Data analytics vs data science: What’s the difference?

Data creation and collection are growing at such a significant rate that data is being called the new oil. Almost every commercial sector is currently witnessing a rapid growth of data generated through varied sources; this is driving changes in decision-making. Business decisions were once based on experience and intuition; now they’re partly or wholly based on data. Although this huge amount of data has become a challenge for many companies to manage, it’s also opened doors to new opportunities for businesses by offering valuable customer and sales insights.

Given the rapid rise of data, two functions have become extremely important to businesses: data analytics and data science. Understanding what data analytics and data science are and the respective value they add in the data-driven world is important. 

What is data analytics? 

Data analytics is the scientific process of analysing raw data and drawing conclusions. Insights garnered from data analytics help businesses optimise performance and make important business decisions. 

Algorithms and processes help data analysts create meaning from raw data. These processes help data analysts assess what’s happened (descriptive analytics), assess why something happened (diagnostic analytics) and make predictions on what’ll happen or what steps should be taken next (predictive and prescriptive analytics).

What is data science?

Data science is an interdisciplinary field that involves processing big data to find insightful patterns and trends using various tools, mathematical algorithms, machine learning principles and statistical techniques. The professionals who use data science techniques to mine insights from big data to aid business decisions are called data scientists.

Data analytics vs data science: Similarities and differences 

Data science and data analytics share significant similarities, but also differ in important ways. 

Similarities

Data science and data analytics are closely related. They both use and analyse data to gain insights and help businesses make important decisions. 

Differences  

The main differences between data analytics and data science can be seen in each discipline’s purpose and approach. 

Purpose

Fundamentally, data scientists and data analysts approach data differently. Data analysts typically focus on analysing past data to inform present decisions and producing insights to answer specific questions that can then be put into action. 

Data scientists, on the other hand, treat data more broadly, exploring it and using it to build models that can predict future outcomes. Data science as a field is more expansive – encompassing data analytics, as well as data engineering and machine learning. 

Approach

Data analysts are generally more narrow and focused in their approach to data. They typically prepare, manage and analyse datasets that are already well defined and then use that information to garner insights and create presentations that help businesses make decisions. 

Data scientists, on the other hand, prepare and explore large datasets and then develop their own analytical models and algorithms. They communicate, co-create and collaborate with stakeholders to explore potential goals and create profitable outcomes. 

Skills needed to become a data analyst

Data analytics involves processes such as data integration and data visualisation and presentation. To succeed in the role, data analysts must be able to: 

• Create data visualisations
• Clean data
• Use MATLAB, R, Python, SQL and NoSQL 
• Understand machine learning 
• Use linear algebra and calculus
• Think critically and communicate well. 

Skills needed to become a data scientist  

Data science is a broad multidisciplinary field that involves engineering, computer science, statistics, machine learning and data presentation and visualisation. A number of data science skills are key to success, including the following:  

• Statistical analysis and computing
• The ability to understand and create machine learning and deep learning 
• Advanced data skills, including processing datasets, data visualisation and data wrangling
• Advanced knowledge of mathematics and programming. 

Big data in data analytics and data science

Given the massive growth of data, considerable opportunities and applications of both data analytics and data science exist across numerous industries. Both fields have a promising future.  

What is big data?

To identify the opportunities within data science and data analytics, understanding a bit more about big data is important. Big data is a large volume of data, present in both structured and unstructured form, that can be mined or processed to get valuable customer insights and improve business processes. 

Unlike conventional data, big data can’t be handled using traditional database programming. This heterogeneous data, available in all types and formats, can come from a myriad of sources, such as customer databases, social media accounts, emails, tweets, blogs, web pages, customer sign-ups, system logs, online discussion forums and transaction data. When the data is aggregated from all these diverse sources for further processing, it becomes big data. Big data is characterised by volume, variety and sources. 

Data analytics applications

Data is present in nearly every industry. As such, its analysis is also widespread across different sectors. 

Healthcare sector

Data analytics allows healthcare providers to improve the quality of care, reinforce the doctor-patient relationship and decrease costs. By aggregating worldwide data, physicians can understand which drugs are least likely to cause side effects, what procedures are most cost-effective for treating a particular disease and which doctors have the best outcomes.

Retail sector

Data analytics applies to every stage of the retail process. For example, by predicting market trends, retailers can identify popular products and where demand would be highest for those products. They can also identify customers who are likely to be interested in them. Data analytics also helps retailers optimise pricing for a competitive advantage.

Financial services

Before the arrival of data analytics, banking and financial sectors faced business challenges associated with meeting regulatory objectives, improving customer intelligence and reducing risks. Data analytics has allowed financial service providers to develop data-driven products, give a more personalised service to their customers, detect and prevent fraud, retain customers, better comply with regulations and increase sales.

Data science applications

Like data analytics, data science is integral to information gathering and use in many different areas and functions. 

Internet searches

Data science is used by search engine algorithms to deliver accurate results according to user queries. The tools and methods of data science are utilised to process a significant number of queries and convert them into relevant results.

Digital advertisements

Data science algorithms have given a whole new depth to the digital advertising world. From display banners to digital billboards and computer bulletins, each uses data science techniques and approaches.

Search recommenders

The search recommender system uses data science to help users find relevant products from billions of options available on the internet. These systems help companies promote their products by providing relevant suggestions to the users in accordance with their demand and search history.

How to become a data analyst

Undergraduate and postgraduate qualifications are typically required to become a data analyst, as well as a portfolio of work. 

To become a data analyst, follow the below steps: 

1. Gain an undergraduate qualification in computer science, mathematics, statistics, IT or business analytics. 
2. Gain work experience. This may be through an internship or completing a voluntary project at a non-profit. Real-world experience is important to build a portfolio for future employers. 
3. Consider obtaining a postgraduate qualification, including a master’s degree in analytics or a data science course.

The average salary of a data analyst in Australia in October 2023, according to Seek, was $95,000.

How to become a data scientist

To become a data scientist, formal qualifications and industry experience are needed. Here’s how to become a data scientist: 

1. Gain an undergraduate qualification in computer science, IT, mathematics or statistics. 
2. Advance your skills with a postgraduate qualification, such as a Master of Data Science.
3. Gain work experience, such as an internship, to build soft skills, grow a professional network and build a portfolio. 

The average salary of a data scientist in Australia in October 2023 was $120,000, according to Seek.

Data scientist and data analyst: Jobs of the future

Whichever path you choose, one thing is true: There’s an enormous amount of potential in both of these careers. According to recent data by Seek, the projected job growth for data analysts in the next five years is 23 per cent, with the projected growth for data scientists being 11 per cent. Being equipped and qualified to take advantage of this growth is important. 

James Cook University’s online Master of Data Science helps professionals leverage this great opportunity to improve their expertise and develop their careers in the ever-evolving field of data analytics and data science. Find out more about the program and apply.