The Importance of Data Mining in Data Science in 2022

13th October 2021

Data mining has become a buzzword in popular culture that’s used to describe everything from cookies on websites to the fear that your phone is being used as a listening device.

In reality, the data mining process is the analysis of large sets of information, or big data, for pattern recognition. Data mining is an essential process in data science because it enables data scientists to ask the right questions.

Let’s analyse data mining itself to answer the question of why it’s important and how developing your skills in data mining can enrich your career in data science.

What is Data Mining?

Data analytics software supplier SAS defines data mining as “the process of finding anomalies, patterns and correlations within large data sets to predict outcomes.”

You might be familiar with some of the data mining tools and techniques, which include classification, clustering, regression, association rules, outer detection, sequential patterns, and prediction. Some of these techniques are also used in data analysis, statistics, and mathematics.

According to the CRISP-DM framework, the data mining framework was published by a project led by a consortium of five companies in 1996.

There are six stages under the CRIS-DM framework, although the order can be changed, and it’s not uncommon to go back and forward between them.

The major phases of CRISP-DM are:

  • - business understanding
  • - data understanding
  • - data preparation
  • - modelling
  • - evaluation and
  • - deployment.

The time required for data preparation, the third phase of data mining can be up to 80 per cent of the time for the entire project. This is because raw data from different sources can vary in quality ­– but it all needs to be cleaned, transformed, formatted, and anonymised.

Key Differences between Data Mining and Data Science

The simple difference between data mining and data science is that data mining is just one part of data science.

Data science is a multidisciplinary field that uses statistics, scientific methods, artificial intelligence (AI), data analysis and of course, data mining, to refine useful information from massive volumes of data. Data scientists also work with programming languages including R and Python.

Beyond data mining, data scientists are building machine learning models and applying them in a variety of settings. You may be familiar with machine learning on websites like Amazon, which recommends other products you may be interested in, based on your purchase.

But that’s not all that Amazon’s machine learning is recommending.

Australia’s Olympic relay swimmers triumphed in Tokyo, thanks in part to a relationship between Swimming Australia and Amazon Web Services (AWS). AWS’s machine learning predicted the swimming squads of other countries, then suggested the best relay team combinations for Australia to win.

“Swimming Australia had a lot of systems that were collecting different types of data,” Karl Durrance, AWS director of enterprise Australia tells the Sydney Morning Herald.

“By bringing this together into a central cloud-based data lake, the Amazon Machine Learning Solutions Lab and Swimming Australia developed a solution that pulled together athlete performance to support coach’s decision-making in competition, predict possible and probable competition outcomes, and influence tactical racing strategies while saving hours of manual analysis.”

Why is Data Mining is Important in Data Science?

Data mining is all that stands between our decision-making brains and a firehose amount of data that is increasing every single day. In fact, each of us is contributing another 1.7 MB to the global data tally – every single second.

So why don’t we just apply machine learning to that data? As it turns out, 90 per cent of that data is unstructured data – things like audio, video, documents, emails, information from the Internet of Things (IoT) and this blog.

Any data that isn’t in a relational database management system (RDBMS) is unstructured data. That’s why the third phase of CRISP-DM, data preparation, can take up to 80 per cent of a data scientists time to convert big datasets into structured data.

Ever since the early days of data processing and computing in general, we’ve known that garbage in equals garbage out (GIGO) – and that truism is more relevant than ever today.

Data mining is important in data science, not only because it takes out the garbage when preparing data, but it aligns data with business understanding to produce data visualisations and information that influences effective decision making.

Data Mining and Machine Learning

Machine learning uses statistical analysis methods to train algorithms that can provide insights from data mining projects. In its simplest form, the algorithms learn the relationships between groups of data, then in predictive models, they deliver forecasts when one or more of those groups is changed.

Data mining can be viewed as a subset of machine learning and business intelligence. It’s the preparatory work of data mining that makes machine learning shine.

IBM claims to have bragging rights to the term ‘machine learning,’ or at least one of their researchers does. Arthur Samuel first used the term machine learning when an IBM 7094 computer beat a human at a game of checkers.

Machine learning is now being used to show public transport users in New South Wales and Victoria how full their train is going to be, or how packed the station is before they leave the house or office. Data that are mined from passenger counting sensors are combined with predictive data modelling and machine learning for real-time crowding estimates.

Other valuable uses of machine learning from data mining include virtual assistants in the home, fraud detection in banks, image detection in healthcare and the movement of money in financial institutions. Deep learning is a further extension of machine learning that makes speech recognition, natural language processing and computer vision possible.

How Data Mining Techniques Can Help Businesses In The Industry

Superannuation provider Aware Super has recently harnessed the power of Microsoft’s Azure Machine Learning to forecast the future growth of its $130 billion in funds for 1 million superannuation account holders.

Aware’s previous modelling system couldn’t handle multiple users running what-if models – in fact, only one data scientist was able to make changes to the model.

“One of the objectives was to make sure that the solution was customisable, and that it could also deal with changes that we may or may not know today, but that we can see coming down the line, whether that’s a regulatory change or environmental change,” Richard McDulling from Aware Super tells ARN.

The Career Opportunities in a Data Science Role 

When it comes to forecasting, the one data point that matters most to future data mining specialists is the predicted job growth for data scientists. According to, the number of data scientist jobs in Australia is expected to increase by almost 28 per cent over the next five years.

Demand for data scientists is also high globally. In the US, data scientist was the third-best job in 2020 based on salary, job satisfaction and job openings.

Seek has also done some data mining of its own with the job advertisements on its platform to determine that the most common salary for data scientists is $130,000.

Masters-qualified data scientists command higher salaries – in fact, top-level data scientists earn a median salary of $240,000. There are no hidden patterns here, it’s just that postgraduate in, equals career rewards out (although PICRO might not catch on as an acronym).

Key Differences When You Study Data Science with JCU Online

You should study data science with JCU Online because you’ll graduate faster. The reason we have one of the fastest part-time master’s degrees in this field in Australia is that you can complete six subjects each year.

You’ll be learning with teaching staff who have years of experience in industry and academia. This interdisciplinary approach sharpens your skills for real-world situations.

And just like CRISP-DM, this nested qualification leads you through a logical sequence so you can ‘qualify-as-you-go’. The first four subjects of the course make up the Graduate Certificate of Data Science – add on the next four and you’ve got a Graduate Diploma of Data Science.

JCU Online’s flexible entry points also give you the opportunity to put your Master of Data Science on hold if life or work demands more of your time.

What Can I Expect to Learn about Data Mining in the Master’s?

We’ll start at the beginning with an introduction to data mining. You will eventually be doing some hands-on data mining with real data sets in RStudio and Python, but before you get there, we’ll learn the basics.

You’ll learn classic techniques for the most common descriptive and predictive tasks in data mining, including clustering, outlier detection and classification. To give you the edge in your computer science career you’ll gain a conceptual, as well as a physical, understanding of these algorithms and techniques.

Once you’re comfortable with software(s), algorithms and data sets, we’ll combine data mining and machine learning.

We’ll provide you with the most common machine learning algorithms and techniques and teach you some sophisticated supervised learning methods. In other words, we’ll put the most important data science knowledge in, so that you get the best data scientist career out.

Data Science

Find out more about JCU’s online Master of Data Science.

Get in touch with our Enrolment team on 1300 535 919

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