Introduction to Data Science vs Data Mining
Today’s technical universe and its ongoing development are now dealing with data related to different fields. Enterprises realized that they could use this data for their progress.
An article from Forbes states that today we have humongous data in hand. In the same article, by the year 2020, new data to be dealt with will be about 1.7 billion per second for all human beings.
When analyzing this data, different terminologies were evolved; these terms include Data Science and Data Mining.
Let’s have a quick overview of Data Science and Data Mining:
What is data science?
Data Science is a field of study dealing with both structured and unstructured data. It is associated with data cleansing, preparation and Data Analytics, predictive modelling, and data visualization.
Data science includes logical reasoning, mathematics, and statistics related to data. Data Science is needed to capture data in a way that helps organizations make proper decisions leading to their growth.
It altogether gives a different viewpoint of looking at things. To be more simple, data science is a cloud of several techniques used for dealing with structured and unstructured data.
Data scientists are responsible for creating models, optimizing models, and deploying them, leading to creating data visualizing reports useful for the stakeholders.
It helps make the data products and many other data-based applications that deal with data in a manner that conventional systems would not be able to do. You can explore your knowledge at Data Science Online Training at 3RI Technologies.
So Data science is a combination and summation of analyzing data with statistics, algorithm development, interpret data to solve complex problems. The main objective is to provide a piece of meaningful information from a large dataset. the information can minimize the risks for a company.
On the whole, data science comprises the following steps:
Accumulation of the data: The process begins with acquiring the structured, semi-structured, or unstructured data.
Processing of data: Now, the data is worked upon. It is cleaned to gain maximum insight through it. Processing of data is a tedious task. It requires a maximum work period.
Data Analysis: After processing, now is the time for its analysis. It is modulated, and algorithms are used for the study of the converted data.
Data Visualization and predictions: For humongous data, visualization is essential to gain output. Through visuals, e.g., graphs, the relevant information is gathered to get the knowledge of trends. Knowledge of trends allows predicting the future, which leads to improving the performance of the business.
The data science training involves-
- The idea of reporting tools/visualization
- Deep Learning
- Data Visualization
- advanced Python applications
- Machine learning
- The quantitative technique- needs to gain knowledge in a deeper level of information
- R language
Usage of Data science
With Data science you can do the followings:
- Descriptive analysis
- Predictive Analysis
- Prescriptive Analysis
- Machine understanding
What is data mining?
The process of discovering patterns in the massive structured datasets is known as Data Mining. Data Mining involves methods linked with all machine learning, statistics, and database systems.
Data considered here is mostly structured, which is huge. By this, the data is gathered, which was at first not known and not utilized. But afterwards, the gathered data is used to make business decisions.
It contributes to knowledge of formerly unknown patterns. So, in all, we can state that data scientists and machine learning experts use data mining as a technique to convert large data sets to structured and more usable forms.
Like Data Science, Data Mining also comprises data cleaning, pattern prediction, statistical analysis, data visualization, etc.
Data Mining comprises the following steps:
Integration and Cleansing data: At first, the data is gathered from different sources, and the irregularities in the data are removed.
Data for Data Mining: The next is to extract the operational data from all the integrated information that is a source for Data Mining.
Transformation of data: Data obtained may have some faults; it may be inconsistent and has some absent values, which require cleaning. The data is then normalized into a usable format.
Data Mining: Here is the place where the patterns are analyzed that are present in the data. After Identifying relevant patterns, the other data is eliminated to avoid cluttering.
Data Usage: The patterns discovered during Data Mining are used to extract well-informed decisions. This representation is given to the stakeholders in the form of graphs, tables, etc.
Below given are the significant differences between data science and data mining.
Data Science works with both structured data sets, whereas Data mining is confined to structured data sets.
The Focus area of Data Mining is business processes to make the data used to be competent in the market; on the other hand; Data Science is a study for building data-centric products.
Data Mining is an activity that is a part of the Knowledge Discovery in Databases (KDD) Process, while Data Science is more like a study of Applied Mathematics or Computer Science.
Data Science has a broader perspective as compared to Data Mining.
Some activities as steps in Data Mining such as statistical analysis, correction of data flaws, and pattern recognition intersect with Data Science. So, we see that Data Mining becomes a subset of Data Science.
Data Science has more general use, whereas Data Mining is restricted to data patterns.
Thus, we can conclude that Data Science, as data-driven science, is a field or a wide area that includes obtaining and analyzing data and gaining information.
Data Mining is referred to as data discovery. It is a method or technique of data analysis that focuses on extracting usable information from a dataset and using it to dig up the covered patterns. You can Enroll in Python Online Training. We cover all concepts of data science with python.