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What is Data Science and EngineeringWhat is Data Science and Engineering

What is DS&E

Before we try to make clear the differences between the two most prominent professions within data science – data scientists and data engineers – let’s learn about how data is used in industries and why it is important for us to work with data in a world where technology keeps changing. To meet market expectations when making product or service, companies need information to understand the current market, their competitors as well as target users. This useful information is stored as data that is scattered across the internet and both data engineers and data scientists play a vital role in this complex data process.

What is Data Science & Engineering?

We have broken it down into a few key terms below.

Statistics: The base of all Data Mining and Machine learning algorithms.

Data mining: The extraction of data from databases, data warehouses, or complex datasets. Engineers “mine” data and prepares them for further analysis.

Data analytics: It involves analysing data using statistics or programming to find trends, patterns, and derive useful insights.

Machine learning: A technique that develops complex algorithms to process large datasets and generate results to users through artificial intelligence. It can be used to understand data and build models that can be used by humans.

Big Data: An umbrella term that refers to a vast and complex collection of datasets that cannot be processed using existing database management tools or traditional data processing applications. They are processed through specialized techniques and customised tools like software, algorithms, programming, etc.

Differences between Data Science and Data Engineering

Traditionally, data scientists and data engineers are specialized roles that are responsible for certain tasks of the data process, each requiring a different skillset.

A data engineer is a data professional responsible for preparing data infrastructure for analysis. They extract relevant data from a variety of sources using programming languages such as Java, Scala, C++ or Python. They also build “pipelines” and maintain the systems that store, extract, and organise data to ensure that data is accessible in the future, as well as saving time on coding.
A data scientist work on analysing extremely large amount of data (Big Data) that predicts trend and presents valuable insights to help businesses make decisions. Data science is an important tool used to understand more about product design and manufacturing. Data scientists apply their knowledge in advanced statistics modelling, data analytics, data visualization and machine learning algorithms to identify trends and answer questions that are relevant to organisations.
Hence, as a multidisciplinary field, data professionals in Data Science and Engineering requires strong methodological and engineering skills, along with an analytical approach to address data-driven problems. Combining data science and data engineering enables engineers to manage the entire life cycle of the data analytics process, which includes collecting large amount of data, analysing them through mathematical and statistical models, as well as machine learning algorithms, and visualizing data to inform decision-making.

Why Data Science and Engineering?

How is a background in engineering helpful to the data science industry? Well, data scientists have an in-depth understanding about different data systems and their ability to code helps enhance the productivity and quality of the algorithm code. They also have prior experiences in data cleaning and detecting outliers within the datasets, thus generating more accurate and effective decisions. Possessing multiple skills from data extractions to developing insights and software engineering, data scientists who has a background in engineering are more advantageous career-wise.
Vice versa, having the skills of data science allows engineers to process large amount of data after collecting them. For instance, Civil Engineers can monitor the infrastructure conditions above and below the ground through data mining, sensing, and analysing. Chemical Engineers can be benefitted from possessing data science techniques to navigate through large datasets.
To date, data scientists are falling short. Yet, many sectors have turned to data specialists to help with extracting useful information from a tsunami of data and then using machine learning to analyse data that helps inform business decisions and product design. As a result, bridging the knowledge gap between data science and engineering is especially crucial in response to such shortage in talent. Engineers who are trained in data analytics are at an advantage in discovering more job opportunities and breakthroughs.

Career Prospects

Working with data is currently a highly wanted profession in many different industries such as finance, consulting, marketing, telecommunications, medical and many more. According to the Salary Explorer in 2022, data scientists in Hong Kong has a monthly salary of HKD27,300 to HKD91,800, with an average monthly salary of HKD58,100. It is predicted that there is a steady and optimistic salary increase for experienced data scientists. Another study by LinkedIn found that statistical analysis and data mining was ranked the second most sought after skills by various employers, highlighting the huge potential for data professionals in today’s work force.