Organizations have realized that the amount of data they’re storing possesses a giant value, so they have started employing multiple techniques and recruiting tech experts to exploit that value.
You can encounter these two popular topics among all technical terms.
This article will take you through the seven significant differences between the two subjects and help determine the suitable major for your potential.
Definition
Data science is a field that involves dealing with and using an enormous amount of data to build prescriptive analytics and predictive models.
It focuses on exploiting, analyzing, and checking the models, as well as capturing, and building the models using the available datasets.
This subject is a crossing point of computing and data or a combination of the two fields of Statistics and Computer Science.
On the other hand, data mining is an important technique used for extracting vital knowledge and information from a giant library of statistics.
It generates insight by thoroughly processing, extracting, and reviewing the giant source of information to identify correlations and patterns that can be crucial for the organization.
Key Differences
| Data Science | Data Mining | |
|---|---|---|
| Focus | Conduct social analyses, perform experiments, develop predictive models | Discover, collect, and extract meaningful patterns |
| Tools | Python, Apache Spark, SAS, etc. | Weka, RapidMiner, Orange, etc. |
| Type of data | semi-unstructured, structured, and unstructured | structured, sometimes unstructured |
| Professionals | Programming, pattern analysis & engineering, and AI research | Portray, organize, harvest, and discover meaningful insights |
| Skills | Statistics, programming, mathematics, machine learning | Operating systems, programming languages, and analysis tools |
| Salary | $124,540 | $125,575 |
Focus
Data mining plays a critical role in an organization’s analysis process. Usually, you won’t see people use DM outside a company setting since it’s tailor-designed for helping businesses understand and collect statistics.
Meanwhile, data science focuses on scientific studies. Scientists use those studies to conduct analyses, perform experiments, and develop models.
Tools
Some essential tools that data science utilizes are:
- Python
- Apache Spark
- SAS
- Tableau
- R
- D3.js
- TensorFlow
Data mining primarily takes advantage of the following tools:
- Weka
- RapidMiner
- KNime
- Oracle Data Mining
- Apache Mahout
- TeraData
- Orange
Type of Data
Generally, DM only concentrates on structured datasets, though it sometimes uses unstructured statistics as well.
Meanwhile, DS professionals spend most of a working day working with semi-unstructured, unstructured, and structured patterns.
In this regard, DM is more straightforward since it doesn’t require professionals to deal with all sorts of patterns, whereas DS experts must have comprehensive knowledge and be adept at multiple sorts.
Professionals in the Field
Data miners only have to understand and know how to portray accurately, organize, harvest, and discover meaningful patterns.
Also, data science professionals must be cultured in many areas and skills, such as domain knowledge, programming, pattern analysis & engineering, and AI research.
If you want to work in the DM field, you need to gain some skills and knowledge that DS workers have, yet not as many.
Skills
A DM professional needs to possess a blend of interpersonal, technological, and business skills. When it comes to technical abilities that workers in this field must master, we’d like to mention the following:
- Experience in operating systems, particularly LINUX
- Knowledge of various programming languages, including Perl, Java, and Python
- Familiarity with analysis tools, like Hadoop, SAS, NoSQL, and SQL.
The jobs involved in DS require more complex skills than DM.
Besides a robust foundation in statistics and mathematics, DS specialists must have knowledge and understanding of programming and be adept at sophisticated modeling software.
To sum up, a typical scientist will have to acquire the following skills:
- Statistics
- Programming
- Mathematics
- Processing huge datasets
- Deep learning
- Machine Learning
- Computing and Statistical analysis
Salaries
An average data scientist can earn about $124,540 a year. If you’ve just started your career, your salary for an entry-level position starts at $68,813 a year.
After gaining adequate experience and more advanced skills relevant to the subject, the salary will rise to around $172,579 a year.
Meanwhile, an entry-level position in the data mining field will offer an average salary of $125,575 a year.
You can raise your salary range to $172,085 a year after accumulating years of experience and acquiring more advanced skills.
Which Is For You?
Both domains are riveting and valuable fields to experiment and work in. The question ‘Which is the suitable path for you?’ boils down to your purposes, desired career goals, and personal preference.
When considering which field applies best to you, it’s necessary to determine how broad or narrow your domain is, how much time you’re willing to devote to studying, and what sort of job you expect to do.
If you’re fond of developing yourself in a domain with more specific purposes and goals and not willing to devote a long time studying, DM should be the right choice for you.
The specific set purpose and goal are to collect, organize, and present datasets to identify meaningful insights within them.
You can work in this area as a contracted worker or business analyst, and there will be no need to devote years to classes and courses.
On the other hand, DS should be an ideal way to go if you look to build your career in a field with less specific purposes and goals and don’t mind spending years on particular classes and internships.
Performing experiments to detect unknown facts and patterns while creating data-centered products will allow you to put your creativity into practice and give you chances to study more exciting topics in the future.
Conclusion
Data science or mining? Whichever you opt for, ensure to research and weigh up the benefits of your dream field thoroughly before starting your education.
