This is the modern world. A world of a new vision and new opportunities. World of big data!!!! Analytics along with big data has virtually changed every industry and business around us. Big data does not only mean the big slice of information, it actually means the complexity of the information. And to make this complex data understandable and useful, data mining and data analytics is used. You might be curious about what is data mining and data analytics. Are these two terms the same? If not, what's the difference? Not to get confused as this article will definitely clear all your doubts.
Let’s get started with this simple clarification.
Every electronic device leaves trails of information when used. Information like location, performance, or data captured. The online communication of people using various devices leaves huge information trails to gather a big amount of data. The huge data captured increases the complexities of information. Data alone has zero value unless it is extracted from an insightful context. This extracted piece of information is the actual valuable asset. So, to make data intelligent and useful, Data Mining and Data Analytics are used.
The difference between data mining and data analytics is vital and it plays a major role in taking all data-driven decisions and increasing the success rate of the assignment.
In this article, we will understand what is data mining and data analytics. Let us first understand the difference between data mining and data analytics; they are also known to be the subsets of business intelligence. Let us also discover data mining and data analytics separately.
What is Data Mining?
Data mining is the process of withdrawing meaningful information within large data sets with the purpose of getting information from big data stores through automated methods. The useful patterns and trends of the data are observed and processed.
The conversion of raw data into meaningful information based on variables, inputs & features is data mining. Various algorithms are filtered from the cores in the statistical format from artificial intelligence, machine learning, and computer science, in order to develop models from data. The formation of the problem, understanding the data, building the data model, predicting the results, and building the process to apply the methods to the model. Descriptive analysis is also a part of data mining.
Now that you know what is data mining, let us understand what is data analytics.
What is Data Analytics?
Let’s dig into the basic question that strikes everyone: What is data analytics? Yes, that is one of the curious questions many want to know and understand. Data analytics is the science that delivers meaningful information from the collection of raw data. It analyses raw data to find various trends and find solutions from big data.
The developed systems and software examine the large data. This has proved very catchy for many business intelligence and application-based companies. Data analytics can also be processed by analyzing data from any particular domain, for instance, website analytics.
For some, it can be a process of expanding business possibilities to specific content areas, such as market, supply chain, service, sales, distribution, etc. The statistical and mathematical analysis of data that connect the arrays, and segments and predicts the futuristic majors and outcomes, is all described by data analytics in statistical methods.
Data analytics helps in the integration of structured and unstructured information from real feeds and reports, opening new ways of insight into business structures.
What is Data Mining Techniques?
Here are some Data Mining Techniques:
Classification Analysis
Classification analysis is used to allocate the explicit data for different classes. It is used to retrieve specific data and metadata information.
Association
Association in data mining refers to the process that identifies the connection of definite variables from big data stores.
Outlier Detection
Outlier detection is the process where data is identified in the database that does not match the expected data pattern.
Clustering
Clustering is bringing together a series of various data points based on their characteristics. Data mining divides the data into subsets that manage to define more accurate decisions in terms of demographics. Clustering is the technique used to represent the data visually.
Regression analysis
Regression analysis is the process of analyzing connections among various different variables.
What are the Types of Data Analysis?
In today’s data-rich time, figuring out how to analyze and extract true value from business insight is the real formula for primary success. Let's begin by understanding what are the types of data analysis. There are two kinds of analytics, qualitative and quantitative analysis.
Qualitative analysis
Analysis which describes the product characteristics. It strongly highlights the quality of the product.
Quantitative analysis
The quantitative analysis basically emphasizes the numbers. The Analysis of a product, by means of complex mathematical and statistical modeling. It focuses mainly on the numbers.
Are Data Mining Related to Data Analytics?
Are data analytics and data mining the same? Let’s figure out the basic difference between data mining and data analysis.
Which Software is used for Data Mining?
The main purpose of data mining software is to extract meaningful information from the largest data stores and transform it into structured data form that gives a clear understanding of the huge data. Data mining helps companies to get clear future views or insights from huge data volumes and transform them into actionable information.
The data mining system has various advanced functionalities that solve various data complexities in a different manners. Many products have different methods that validate the results. Hence its completely upon your requirement to choose any specific software as per your project requirement. So here is the list of the software that is used for data mining:
1. SAS Data mining
SAS had developed a statistical analysis system. It is the best-known program that offers graphical UI for nontechnical users. It is one of the widely used and best data mining software.
Features:
Big Data is analyzed by the SAS data mining tools.
It is an appropriate tool for data mining, text mining & optimization.
SAS offers highly scalable memory processing architecture.
2. Zoho Analytics
Zoho Analytics is a program based on business intelligence and analytics platform. It allows the user to create meaningful future prospect and report through dashboards and compelling visuals. The AI-powered assistant enables users to bring innovative and intelligent solutions from the smart dashboard reports.
Features:
The wide array of visualization charts, reports, summary views, pivot tables, and databases.
Expanded analytics using ML, AI, and NLP.
White label BI portals.
3. R-Programming
R is a graphic and statistical computing language. It is widely used for big data analysis.
Features:
Efficient in data handling and huge storage facility.
It provides a graphical representation of data analysis through a screen display.
It provides data analysis tools that are used for integrated data collection.
4. Rapid miner
Rapid miner is a free data mining tool. It is used for data mining and model deployment. It is mostly used for predictive setup analysis.
Features:
Integrates with in-house databases.
Big data predictive analysis.
Remote analysis.
Data filtering and data merging.
Build and validate predictive models.
Reports notifications.
5. Oracle BI
Oracle BI is an open-source machine learning and data visualization for the beginner or an expert.
Features:
Interactive data visualization.
Oracle presents Interactive data exploration for qualitative analysis.
It offers a huge range of perks to data mining from various different data sources.
Are big data analytics and data mining the same? Both data analytics and data mining are involved in handling huge amounts of data for business purposes. However, both data analytics and data mining are two different sets of operations. Thus they both are different.
FAQ
Does data mining require coding?
Data mining does require coding. Programming languages like Python, R are used to visualize and analyze data.
Who gets paid more data scientist or data analyst?
The average salary of a Data Scientist in the US is $90,000 per annum. The average salary of a data analyst in India is 5 Lac rupees per annum. In India, the average salary of a Data Scientist is 8 Lac rupees per annum.
How difficult is data mining?
Data mining is not as hard as it sounds. If you want to pursue a career in data science, study the various data mining course benefits from fingertips. It will clear your perspective thus suggesting you right career path.
How do I get a job in data mining?
Follow the steps to avail your position in the field of data science.
Get an undergraduate degree
Gain employment as Data Analyst
Pursue an advanced degree or program in data science
Get hired as a data mining engineer/specialist
What are the best text-mining tools?
Monkey Learn – user-friendly text mining
IBM Watson – AI platform
Google Cloud NLP – Custom machine learning tool
Meaning Cloud – APIs for text analysis
What are the 5 types of data analytics?
The 5 types of data analytics are Prescriptive, Predictive, Diagnostic, Descriptive and Cognitive Analytics.
What are 4 types of data?
The 4 types of data are
Nominal data.
Ordinal data.
Discrete data.
Continuous data.
What are 6 types of data analysis?
The 6 types of data analysis are
Descriptive Analysis.
Exploratory Analysis.
Inferential Analysis.
Predictive Analysis.
Causal Analysis.
Mechanistic Analysis.
What is a data analytics career path?
If you have the zest to become a data analyst you have to fulfill your dream by learning from the right place. At fingertips, you will be given career guidance along with placement support.
As you start off as a data analyst position you can progress further and head to become a senior data analyst and later also achieve a data scientist or chief data technology officer position.
Is data analyst the highest paying job?
Data Analyst is amongst the highest-paying job. Data analyst salary increases with expertise and on-field practical experience. An entry-level data analyst with less than 3 years of experience data analyst can draw almost 5 Lakhs per annum in India.
What are in-demand data analyst skills?
A data analyst should be proficient in the following data analytic skills
Data Visualization, Data Cleaning, MATLAB, R, Python, SQL and NoSQL, Machine Learning, Linear Algebra and Calculus, Microsoft Excel, Critical Thinking, Communication
What are the best data analytics tools?
The best data analytics tools that are highly in demand are:
R and Python, Microsoft Excel, Tableau, RapidMiner, KNIME
Power BI, Apache Spark, QlikView, Splunk
Conclusion
Hope this read helped you understand the difference between data mining and data analytics. They both have their own significance and need different expertise. Both data mining and data analytics are data-driven and hold great importance in the big data world. In any area, different expertise will lead to a successful career. If you are curious to learn data science, contact fingertips and get meaningful information on your future prospects. The practical hands-on assignments, real-time industry exposure, and expert mentors will surely be of great assistance to decide your career path.
