Difference Between Data Science vs Data Mining
Data Mining is about finding the trends in a data set. And using these trends to identify future patterns. It is an important step in the Knowledge Discovery process. It often includes analyzing the vast amount of historical data which was previously ignored. Data Science is a field of study which includes everything from Big Data Analytics, Data Mining, Predictive Modeling, Data Visualization, Mathematics, and Statistics. Data Science has been referred to as the fourth paradigm of Science. (the other three being Theoretical, Empirical and Computational). Academia often conducts exclusive research in Data Science.
Historical Perspective
Before we move to the technical descriptions let’s have a look at the evolution of the terms. A historical investigation will clarify how the terms are used currently.
- The word ‘Data Science’ has been around the 1960s but back then it was used as an alternative to ‘Computer Science’. Presently, it carries a completely different meaning.
- In 2008, D. J. Patil and Jeff Hammerbacher became the first individuals to call themselves ‘Data Scientists’ in order to describe their role at LinkedIn and Facebook respectively.
- In 2012, Harvard Business Review article cited Data Scientist as the ‘Sexiest Job of the 21st Century’.
- The term Data Mining has evolved parallelly. It became prevalent amongst the database communities in the 1990s.
- Data Mining owes its origin to KDD (Knowledge Discovery in Databases). KDD is a process of finding Knowledge from information present in databases. And Data Mining is a major subprocess in KDD.
- Data Mining is often used interchangeably along with KDD.
Although these names have come into picture independently, they often come out as complementary to each other as, after all, they are closely related to data analysis.
Head to Head Comparison between Data Science and Data Mining (Infographics)
Below is the Top 9 Comparison of Data Science and Data Mining:
Example Use Case
Consider a scenario where you are a major retailer in India. You have 50 stores operating in 10 major cities in India and you have been operational for 10 years.
Let’s say, you want to study the last 8 years’ data to find the number of sales of sweets during festive seasons of 3 cities. If that’s your objective, I would recommend you employ a person with Data Mining expertise. A Data Miner would probably go through historical information stored in legacy systems and employ algorithms to extract trends.
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Consider another case where you want to know which sweets have received more positive reviews. In this case, your sources of data may not be limited to databases, they could extend to social websites or customer feedback messages. In this case, my suggestion to you would be to employ a Data Scientist. A person employed as a Data Scientist is more suited to apply algorithms and conduct this socio-computational analysis.
Key Differences Between Data Science and Data Mining
Below is the key difference 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 a 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.
Note
- Data Science and Data Mining should not be confused with Big Data Analytics and one can have both Miners and Scientists working on big datasets.
Data Science vs Data Mining Comparison Table
Below is the comparison table between Data Science and Data Mining.
| Basis for comparison | Data Mining | Data Science |
| What is it? | A technique | An area |
| Focus | Business process | Scientific study |
| Goal | Make data more usable | Building Data-centric products for an organization |
| Output | Patterns | Varied |
| Purpose | Finding trends previously not known | Social analysis, building predictive models, unearthing unknown facts, and more |
| Vocational Perspective | Someone with a knowledge of navigating across data and statistical understanding can conduct data mining | A person needs to understand Machine Learning, Programming, info-graphic techniques and have the domain knowledge to become a data scientist |
| Extent | Data mining can be a subset of Data Science as Mining activities are part of the Data Science pipeline | Multidisciplinary – Data Science consists of Data Visualizations, Computational Social Sciences, Statistics, Data Mining, Natural Language Processing, et cetera |
| Deals with (the type of data) | Mostly structured | All forms of data – structured, semi-structured and unstructured |
| Other less popular names | Data Archaeology, Information Harvesting, Information Discovery, Knowledge Extraction | Data-driven Science |
Conclusion
So here you go! I am sure now you are more aware of what the key differences between the two are and in what context the two should be utilized. One thing you should remember is there are no formal and precise definitions of Data Science and Data Mining. There are still debates going on amongst the academia and the industry as to what constitutes an accurate definition. However, everyone is on the same page with respect to the high-level differences and descriptions of the two terms which we explored in this article.
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