As we are entering the age of big data, data scientists are becoming a critical enterprise asset in almost every organisation. Hoping to take your first step down the data scientist path? Good news to you, no matter your background, data science is one of the most dynamic fields to enter into. Let’s look deep into the life of a data scientist and what could be done to navigate a successful career change.
What is a data scientist?
As the title implies, the duty of data scientists is mainly about approaching data from a scientific angle, and thus, inseparable from its core – “data science”.
Living in a digital age, almost every part of our daily lives can be reduced to a number. With an aim to extract valuable insights from an enormous amount of data, the data science discipline emerged.
Data science is in fact an interdisciplinary field, given that it combines a number of pre-existing disciplines such as computer science, statistics, mathematics, software development, machine learning and more. By applying a range of logical and analytical techniques, data scientists are able to precisely explain trends and patterns in the data, generating actionable plans for businesses and other organisations.
TL;DR: Data scientists are all-rounded professionals who can make messy data more digestible and help laymen increase the accuracy of their decisions.
What does a data scientist do?
It’s almost impossible to summarise the duty of a data scientist in a single paragraph since the task list can just go on and on. Instead of being unnecessarily long-winded, let’s just put together 10 key responsibilities of a data scientist:
- Identify the relevant data sources according to business needs
- Acquire both structured and unstructured data
- Integrate data into usable formats
- Investigate data with predictive models and algorithms
- Analyse data with tools such as Python, R, SAS or SQL
- Verify the quality of data and remove unwanted observations
- Recognise trends and patterns from data to generate critical business insights
- Create interactive visuals to improve audiences’ understanding
- Present final results to executive and project teams
- Keep track of the latest innovations in the data science field
Data Scientist vs Data Analyst vs Data Engineer
Although the work of data scientist, data analyst and data engineer might seem similar, they play quite a different role in the data analytics journey.
Data engineers set a solid ground for future analytical usage. They help to develop the foundation for multiple data operations and structure the framework for data analysts and data scientists to interpret.
In general, data engineers are more specialised in the programming field since they design and build data infrastructures such as databases, big data repositories as well as data pipelines for transforming data between systems.
According to Glassdoor, the average monthly salary of a data engineer is HK$29,000 in Hong Kong.
Working on the data prepared by data engineers, data analysts are responsible for the next step – extracting usable information from the given pool of data.
There are 4 stages of data analytics, and data analysts usually focus on the first 2 stages:
According to Glassdoor, the average monthly salary of a data analyst is HK$21,400 in Hong Kong.
Data scientists are often considered as the more senior level of data analysts since they have to oversee every single part of data analytics.
For example, data scientists put together various disconnected sources and discover the underlying dependencies between various data points, whereas data analysts normally look at data from one source. Moreover, data scientists need to make informed predictions by building machine learning algorithms and statistical models which requires a strong background in mathematics and computer science.
Most importantly, data scientists are expected to communicate well both verbally and visually as the last step is to deliver an impressive presentation to decision-makers. Given that the data might be too complicated for non-technical stakeholders, it is of utmost essence for the data scientists to present findings and recommendations clearly and concisely.
Read More: Ultimate Guide to Data Science 2022
What skills do data scientists need?
1. Data visualisation
As decision making increasingly relies on data that often comes with overwhelming velocity, the ability to create graphical representations of information can definitely help you become more productive in your role.
Compared to boring words, visual elements such as charts, graphs and maps are relatively simple and easy to read. By utilising various data visualisation tools and techniques, data scientists should be able to present simple data in a more visual and engaging way.
2. Machine learning
Needless to say, it is impossible for humans to handle massive amounts of data manually since one tiny incorrect mistake can already lead to meaningless or misleading results.
Therefore, data scientists always work hand in hand with machine learning. By applying algorithms to data, computers can automatically perform pattern recognition and hence, enhance the efficiency of data processing.
3. Deep learning
As a branch of machine learning, deep learning attempts to cluster data and make predictions with incredible accuracy.
Inspired by the human brain, deep learning is specifically designed to detect patterns within unlabelled datasets and distinguish meaningful characteristics without human intervention. Since deep learning algorithms can draw similar conclusions as humans, data scientists should at least understand its foundation in order to manage the exponential amounts of rapidly changing data.
4. Pattern recognition
One of the greatest challenges of data scientists is to mine the hidden patterns in data. To streamline this complex yet crucial process, there are multiple innovative ways to recognise patterns quickly and accurately even though they were partly hidden.
Therefore, not only do data scientists need to build the statistical model, but they also need to keep advancing the robotics and automation algorithms for better results.
5. Data preparation
Data preparation is properly the lengthiest part in the life cycle of data analytics, however, error-free data is the cornerstone of valuable insights.
To cleanse and validate data, the scientists have to eliminate faulty data and fill in missing values. Once the problems are resolved, data scientists can continue to update the format or value entries in order to reach a well-defined outcome. These tasks require an intense investment of resources and could never be done by those who may lack advanced IT and logical techniques.
6. Text analytics
According to SlickText, 5 billion people send and receive SMS messages across the globe, not to mention other forms of textual content such as formal email, social media posts, customer support notes and more.
Unlike uniform numbers, each approach to text analytics can bring out diverse findings. With an aim to improve objectivity, sometimes data scientists might need to set manual rules concerning how each word relating to their industry should be understood and analysed by the system.
Is data science a good career?
Demand for data scientist roles is on the rise, as of April 2022, there are already 5,280 openings relating to data scientists on JobsDB alone.
As a matter of fact, the entire industries are being reshaped by data analytics, below are some of the largest industries that data is marking big changes in:
Healthcare professionals have long been collecting huge amounts of data for medical use, examples include blood pressure range, sucrose level and BMI.
Thanks to today’s always-improving technologies, the medical field can go beyond simple data collection, but also create comprehensive healthcare reports and convert them into relevant critical insights that can then be used to provide better care.
Back in the days when computers could only digest structured data, the flexibility and use cases were limited.
New technologies allow modern investment firms to analyse both structured and unstructured data, including those that are not easily quantifiable or organised in a set form, which helps investors to identify strong businesses with attractive valuations and prospective opportunities.
Big data is especially important in the logistic sector since the supply chain as a whole is extremely data-driven. From freight tracking to warehousing, there are countless data points that are worth investigating.
By applying statistical methods to both new and existing data sources, decision-makers can gain new insights into sales, inventory and operations planning based on a balanced combination of experience and analysis.
How to become a data scientist?
Earning a relevant educational background is a general step to becoming a data scientist.
Given that the field of data science is relatively new, the most sought-after studies are statistics, mathematics, information technologies and computer science. If you are not an undergraduate anymore but planning to quit your job for a career switch, it is recommended to acquire essential knowledge such as programming languages, database architecture, SQL and MySQL management through online courses or boot camp. You can even pursue a master’s degree to give your future career a strong boost.
To excel in their careers, data scientists have to equip themselves with numerous skills, including statistics and probability, model deployment, machine learning, deep learning, data manipulation and analysis, data visualisation and more.
Practising these skill sets outside of the classroom is as important as the time you spend inside of it, therefore, don’t be afraid of putting your learnings into practice on real-world projects. There are many open-source databases for you to work on, for example, Kaggle, NASA, Wikipedia and UCL Machine Learning Repository.
Every great hire begins with candidates that possess specific traits and characteristics, so do data scientists.
Since data scientists work constantly with statistics, data, mathematical and logical algorithms, an individual with a detail-oriented mind is definitely a plus. Moreover, experimentation is a major focus of data science. Data scientists have to try different algorithms against different combinations of data, meaning they will encounter countless failures until discover the right solution. If one lacks the resilience to accept repetitive failures, he or she might not be a good fit for this activity.
Get an entry-level job
Though there are many different ways to kick-start your career, getting an entry-level position gives you a chance to take the first step. By gaining hands-on experience and working alongside data science specialists, you can expand your knowledge and develop a better understanding of the industry at large.
Data scientist salary in Hong Kong
Data scientist course
If you are looking to transition into data science from a completely different background, it is highly recommended to seek guidance from experienced mentors rather than learning alone.
Regardless of your skill level or career goals, signing up for a boot camp can bring you many advantages as they are generally more flexible and affordable than getting a degree from a traditional university, coupled with more hands-on data science projects that allow students to apply their newfound skills and knowledge.
Preface’s Data Science & A.I. with Python encompasses a wide range of topics:
- Python for Data Science
- Web Scraping with APIs
- Data Crawling and Data Mining
- Data Cleaning and Supervised Machine Learning
- Deep Learning
- NLP and Image Classification Learning
- Data Visualisation
Each of these topics builds on the previous ones, so students can acquire these skills in the right order without getting lost and wasting time. In the final module, students can even build, train and deploy their own machine learning models at scale, which will be a great project showcase to brush up on your portfolio.
1. How long does it take to be a data scientist?
It depends, as the pace of career progress varies from person to person.
However, with reference to a survey conducted by KDnuggets, the median is about 5 years across the globe and 4.9 years in the Asia region.
2. Can I learn data science on my own?
Certainly. With so many online resources available, it is possible for a newbie to explore the fundamentals of data science as a newbie.
Still, if you are truly serious about career success, it is suggested to take classes taught by renowned educators since they will offer you a more structured and up-to-date curriculum that prevents you from spending a lot of time learning irrelevant information while missing the most important concepts.