stay curious and ask the right questions while going through vast amounts of data.
Data scientists straddle the fine line between IT and business. While technical skills form the foundation of data science, a successful data scientist needs to have strong business acumen. This is because you need to identify business problems, find the answers and insights in data, and present actionable solutions. If the ins and outs of business are boring to you, data science may not be the right career choice.
Data scientists need to work with different groups of people, depending on the nature of the project. From CXOs and product managers to marketing and sales teams, the IT department and even the end customer. Data scientists need to be able to collaborate with different groups of people, sometimes for the same project. Someone who doesn’t love working with teams and diverse people may have a hard time as a data scientist.
It is not enough for data scientists to just be good at understanding and interpreting data. They should also have the ability to present their insights in the form of a compelling narrative to key decision makers. They also need to communicate with different teams as they extract the information they need to generate the best insights.
A bachelor’s degree in fields like mathematics, statistics, computers, and engineering should help you set the foundation you need to become a data scientist. Many data scientists go on to do specialized Masters and Ph.D. degrees in data science. These degrees help you build significant analytical skills that can help in your data science career. However, you can always go for a strong online course or full-time immersive bootcamp in data science instead of a full-fledged degree. This is especially true if you are based in Hong Kong as there aren’t too many reputed data science degree programs here. There are several reputed data science courses in Hong Kong that will equip you with the technical and analytical skills you need.
This is the most commonly used programming language in data science. It’s a very versatile language and can be used in every stage of the data science process. From being able to create any kind of dataset to importing SQL tables with ease, Python has it all.
R is one of the best analytics tool out there for budding data scientists. 43% of data scientists use R. While Python is the aptest language for cleaning data and standardizing it, most data scientists use R to run analysis on this data.
While knowledge of the Hadoop platform is not compulsory, it is definitely a desirable skill today. A study ranked Apache Hadoop as the second most important skill needed by data scientists. Hadoop helps you convey data to different points on a system. It also helps with data filtration, data exploration, summarization, and data filtering. However, Apache Spark, which performs similar functions as Hadoop is quickly gaining popularity.
Machine learning and Artificial Intelligence consist of many skills including supervised machine learning, regression trees, neural networks, computer vision, outlier detection and so on. Of course, someone starting out as a data scientist does not need to have a strong knowledge of AI and ML techniques. However, given that there are so few AI and ML- proficient data scientists out there, this is a great way to stand out.
Of course, becoming a good data scientist can take a lifetime because the field is constantly growing and innovating. Moreover, most data scientists agree that 1-2 years of experience is needed for a respectable level of expertise in data science. In addition to basic technical skills, you also need to build domain expertise over time. However, you can master the basics in 4-8 months, depending on the skill base you possess and the time you can commit.
A fresher can become a data scientist as long as they have the inherent aptitude and a willingness to learn. If you have a bachelor’s or advanced degree in a related field like mathematics, computer science, or engineering you may find a job where the employer is willing to train you.
However, the best route to getting well-paying data science jobs in Hong Kong is to find a data scientist courses in Hong Kong that suit you and from where you can start your career. You can find both offline as well as online courses that can teach you the basics of data science. For instance, Xccelerate has both immersive as well as part-time courses in data science and machine learning. Get trained from the best minds in Hong Kong today!
If for instance, you already have a background in coding, especially in R and Python, you can choose a more advanced data science course that focusses on domain expertise and advanced data science tools. If you are a complete newbie, going for a more comprehensive course that starts with the basics is a better idea.
Hong Kong does lag behind the USA, Europe and even Asian countries like Singapore when it comes to data science jobs. However, with the explosion of Big Data, data science jobs in Hong Kong are opening up.
There is a lack of skilled data scientists in Hong Kong due to which recruiters look for employees from the US, Europe and other parts of the world. There is a huge demand-supply gap when it comes to data science jobs in Hong Kong. For there are very few people who have the right data scientist job qualifications.
Data scientists with an experience of 3-5 years can expect a monthly salary between HK$ 30000 to HK$ 70000. Most data scientists in Hong Kong today are attached to the IT department or to a particular business function like marketing. However, many companies in Hong Kong are expected to have their own data science and analytics teams in the next 5 years. The data science job outlook in Hong Kong will only become better in the coming years.
Startups have seen a huge boom in Hong Kong in the last few years with the push by the Government. Moreover, several ex-investment bankers have left high-profile jobs to start their own companies. Startups are a great place to learn and acquire data science skills as they are constantly evolving. Although the pay may not always match up to established firms, they will probably be more willing to hire talent they can train in the field of data science.
We've already covered the benefits of learning Python for Finance in a previous post, but it should be re-iterated here. Given the long history between Hong Kong and the financial industry over the years, and considering that most financial institutions in the world have a presence and staff here, its a no-brainer for staying competitive. Given the burgeoning interest and high-demand, we have specially tailored a Python for Finance part-time course.
Both investment and retail banking companies, as well as the Big 4 audit firms, are looking to hire data scientists. Due to the lack of experienced data scientists, they are actually having to retrain their CPAs as data scientists. You can get a great salary package as a data scientist in these companies. Another industry that has vast amounts of data that it can leverage to great advantage is insurance. Companies like Fidelity, AIA, and Manulife pay exceedingly well and are looking to hire data scientists.
The hospitality industry is looking at data science to understand how to optimize occupancy and tariffs. They pay well, have large quantities of data to work with, and are willing to invest in cutting-edge technology.
This depends to a large extent on your existing skill base. If for instance, you already have a background in coding, especially in R and Python, you can choose a more advanced data science course that focuses on domain expertise and advanced data science tools. If you are a complete newbie, then going for a more comprehensive course that starts with the basics is a better idea.
The course you take up depends on your availability as well. If you're still a student or looking for a job, doing a full-time course is a good idea. Xccelerate's immersive course in data science and machine learning is great for newbies. There are no prerequisites and the course is for those who want to learn data science from scratch. Of course, you need to invest 16 weeks and spend 8 hours a day, five days a week. This course opens up many careers including Junior Data Scientist, Data Analyst, Data Engineer, Machine Learning Engineer, and BI Associate.
If you're already working and can't commit to a full-time course, consider a part-time opportunity. Xccelerate has a part-time data science course for two hours a day, twice a week. It can help you find a job as a Data Professional, Junior Python Programmer, or Data Analyst. So the answer to the question "can a fresher become a data scientist" is a resounding yes. However, you need to be willing to put in the time and effort.
Hong Kong still has a long way to go before it can become an established data science center. However, the Big Data jobs have seen a huge improvement in the past few years and demand for data scientists in Hong Kong far exceeds their supply. This is a great time to invest in the skills that can lead to a very challenging yet rewarding career.
11 Feb 2019
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