What Are the Types of Data Mining (and How to Start a Career)?

Data mining and data science have seen some of the most significant growth in recent years, with India alone predicted to see 11 million more job openings by 2026 [1]. This massive demand increases the value of data mining and machine learning skills. If you’re interested in pursuing data mining jobs, learning data mining basics is an essential first step. Discover the basics and more while exploring various facets of this growing field. 

What is data mining?

Data mining relies on computers to extract, analyse, and examine patterns in large quantities of data to glean insights. Businesses, brands, and individuals can use this process to learn about trends and patterns and even predict future probabilities. It has many uses and applications, from finance to media to the medical industry. Anywhere data is generated, you can use data mining to find solutions to systematic problems or predict future probabilities in a particular field. 

Various industries use data mining to improve their outcomes and strengthen their businesses. Examples include: 

• Telecommunications, technology, and media: Companies can use data mining to sift through the deluge of consumer data for improved customer targeting.

• Retail: Understanding customers and their behaviour is essential to optimising marketing and improving the customer experience. Data mining allows retailers to analyse information in customer databases to forecast sales and provide targeted marketing efforts.

• Manufacturing: Data mining allows manufacturers to forecast potential problems, such as maintenance, to minimise downtime. It also improves quality assurance and can help businesses meet demand forecasts.

• Education: Data mining can use learner data to help educators predict learners' performance. This insight can help with course development and the creation of intervention strategies to help deliver extra help to those who need it most.

Machine learning vs. data mining

People often group data mining and machine learning together, but they are different. While data mining allows humans to extract knowledge from large amounts of data, machine learning is a process in which computers use a combination of algorithms and data to learn. 

Data mining uses computers and algorithms to find patterns in swathes of data that are not easily identifiable by humans or would take too much time to analyse. The computer identifies a pattern in an array of consumer data, but it is up to a human to decide how they want to interpret that data. 

In machine learning, a computer may use data mining to find patterns and relationships in data, but ultimately, the main goal is to teach the computer to learn how a human does. The computer knows how to use new data and information, make a prediction or analysis, and determine what to do with that data in the future. 

For example, if data mining finds a pattern in a clinical drug trial that indicates an adverse effect of a drug, a clinician still needs to examine the data pattern, verify it, and decide what to do with the information. 

Popular types of data mining

Now that you have a basic understanding of data mining and how it works, let’s take a look at some popular types of data mining:

• Anomaly detection

• Association rule learning

• Classification analysis

• Clustering analysis

• Regression analysis

Anomaly detection

As its name suggests, anomaly detection looks for outliers, points in a data set that fall outside the typical pattern. These data points are statistical anomalies that require further consideration. It would be up to the business to examine these anomalies and find explanations for them. 

Association rule learning

Association rule learning examines the relationships in large amounts of data. You can use it to uncover the relationship between two data points or more. For example, supermarket chains and online marketplaces use this association to give consumers suggestions to consumers of products that others often buy with products they have in their cart. For example, if a market owner finds that consumers typically purchase beans with spices, the owner may want to move those items closer together in the store. 

Classification analysis

Classification analysis focuses on putting data points into classes or groups based on a particular set of factors to help answer specific questions or solve specific problems. For example, a medical professional could analyse patient symptom data, grouping them into classes to provide the patient with an accurate diagnosis and prescribe the right medicine based on the classification of their symptoms. 

Clustering analysis

Clustering analysis combines association rule learning and classification analysis, using data mining to identify relationships between data points and put them into their category based on their relationship. By grouping similar data, users can combine multiple factors of a person, such as age, job, and purchase behaviour, classify them, and arrive at a prediction. For example, if a retail company wants to offer coupons on certain products, it may use clustering to review data points like consumer behaviour, inventory levels, and sales data. The insights from clustering analysis can help that company predict the type of discount that will yield the greatest success.

Regression analysis

Regression analysis in data mining is a vital modelling scheme you can use to discover the factors or data points relevant to making a decision based on your models. You can use regression analysis to make decisions based on data trends and make accurate predictions about future trends. This analysis is best at finding the certainty of something happening. For example, a company may look at data trends in the monsoon season and use regression analysis to decide how much or how little to stock shelves with a particular product based on the weather changes. 

How to start a career in data mining

Data mining is growing in demand each year. You can find the technology throughout many sectors, from finance and real estate to retail and medicine. This means that you will find many different kinds of jobs that use data mining and data analysis. 

Data mining jobs exist in nearly every industry. For example, data mining can help predict fraud and forecast pricing in finance and banking, and medical research can help identify drug side effects in clinical trials. 

A quick search on Naukri, a platform that connects job seekers and employers, reveals the scope of data mining jobs available in India. Starting your career requires you to build the qualifications employers are looking for.  

Data scientists and analysts often deploy data mining at various levels. To become a data scientist, you need in-depth programming skills in SAS, MATLAB, R Programming, Python, and SQL. Data scientists using data mining need many different languages and types of analysis to make the best predictions using data. 

Data scientists usually have an undergraduate or postgraduate degree in computer science, business technology, economics, or a related field. Many higher-level data scientists typically have a master’s of science in data science or a related field. 

If you already work in the tech industry and have a degree, you can learn data mining techniques through online courses and certifications, which can also help boost your credentials. Still, gaining experience through an entry-level position or an internship can provide valuable hands-on, practical knowledge. 

Next steps

Whether you are interested in learning the basics or upskilling to pursue data mining jobs, consider taking an online course to explore the field and build your skill set. For example, the Data Mining Specialisation course through the University of Illinois Urbana-Champaign and the Applied Data Science with Python Specialisation course through the University of Michigan are available through Coursera.