Difference between Data Science and Data Mining is going to be very interesting.
There are not so many differences between them, I should say the similarities between them.
Talking about Data Science, I have already discussed What Data Science actually is. If you have not check yet my previous posts, Please go and Check them out, you are missing something. Data Science is an umbrella that contains many other fields like Machine learning, Data Mining, Big Data, statistics, Data visualization, Data analytics etc. Below I have listed those posts, Visit them –
Machine Learning Books are not always so cheap. But we as a student want them for FREE as a PDF. So, I have googled these very well-known books on Machine Learning. Here you will find top Machine Learning books for FREE.read more
In this post, I have listed some well-known books for Data Science. I managed to get it for free from Google Download the PDFs by clicking the images.read more
The answer is Yes, It is very much needed to learn about Tableau. It means a lot to the Data Scientists. It simplifies the visualization of datasets.read more
Difference Between Data Science and Big Data is really complicated. Data Science is a field that comprises of everything that related to data cleaning, preparation, and analysis. Big Data refers to humongous volumes of data that cannot be processed effectively with the traditional applications that exist.read more
Is SHELL needed to be a Data Scientist? the answer is Yes, SHELL ( Shell is the sleeping beauty of Linux ) is Needed for Data Scientists to get the data and to work with that data. Everyone is busy to Learn R or Python for Data Science, learn Shell for Data Science.read more
Is SQL needed to be a Data Scientist? the answer is Yes, SQL ( Structured Query Language ) is Needed for Data Scientists to get the data and to work with that data. Everyone is busy to Learn R or Python for Data Science, but without Database Data Science is meaningless.read more
Now, Let’s talk about What is Data Mining?
Data Mining is the process of gathering information stored in the database that was previously obscure and unknown. This information can then be used to make appropriate business decisions. Machine Learning is the technique that is broadly used in Data Mining. The essential objective of the data mining process is to extricate data from different arrangements of information trying to change it inappropriate and justifiable structures for inevitable utilize.
You look for consistent patterns and / or relationships between variables. Once you find these insights, you validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction.
Main Differences between Data Science and Data Mining –
Data Mining is an activity which is a part of a broader Knowledge Discovery in Databases (KDD) Process while Data Science is a field of study just like Applied Mathematics or Computer Science.
Often Data Science is looked upon in a broad sense while Data Mining is considered niche.
Some activities under Data Mining such as statistical analysis, writing data flows and pattern recognition can intersect with Data Science. Hence, Data Mining becomes a subset of Data Science.
Machine Learning in Data Mining is used more in pattern recognition while in Data Science it has a more general use
Data Science Vs Data Mining Comparision Table
|It is an area.||It is a technique.|
|The main focus is on the Scientific study.||The main focus is a Business process.|
|Its goal is to build Data-centric products for an organization.||Its goal is to make data more usable.|
|A person needs to understand Machine Learning, Programming, info-graphic techniques and have the domain knowledge to become a data scientist.||Someone with a knowledge of navigating across data and statistical understanding can conduct data mining.|
|Data Science consists of Data Visualizations, Computational Social Sciences, Statistics, Data Mining, Natural Language Processing, etc.||Data mining can be a subset of Data Science as Mining activities are part of Data Science pipeline.|
|It deals with all forms of data – structured, semi-structured and unstructured.||It deals with mostly structured.|