2021 Career Guide: Data Science & Machine Learning in Hong Kong

Thomas Ho

30 Jan 2021

Exploring Data Science & Machine Learning as a career in Hong Kong

The world is digital. You and everyone around you lives and breathes data, creating terabytes of information every second. What happens to all this data? Does it have any future value, any hidden answers that we’re not able to see in the physical world?

If you have an aching curiosity for such answers, then exploring Data Science might be the field for you.

Data Science: What’s it all about?

Simply put, Data Science is a special interdisciplinary field that deals with deriving actionable insights from vast amounts of structured and unstructured data. These insights are meant to empower businesses to take better actions to increase sales.

Now that’s a business perspective. But for you, Data Science is all about making meaning from raw data, seeing future trends and patterns, and devising solutions accordingly.

Data is the new gold. Every industry that wishes to thrive in the coming decades will need to rely on data. It’s not just important for staying competitive, but also for businesses to retain customers and maintain engagement.

Businesses need to learn what customers are inclined towards, what are the new emerging trends and what products/services are going to be the most in-demand. As per Market Research Future survey, the global data analytics market size will reach USD 132,903.8 million at a 28.9% CAGR by 2026.

Big Data in City

The Data Science boom in Hong Kong

As compared to the world's leading tech cities like London, Singapore and Silicon Valley, Hong Kong was lagging way behind in data science positions. With the government’s push towards tech startups, this changed drastically and created an enormous demand for data scientists in sectors like supply chain, real estate, hospitality, and finance.

Data analytics is one of those areas where the quality of talent is a critical factor. The analytics or business intelligence department of a company will not thrive unless it has expert data scientists with the perfect combination of technical expertise & problem-solving ability.

The future lies in amassing, understanding and evaluating data. With more and more industries leveraging data and tech to lead the market, data science is definitely future-proof. No wonder it is being hailed as the sexiest job profile of the 21st Century.

Since 2017, data scientists and analysts have become one of the top 10 most in-demand tech professionals in Hong Kong, as per The School of Data Science at City University in Hong Kong.

Read Also: Top 10 Uses of Python in Business

How to explore Data Science as a career option?

Data Science is indeed a vast field. But more importantly, it's a very new field that is constantly evolving and growing. Becoming a master in this field is quite impossible at this point. But finding the right career path is quite possible.

Tackling the confusion with Data Science terms

Your curiosity must have compelled you to research a lot on what data scientists do. Do you feel overwhelmed by reading big words like Data Mining, Data Warehousing, Machine Learning, and Data Analysis, which make sense to you as individual fields but can’t show you the whole picture in regards to data science? Don't worry, you’re not alone! Let’s get this confusion sorted out.

Realising your inner potential

Forget all those big terms for now. Just focus on your personal traits and inclinations, and let’s see what kind of a career path you can take in data science. You must have seen various types of job roles and postings across the internet with fancy names. But at the crux of it, the career offerings in data science boil down to 3 main categories. Let’s explore them and see what kind of people usually excel in each category.

3 main job categories in data science

1. Analyst:

  • People who love analysing large volumes of data, visualising raw data in more comprehensible formats (charts, graphs etc.)
  • People who like statistics and love interpreting data patterns and trends.
  • People who love finding answers to pressing questions via diligent studies and fact-finding research.

2. Engineer:

  • People who love coding and making software
  • People who can think algorithmically and create programs for sorting raw data into structured data sets.

3. Scientist:

  • People who have the capacity to act as both engineer and analyst
  • People who like thinking creatively to find solutions based on the answers received from an analyst.
  • People who have strong business acumen and can make tough decisions.

using data at work

Read Also: The Top 7 Myths About Being a Data Scientist

Job trends of data science in Hong Kong

This must give you a basic perspective of how to switch to a career in data science. Let’s take a look at some job trends for the above-mentioned categories, specific to Hong Kong. According to PayScale:

  • The average annual salary for an entry-level data scientist is HKD 319,950

  • The average annual salary for an mid-level data scientist is HKD 390,000

  • The average annual salary for an senior data scientist is HKD 520,000

  • The average annual salary for an entry-level data analyst is HKD 222,000

  • The average annual salary for an mid-level data analyst is HKD 258,000

  • The average annual salary for an senior data analyst is HKD 374,000

As per Glassdoor:

  • The average annual salary for an entry-level data engineer is HKD 192,000

  • The average annual salary for a mid-level data engineer is HKD 276,000

  • The average annual salary for a senior data engineer is HKD 420,000

A career in Data Science: What it takes?

To clearly understand what makes you eligible for a career in data science, let’s take a look at what makes the foundation of data science.

The 4 Pillars of Data Science

  1. Mathematics: Mathematical knowledge is the foremost requirement, as data science is purely based on statistics and probability.

  2. Computer Science: Working with data means using computers all day. Computer programming and proficiency with coding languages are critical requirements for any specialty field within data science.

  3. Business: At the end, all data is used to make big business decisions. Having a strong business acumen is a must.

  4. Communication: In data science, you work as a team, and you excel as a team. Having strong communication skills is the only way you can succeed in this field.

Keeping these pillars in mind, let’s explore what kind of education you must have to be eligible for a career in data science.

Preferred Degree Courses

  • Bachelor's degree in engineering

  • Masters degree in science

  • Graduate in mathematics or statistics

  • Bachelor's degree in computer sciences

These are some of the courses one can take and proceed with an advanced course in data science. But in reality, you can have any background and still get a shot at a data science career by doing a coding bootcamp like full-time data science & machine learning bootcamp. It’s all about having the right skills and landing the right role.

But before we go any deeper, let’s clear up the confusion with those big terms. Let’s see what each term means in regards to data science.

Read Also: Is a Data Science Bootcamp worth it to get a Data Science Job

Understanding various data science terms

Data Science is an umbrella term for all the below-listed terms.

  • Statistics: It is the base of all Data Mining and Machine learning algorithms. It’s all about concepts like sample data, population, and hypothesis.

  • Data mining: Large sets of data collected from databases, data warehouses or complex datasets like time series or spatial, are mined to take out interesting correlations, trends and patterns among the data items.

  • Data analytics: People may be confused with the difference between Data Science and Data Analytics. Data analytics involves analyzing data using statistics and/or programming to find interesting correlations, trends and patterns and discover useful insights.

  • Machine learning: A technique which develops complex algorithms for processing large data and delivers results to its users using a computer or through artificial intelligence. The goal of machine learning is to understand data and build models that can be understood and used by humans.

  • Big Data: The processing of huge data sets is often not feasible or achievable due to physical or computational constraints. Special techniques & tools (e.g., software, algorithms, parallel programming, etc.) are therefore required. Big Data is the term that is used to encompass these large data sets, specialised techniques, and customised tools.

Which skills are the most attractive?

Programming skills along with knowledge of statistics and problem-solving skills, make up the arsenal of an eligible data scientist. Some of the in-demand and trending data scientist skills are:

  • Programming Languages: R/Python/Java

  • Data Modelling

  • Statistics and Applied Mathematics

  • Working Knowledge of Hadoop and Spark

  • Databases: SQL and NoSQL

  • Machine Learning and Neural Networks

  • Proficiency in Deep Learning Frameworks: TensorFlow, Keras and Pytorch

data science team

Data Engineer vs Data Analyst vs Data Scientist

This section shall give you the answer to one of the most aching questions you may have had, i.e. What's the difference between data engineer vs data scientist vs data analyst?

Once you have a basic understanding of what job profile you’d like to go for, it’s time to understand what kind of specific roles exist in each of the 3 main categories and what kind of skills each role requires.

Data Engineer

data engineer

Basic job description:

Building software to create a viable data infrastructure to mine new data, organise raw data and store data.

Focus skills:

  • Database systems languages (SQL and NoSQL)

  • Data warehousing solutions

  • ETL tools

  • Data Modelling

  • Machine learning

  • Data APIs

  • Python, Java, and Scala programming languages

Specific roles in this category:

  • Machine Learning Engineer: Machine learning engineer is a person who has specialised in machine learning models. This is more of a software engineering role that involves taking a data scientist’s analysis and turning it into deployable software.

  • Data Warehouse Architect: This is a speciality field within data engineering. These professionals are in charge of a company’s data storage systems. SQL skills are a must for a role like this. You’ll also need to acquire a lot of other tech skills based on the organisation’s existing tech stack (tech infrastructure)

Doing an extensive data engineering course Hong Kong could open up huge opportunities in this category.

data analyst

Data Analyst

Basic job description:

Analysing data patterns, relations and finding answers for other teams

Focus skills:

  • Intermediate SQL queries

  • Data cleaning

  • Data visualisation

  • Probability and statistics

Specific roles in this category:

  • Business analyst: A data analyst who is focused on analysing market and business trends. This position requires familiarity with software-based data analysis tools (like Microsoft Power BI)

  • Quantitative analysts: Quantitative analysts, sometimes referred to as “quants”, use advanced statistical analyses to answer questions and make predictions related to finance and risk.

  • Systems analysts: These professionals are often tasked with identifying organisational problems, and then planning and overseeing the changes in the tech infrastructure of the organisation.

  • Marketing analysts: These professionals are tasked with looking at sales and marketing data to assess and improve the effectiveness of marketing campaigns. In the digital age, these analysts have access to increasingly large amounts of data via platforms like Facebook Analytics or Google Analytics.

Doing an extensive machine learning & data science course could open up huge opportunities in this category.

data scientist meeting

Data Scientist

Basic job description:

Think creatively based on a data analyst’s findings and make decisions or predictions based on a thorough evaluation. A big part of it entails machine learning roles and responsibilities.

Focus skills:

  • Building and training machine learning models that can make reliable future predictions based on past data.

  • Advanced programming skills in Python or R

  • Supervised and unsupervised machine learning methods

  • Statistics and the ability to evaluate statistical models

  • Understanding of various tools like MATLAB, Excel, ggplot2, Tableau

  • Familiarity with machine learning frameworks like TensorFlow, Keras and Pytorch

Specific job roles:

In this category, Data Scientist is usually the only job role that exists across industries. There can be different seniority levels, but the job profile remains the same.

Doing a part-time Python course in Hong Kong could open up huge opportunities in this category.


Taking the right Data Science course

For absolute beginners:

This depends on your existing skill base and qualifications. If you don't have the capacity or time to commit to a full-time course, consider a part-time opportunity. This will give you good exposure to the industry and help you better understand the nuances of various jobs.

Xccelerate offers a beginner friendly part-time python course and part-time data science course to help you learn the basic knowledge in the following categories:

  • Data Professional

  • Junior Python Programmer

  • Data Analyst

If you wish to learn all the fundamentals from scratch and then move on to a new career, then going for a more comprehensive course is a better idea. Xccelerate's immersive coding bootcamp in Data Science and Machine Learning is great for amateurs. There are no prerequisites and it is suitable for anyone who wants to learn Data Science from scratch.

This course opens up many job opportunities like;

  • Junior Data Scientist

  • Data Analyst

  • Data Engineer

  • Machine Learning Engineer

  • Business Intelligence Associate

For others with coding background

If you already have a strong background in coding, especially in R and Python, you can choose a more advanced data science course with strong career support that focuses on domain expertise, advanced data science tools and career support.


Data Science in Hong Kong is booming! This is a great time to invest in the skills that can lead to a very interesting and rewarding career. Due to the lack of reputed universities with specialised courses in data science, your best option is to complete a graduate or an undergraduate degree and go for a data science course offered by independent institutes, like Xccelerate.

If you are at a choice point in your career and need someone to help you navigate professional challenges. You can make an appointment to our complimentary 1-on-1 Career Consultation and receive personalised career advice.

Read Also: Is Data Science Difficult to Learn?

Thomas Ho

30 Jan 2021

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