Without Data You are just another person with an opinion!
Today, data is growing at a pace which is faster than ever. And that is why it has become essential for everyone to understand these two terms The meaning of the word Science and mining are poles apart and it’s different in its own sense.
But, the catch here is that when the prefix ‘data’ is added before them they form a close association. It is confusing for many to differentiate between the two.So to all the confused souls(Not so confused too can read :P) out there, I am providing this brief guide that will make it easy for you to understand the significance of the terms.
With the increasing amount of data, several ways have been introduced to handle and process it. And the ways to consider to process data are Data Science and Data Mining.
For starters, let’s get an idea of what these two terms are:
Data science: It is a field of study which includes big data analytics, data mining, predictive modeling, data visualization, mathematics, and statistics, behavioral/social science, etc. It is the process of collection of data, analyzing it and making decisions with the help of it. Data scientists create several products and applications based on data and that deals with it.
Data mining: It is about finding meaningful information in a dataset and using this information to uncover future hidden patterns. It is an important step that often includes analyzing the vast amount of historical data which was obscure and unknown.
Before we come to steps I want to tell you that :
Flipkart is looking for data science professionals!
Starbucks is continuously growing because of Data Science!
In Data science, following are the steps:
1. Gathering data– The first step in the process is to gather data. It can be structured, unstructured or semi-structured.
2. Data munging– Once you’ve got your data, it’s time to work on it. The ‘raw’ data is cleaned and transformed into an understandable format to get the most value out of it. This is probably the longest step. Data scientists report data cleaning is about 80% of the time spent on the whole process.
3. Analyzing data– After cleaning the data, now is the time to analyze it by applying algorithms and necessary statistical models.
4. Data visualization– When large amounts of data are to be dealt with, building visualizations or graphs are the best way to explore and communicate results.
5. Producing predictions– Machine learning algorithms help you getting insights and predicting future trends. More than just creating predictions, this step can help you build new products and processes.
6. Recapitulate– Insights help to develop more features so as to continuously improve model outputs and deliver timely performance and accurate results.
In Data mining, following are the steps:
1. Integrating data– The first step is to collect and combine data from all different sources.
2. Selecting data– Not all the data gathered is useful, so in this step, we select only the data which is useful for data mining.
3. Data cleaning– The selected data may contain errors, missing values, and inconsistency that needs to be cleaned. Different techniques and tools are required in this process.
4. Data transformation– Smoothing, aggregation, normalization, etc are some techniques used to transform data into an understandable format.
5. Data mining– Now is the time to apply techniques like clustering and association analysis for the process of data mining and to discover interesting patterns.
6. Pattern evaluation– Removing redundant patterns to avoid confusion and analyzing remaining patterns is an essential step in this process.
7. Using the discovered knowledge– The final step in this process is to make proper use of the knowledge that is discovered in this process to make decisions.
Still not clear?Read Everything You Need to Know About Data Mining and Data Science to clear away your doubts.
- The word data science’ has been around since the 1960s, whereas the term data mining became widespread amongst the database communities in the 1990s.
- Data science is an area, and Data mining is a technique.
- Data science focuses on scientific study and data mining focuses on the business process.
- The purpose of data science is building predictive models, social analysis, unearthing unknown facts, and the purpose of data mining is to find information or facts previously unknown or ignored.
- Data science is multidisciplinary which consists of data visualizations, social sciences, statistics, data mining, natural language processing, but data mining is a subset of data science.
- Data science deals with all kinds of data whether structured, unstructured or semi-structured and data mining deals with mostly structured data.
- Another name for data science is data-driven science, and for data mining is data archeology, information harvesting, information discovery, knowledge extraction.
- Data science aims at building data-centric products for an organization, but data mining aims at making available data more usable.
In this article, we discussed the key differences between data science and data mining and in what context they should be used to get the maximum output. You might be well versed with these two terms now.
Do stay updated for related articles on similar topics and please mention what I missed out in the comments section.