A Complete Guide On Data Analytics Vs Data Mining

February 17, 2024 Manisha Veer

In today’s times, every business runs on data. Be it identifying past patterns, strategizing the next move, or making future decisions. So, now you know, that data-related fields are always staying high in the market. Gathering data and patterns is every organization’s job these days and professionals are hired so that they make it more sensible and easy for the company. Both techniques are used to work with data, but they differ in their goals and methods. In this article, we will explore everything about Data analytics Vs Data mining.

A guide on data analytics vs data mining

What is Data Analytics?
What is Data Mining?
Key Difference between Data analytics vs Data mining.
Skills required to get into data analytics vs data mining.
Career opportunities between Data analytics vs Data mining
Your responsibilities in respective roles
Recruiters hiring these professionals
Who can get into these fields?
The salary difference between Data analytics vs Data mining.
Topics you will learn in data analytics
Topics you will learn in data mining
Examples of data analytics vs data mining
Summary table of Data analytics vs Data mining
FAQs

What is Data Analytics?

Data analytics is used by businesses to strategize and plan a better future for their growth. This process is used to extract all the statistical insights from the internet and study their patterns. This is one of the main parts of every business these days to develop their services and product level in the market. It is done through different techniques such as descriptive analysis, diagnostic analysis, and predictive analysis. The collected numbers always help in predicting future trends, and making the right decisions in the coming campaigns or so.

Data analytics has become a part of every business system. Healthcare, finance, to technology are all using data in this internet era. Through data, professionals are studying all about human behavior, their preferences, and more. This helps any organization to improve their products and make them more appealing to their customers and this eventually hits high sales to the company. Data is easily collected these days through Internet insights.

What is Data Mining?

Data mining is digging a little deeper into data analytics. It majorly involves machine learning to gather more concise and accurate information. This is used to collect and bring out more data that was not identified earlier and can be of great help in clarifying decisions. Data mining works to revise the entire data again and helps in categorizing and bringing a more detailed vision to the table.

The techniques used in data ming are clustering, classification, and regression analysis. These systems help in identifying spam emails, frauds, etc. The professionals can gather clear information on customer behavior, and reasons for the product or service losses. Overall, this helps in better outcomes for business sales and growth.

Key Difference Between Data Analytics vs Data Mining.

4 key differences between data analytics and data mining:

Goal	Data analytics is designed to collect specific information and make future decisions whereas data mining is created to extract all unknown insights of the business and help in identifying spam and choosing the perfect future trends for the benefit of the business.
Techniques	Processes used in Data analytics are majorly involved with statistics such as descriptive analysis, diagnostic analysis, and predictive analysis. Vs The processes involved in data mining are majorly machine learning such as clustering and classification.
Scope	Data analytics is a mainstream subject to learn. It includes everything from identifying patterns, numbers and to making future decisions Whereas, data mining is a subcategory of the mainstream subject, it helps the data analytics process to dig deeper and provide better solutions.
Outcome	The final results of data analytics are to make the informed future decision for the business while data mining gives better insights by giving relationships between each set of data and helps the organizations in improving their sales by making exact changes needed.

Before making any decision you might wanna check on a few skills that can give you an upper hand on the job. If you already have them then it’s great otherwise you can take up a few courses and practice more to build them.

Skills needed for data analytics are:

If your favorite subject is mathematics in school or college, then you have a level up in this field.
A basic understanding of machine learning and computer science can help too.
Good Communication skills.

Skills needed for data mining are:

Be good at any of the programming languages – As you will be dealing with lots of data and operations, scope in these areas is necessary. So, buckle up and learn Python, R, and many more.
Good knowledge of statistics
Decent presentation skills.

Career Opportunities Between Data Analytics vs Data Mining.

Data analytics professionals may land into job roles such as:

Data Analyst: Your job here is to collect and identify insights that can help in information for future decisions.
Business Analyst: Your involvement here is to study the data and help the business in the finance sector with the costs and improvements they can make.
Data Scientist: Your job is to input a lot of new strategies in machine learning techniques and bring out better information in huge sets. Note that you might want to be good with Python and R because you will be working with a major part of programming.
Data Engineer: The role here is to build and maintain the structure and functioning of the entire database that is used for the collection, storage, and analysis of data.
Business Intelligence Analyst: This job needs good data visualization skills because you will be responsible for creating final dashboards that are presented to stakeholders. Make sure you can explain numbers in simpler language for the non-technical stakeholders as well.

Data Mining professionals may get into:

Data Mining Analyst: This role is required to use new-age techniques to understand and bring out the patterns of large data sets. You might need to learn Python and other programming languages to make your job easy on the site.
Machine Learning Engineer: Your job here is to create artificial intelligence kind of machines to analyze and make patterns. Note to learn Java or Python beforehand.
Data Scientist: The job here is to analyze huge data using new strategies and advanced techniques in statistics and machine learning.
Business Intelligence Analyst: As discussed previously, you will be responsible for creating dashboards that are presented to the stakeholders so your job is very crucial. You have to take care of visualization and communication in your reports that should be understood by any background investors.
Research Scientist: Your job as a research scientist is to develop, create and introduce new techniques in data mining that can be of help to decode big complications.

Your Responsibilities in Respective Roles

As a data analyst:

You will be doing hypothesis testing of the data and creating new models.
You will be bringing out conclusions and helping business decisions in most cases.

As a data mining specialist:

You will observe the new trends in the data and extract their patterns.
You have to deal with lots of data in the meta form and present it in a clear and easy format to the business decision boards.

Recruiting Companies That Hired These Professionals.

Technology Companies:

Amazon
Google
Microsoft

Financial Services Companies:

JPMorgan Chase
Goldman Sachs

Healthcare Companies:

UnitedHealth Group
CVS Health

Retail Companies:

Walmart
Target

Consulting Firms:

Deloitte
Accenture

Government Agencies:

National Security Agency
Federal Bureau of Investigation

These are just a few examples of recruiters that hired data mining and data analytics professionals. There are openings for these professionals now and then in the MNC and other sectors frequently.

Who Can Get Into These Fields?

Graduates in relatable fields such as computer science, engineering, and economics are eligible to get a job in data analytics and data mining fields. You may find many companies offering entry jobs with training.
Other background graduates also can enter into these professions. Make sure you learn the subject and skills through courses and boot camps and other stuff as mentioned above.
IT Professionals can grab these field roles by layering a few more learnings about data analytics and data mining on top of their existing skills of data visualization, database management, etc.
Data-driven careers are in demand so, if you are planning on changing careers to these fields you can do that by leveraging your experience and by adding these new techniques.
If you are already a professional working as a Data analyst or Business analyst, you can deepen your skills in data mining and develop your career.

Recommended Read For Data And Mathematics Enthusiasts

Salary Differences Between Data Analytics vs Data Mining

The salaries of data analytics and data mining professionals in India can vary depending on a variety of factors such as experience, skills, location, industry, and organization size. However, here are some general salary ranges for data analytics and data mining professionals in India based on industry reports and job portals:

	Entry level	Experienced
Data Analytics	INR 4 to 8 lakhs per annum	INR 10 to 20 lakhs per annum
Data Mining	INR 3 to 6 lakhs per annum	INR 8 to 18 lakhs per annum.

Variety of Subjects You Will Cover in Data Analytics Vs Data Mining:

These are just a few examples of the topics that you may learn in a data mining course. The specific topics covered will depend on the course or program that you choose to pursue.

Examples of Data Analytics vs Data Mining.

Data Analytics:

Number of visits to a website or any page
Collection of Feedback on emails, etc
Summary of Social media engagement through post comments, likes shares etc.

Based on these data points, they try to solve your issues and make the business more efficient.

Data Mining:

Display similar products or services you already purchased.
Showcasing a group of products purchased by a large group of people.

Based on your previous watch history or purchase list, they understand your behavior and try to persuade you by showing similar products around.

Also Read: Data Analytics Courses in Noida

Summary Table of Data Analytics vs Data Mining

Data Analytics	Data Mining
This is to analyze data insights and trends and use that information to make better future decisions.	This process is to dig deeper and gather more unknown information and large data sets to analyze relationships.
This system uses statistical data to analyze.	It takes algorithms to discover patterns and relationships in the data
It is used to analyze customer behavior, trends, and other operations.	It is used to detect fraud, spam emails, etc, and to recommend systems.
Typically uses structured data that has already been cleaned and processed	Often deals with raw, unstructured data that requires preprocessing and cleaning
Commonly used tools include spreadsheets, BI tools, and statistical software like R and Python	Commonly used tools include data mining software like RapidMiner, WEKA, and Orange
Majorly used in business marketing and finance because here so much data is available and can be used for informed decision-making.	It is often used in scientific research, healthcare, engineering, and other areas where large amounts of data need to be processed and analyzed
Can involve data visualization to communicate insights in high demands.	Can involve visualization techniques such as heat maps, scatterplots, and network diagrams to explore data and identify patterns
Skills required include statistical analysis, data visualization, communication, and critical thinking	Skills required include programming, machine learning, data cleaning and preprocessing, and data visualization

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FAQs

Q1. Which is better, Data Analytics vs Data Mining?

It’s like comparing gold and diamonds. Data-driven fields are always in demand and are going to be more at peak in coming years. Well, the difference between data analytics and data mining is the techniques and tools that go into each process. In bird’s view, data analytics is looked at as the mainstream subject, and data mining is considered a sub-category in it.

Data analytics is used to collect, analyze and make informed future decisions for the business whereas data mining is the process that collects large sets of data and brings out unknown patterns and relationships between them. This data mining process uses more algorithms and helps in detecting fraud and spam too. Majorly research and healthcare sectors have complex data to deal with so this kind of organization looks for data mining roles. Overall, both fields are great choices and in demand for the next decades. Choose based on what kind of analysis system you wanna be part of.

Q2. Is data mining part of data science?

Data mining is considered mildly a part of data science because it deals with techniques and methods to work with data. Data science is a skillset that has computer science and statistics and data mining subjects which is very helpful in bringing out insights and patterns from large sets of data. Data science is used for cleaning, collecting, and visualization of data whereas data mining is used to extract relationships between similar data and identify frauds, and that can be used in decision-making. They are different purposes but data mining can be in the data science pipeline.

Q3. Is data mining easy or hard?

Yes, data mining can be a little difficult in job sites because of the complex data sets given to analyze and the techniques and algorithms can get confusing sometimes. But, if you have the right skills, it is easy to deal with. Having patience while cleaning and preprocessing before analysis is the key. Learning the machine learning techniques and programming beforehand can be of great help too. Anyways, the difficulty level comes from various reasons like complexity, expertise, and more. The right skills, techniques, and tools can help in discovering insights and patterns with ease.

Q4. Is data mining math?

Data mining itself says it has data that means numbers and statistics. Yes, it has a lot of maths to deal with on an everyday basis. Collecting data, and analyzing and extracting meaningful information from it is all done in statistical methods, probability theory, linear algebra, and other mathematical concepts. Data mining involves an algorithm to identify patterns, insights, and relationships between large data sets. The techniques involved in data mining such as clustering, classification, and regression are all done with mathematical principle operations. Along with maths, programming like Python is also needed for data mining. Overall, data mining is majorly dealt with maths in hands.

Q5. What are the three types of data mining?

Clustering – Grouping the same set of data is called clustering. Similar preferences and more sales on particular products can be identified through this process. Thus, targeting those groups can improve business and bring in more sales, and gives the organization more clarity on how to target a particular set of audiences.
Classification – This process is used to divide the data into different classes or categories. So that helps in identifying spam and fraud insights.
Regression – This type of mining is used to identify the relationship between the set of data. It uses various kinds of multiple linear regression, quadratic regression, etc to create the link between variables which in turn is set to identify their relationship. For example, some of the relationship sets could be customers’ age and factors that influence them to buy the product.

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