Data Mining Vs. Data Analytics: Difference between Data Mining & Data Analytics

There is no denying that data on all corners surround us. Our generation has been fortunate enough to see the rise of the internet and all the benefits which come with free and accessible information sharing. This ease of information sharing has led to an exponential surge in the sheer amount of raw data generated.

To put things into perspective, all the clicks made by you, the websites you visit, the amount of time you spend on each of the websites you visit, your online presence, etc., are data that you generate. Now, in its raw form, this data is unusable. Nothing of meaning could be extracted out of the trail of data each of us leave.

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However, with the right tools and computing power, this data can then be processed and converted into meaningful insights that drive big corporations’ decisions and dictate their profits. The ones who hail the data to be the next industrial revolution are not wrong.

Must Read: Data Science Vs Data Mining

In this world where data is everything, new fields pertaining to catering specific niches of data must come into the picture. People already serving in these fields throw terms like Data Science, Data Mining, Machine Learning, Deep Learning, Data analytics, etc. quite loosely. For those not in these fields, gaining a basic understanding of these terms can be quite confusing. 

Data Mining and Data analytics are crucial steps in any data-driven project and are needed to be done with perfection to ensure the project’s success. Adhering to both fields’ closeness, as mentioned earlier, can make finding the difference between data mining and analytics quite challenging. Before we are in a state to understand do a data mining vs. data analytics comparison, we must first closely understand the two fields very closely.

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Data Mining

Data mining is a deliberate and successive cycle of distinguishing and finding shrouded examples and identifying useful data in an enormous dataset. It is otherwise also called “Knowledge Discovery in Databases.” It has been a trendy expression since the 1990s. But only in the recent decade has this field really gained traction. The improvement in computing prowess has allowed data mining to become streamlined and mainstream.

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Data Analysis

Information Analysis, then again, is a superset of Data Mining, which includes removing, cleaning, changing, demonstrating the data to reveal significant and valuable insights that can help determine the way to proceed forward and make choices pertaining to the company in question. Data Analysis as a cycle has been around since the 1960s. It has only recently come into the mainstream and has proven to be an indispensable tool in any significant global player’s arsenal.

Now that we know the basics of data mining and data analytics, we are positioned to pit data mining vs. data analytics head to head and understand all the nuances and differences between data mining and analytics. 

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Data Mining vs Data Analysis: Skillset

When it comes to the discussion of Data Mining vs Data Analysis in terms of the skillset there is a lot of difference between data mining and data analysis. Let us go through each domain and see in-depth what is required from each profession, we will then have a better insight on the whole topic of Data Analysis vs Data Mining.

Skills required in Data Mining:

1. Knowledge of operating systems, especially Linux: Data mining engineers usually work on architectures that would set the base for data analysts to build their models. Knowledge of Linux is a must, as most VMs(Virtual Machines) require a Linux-based system to operate in a pipeline.

Linux is a very stable operating system for working with large datasets. Having experience with Spark, deploying a distributed machine learning system on it, and the ability to integrate it with Linux is a bonus for a data engineer.

2. A programming language: There are a lot of languages that are used by data mining engineers out there. Python, R, to name a few. These languages allow you to perform statistical operations on large datasets and allow you to draw an inference from the datasets. Python is a language based on C that works as both a scripting language for the purposes of web development but also offers a huge variety of libraries for data mining, data analytics and data visualization.

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3. The R programming language is a free and open-source tool for statistical computation and graphical analysis, and R analytics refers to the analysis of data by means of this language. This language is widely used in the fields of statistics and data mining.
 

4. Data Analytics tools: A data mining engineer needs to know enough about data analytics so that an architecture can be designed for a data analyst to build the models on. Data Science depends on statistics and programming and here is where SAS comes in. The SAS software package was created by the SAS Institute for use in a variety of statistical applications, including data management, advanced analytics, multivariate analysis, business intelligence, forensics, and predictive analytics.

Skills required in Data Analysis:

Probability and Statistics: Data Science and data analysis are founded upon the pillars of probability and statistics. When trying to anticipate the future, the theory of probability is a great asset. Projection and estimation are crucial components of data analytics. We use statistical approaches to estimate values for use in further analysis. Therefore, statistical approaches rely heavily on probability theory. Data is the foundation of probability and statistics.
 

Data Visualization: Learning anything new from data is just a small fraction of what data analysis entails. To better influence business choices, it is also essential to build a narrative using these insights. This is where the use of data visualization becomes useful.

As a data analyst, one may make their results more accessible by using charts, graphs, maps, and other visual representations of data. Learning visualization tools like Tableau is a common way to hone data visualization abilities. With this standard in business software, one can easily convert their analysis into dashboards, data models, visualizations, and business intelligence reports.

Econometrics: Econometrics is a branch of economics that makes use of statistical and mathematical models to better foresee potential future outcomes. It is important for data analysts to have a firm grasp of econometrics.
 

A programming language: A data analyst without doubt has to be fluent in a programming language suitable for statistical programming. If you want to do more sophisticated analysis than excel allows, you’ll need to learn a programming language, Python and R being the most popular in the industry.

Though both the fields are under the same domain of Data Science, the above mentions the difference between data mining and data analysis. Having mentioned the differences in the skill set of data mining vs data analysis. Let us move to the topic of data analysis vs data mining in terms of fundamental differences in the next section.

Difference Between Data Mining and Data Analytics

Although data mining and data analytics are two different words in the field of data, they are sometimes used in place of the other. The usage and the meaning behind the terms depend highly on the context and the company in question. To set up their individual identities such that you can easily differentiate between the two, you will find the significant contrasting points listed below:

1. Data mining is catering the data collection and deriving crude but essential insights. Data analytics then uses the data and crude hypothesis to build upon that and create a model based on the data.
2. Data mining is a step in the process of data analytics. Data Analytics is the umbrella which deals with every step in the pipeline of any data-driven model. 
3. Data mining shines its brightest when the data in question is well structured. Meanwhile, data analysis can be performed on any data; it would still be able to derive meaningful insights that could help in propelling the corporation to even greater heights.
4. Data mining is tasked to accomplish the main job to make the data that is being used more usable. Whereas, data analysis is used to hypothesize and, in the end, culminate itself in providing valuable information to help in business decisions.
5. Data mining does not need any bias or any notions which are instilled before tackling the data. Whereas, data analysis is majorly used for hypothesis testing.
6. Data mining uses the scientific and mathematical models and methods to identify patterns or trends in the data that is being mined. On the other hand, data analysis is employed to task with business analytics problems and derive analytical models.
7. Data mining usually does not need any visualizations, bar charts, graphs, GIPs, etc., whereas these visualizations /are the bread and butter of data analysis. Without a good representation of the data in question, all the efforts which are put into the analysis of the data would not come to fruition.

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Conclusion

We have been seeing both the terms, i.e., Data Mining and Data Analysis, for a long time. These terms were palpable until the leap in the sheer power computers made it possible for anyone with a computer to jump in and play with data. Both data mining and data analytics are crucial to be done perfectly. Due to the very nature of the two following fields, their names have been used interchangeably by individual business people.

Meanwhile, there are also people present who have appreciated the differences in the areas and made sure to respect the boundaries of the two fields. In whatever camp you might side with, you cannot deny the importance of both in a data-driven world of the 21st century. Another thing set in stone is the skillset required for both of these fields.

It would help if you had different expertise to be successful in either of the areas. You need a more analytical approach to tackle data analytics. In contrast, you need a pattern recognition mindset and a knack for coding to make a name in the field of data mining.

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