Data Science vs Data Analytics - Key Differences You Must Know

As we progress towards a future where the Internet of Things (IoT) thrives, data is fast becoming the new oil, a powerful commodity that companies are looking to harness to gather useful insights for better decision-making. In fact, investments in IoT technology are projected to grow at 13.6% per year through 2022.

This will also boost the demand for data science and analytics professionals in this future-proof field. However, it isn’t very easy to differentiate between data analytics and data science.

Both of them are essential to helping you analyze and interpret data into valuable and actionable insights. Data science and data analytics are key components of business intelligence and are two sides of the same coin. But before we continue, understanding their definitions is essential to appreciating their differences.

What Is Data Science?

Data science is the field of finding actionable insights from large sets of data. However, it is based on finding answers to questions that we might have. It’s about unearthing the answers to best answer a query we have.

This can be as simple as asking “Which state in the US should we open our next branch in?”. The answer might be as simple as ‘California’, but understanding how to connect disparate data is the challenge.

And in order to accomplish that, the data scientist or professional will be focused on finding and refining the right question to ask. This is where data science shines. Graduates will understand how to predict potential trends and find meaningful correlations.

What Is Data Analytics?

Data analytics, however, is more focused on the here and now. It is far more analytical in discovering the current trends that are occurring from the data.

Data analysts are concerned about capturing, processing and manipulating data in order to uncover actionable insights to solve pressing problems. This can be as simple as finding vulnerabilities in a network security system or the sales trend of a particular store.

Data analytics help to identify patterns and trends that are already happening so that businesses can exploit these opportunities to the fullest.



Key Differences Between Data Science & Data Analytics

Data science and data analytics are both unique fields and their major difference is their scope and their goals.
* Data science is a combination of multiple disciplines that include statistics, computer science, machine learning and AI. And its goal is to ask the right questions in order to gain groundbreaking answers that can lead a company in a new direction or optimize current processes.
* Data analytics is mainly centred around finding actionable data and identifying trends that already exist. These mainly revolve around industries with immediate data needs such as travel, finance and gaming.
* Data science seeks to discover new and novel ways to do business and drive innovation forward. It is about the macro picture and it involves asking the right questions. Data scientists do this by harnessing the power of predictive modelling powered by advanced technologies such as machine learning and even artificial intelligence.

However, a data analyst is concerned about finding answers to an existing set of questions which are normally part of a business’s existing goals such as increasing customer retention or improving revenue generation. This is where data analytics helps by identifying existing connections between datasets. As a data analyst, you will also be creating dashboards to help you better understand the endless streams of data as well as create reports of actionable insights for your management to take action on.

Essential Skills You Need To Build A Data Science & Data Analytics Career

To build a career in these fields, there are many overlapping skills that you must learn. The most essential skills will include the basics of computer science. These include the basics of data analytics, mathematics, numeral analysis and statistics. This will give you a firm grounding in logical thinking and how to effectively work with data.

To build statistical models and to better work and process data, you’ll also need a firm grounding in programming languages. This could include SQL, Python and C++. These will help you organise and categorise big data sets for processing.

As both a data scientist and analyst, you will be expected to understand how to use analytical tools and dashboards such as Tableau and Power BI. This will help you better understand and interpret data to gather insight and for effective reporting.

Additionally, a core data science skill you will need to develop is the ability to develop program statistical models for data analysis. This means understanding deeper programming languages like Scala, using big data tools like Hadoop as well as possessing critical thinking and problem solving ability.

This will allow you to identify trends, ask the right questions and more importantly, interpret the insights correctly, translating them into actual business decisions and predictions.

Exciting Career Opportunities Await In Data Science

Upon graduation, you’ll be able to access a spectrum of industries and roles with your degree in data science. These industries include banking, travel, ecommerce and healthcare.

Here are some of the career paths and roles you can expect to embark on with a data science degree:
* Computer scientist
* Software engineer
* Data analyst
* Machine learning engineer
* Data scientist

As a data scientist, for example, you will be tasked with extracting meaning and interpreting data utilising both analytics tools as well as machine learning algorithms. You will be working with other data analysts including software engineers to help build predictive models to test your thesis and ask better questions to help drive innovation for your company.

With a data science degree, embark on an exciting career in a field filled with constant challenges and opportunities to learn and grow as a technology professional.

Embark On Your Data Science Career With SIM

Keen on taking the next step and start pursuing a career in data science?

At SIM, our Bachelor of Computer Science (Big Data) programme, awarded by the University of Wollongong, is designed in consultation with employers and industry groups to provide you with the most relevant and updated education in the field of Data Science.

As the leading private education institution in Singapore, our lecturers demonstrate both professional and academic competence in the industry, ensuring you receive the very best learning experience possible.
It’s time to join a fast-growing industry brimming with potential for you to develop a fulfilling career across various exciting roles. Data is the future and your future starts with SIM.

This course is offered by the University of Wollongong, one of Australia’s leading universities renowned for the quality of its research and teaching. The degree is taught by top academics and professors with industry experience. In 2021, the University has attained a 5-Star rating from the QS World University Rankings for academic excellence.

Learn more about the University of Wollongong’s Bachelor of Computer Science (Big Data) programme.