The drastic increase in the use of digital statistics has resulted in a knowledge and information revolution. Today, gathering insights and information from available datasets is the key to strategy building and research.
Data analysis and data mining are the important subsets of business intelligence. So, numerous tech-loving youngsters are wondering which subject to focus on.
Data analysis and mining: which is for you? If you have difficulty deciding which career path applies to you, we’re glad to help you find the answer!
Overview
Data analysis refers to a process of visualization, modeling, transformation, cleaning, and extraction of datasets. The goal is to extract valuable and significant information which can help derive conclusions and form decisions.
Data mining is a process used to transform raw statistics into valuable statistics. This process relies on research, and many businesses follow it to exploit untapped statistics, making it meaningful information.
Generally, DA is a superset of DM, divided into confirmatory info analysis, descriptive statistics, and exploratory info analysis in statistical systems.
Key Differences
Below is a core comparison table that highlights significant differences between these two topics and their distinctive identities.
| Data Analysis | Data Mining | |
|---|---|---|
| Team size | a team | can be a single person |
| Skills required | Information processing, mathematics, statistics, analysis tools, etc. | Databases, statistics, programming languages, language processing, etc. |
| Data structure | Datasets can be small, medium, or large | The dataset is structured and extensive. |
| Data quality | Always clean and high-quality | Sometimes poor |
| Hypothesis testing | Require preconceived hypotheses | Not require preconceived hypotheses |
| Forecasting | Use DM forecasts to reach conclusions | Forecast datasets through clustering, deviations, correlations, and classification |
| Responsibilities | Responsible for proposing hypotheses, testing, explanations, and developing models using analytical tactics. | Responsible for discovering and extracting meaningful structure and patterns in available info. |
| Career opportunity | Social media, business, sales, finance, etc. | Marketing, social media, business, computer science, etc. |
Team Size
A single professional with superior technological skills is enough to undertake information mining efficiently. They can pick up information ready for deep analysis with the correct software. This stage doesn’t require a large team yet.
Meanwhile, information analytics needs a large team of professionals. These specialists have to go through and assess the information, determine patterns, and give conclusions.
Skills Required
Here is the skill set that a DM worker should have:
- Deep learning in natural language processing
- Business intelligence knowledge
- Algorithms and dataset structure knowledge
- Public presentation skills
- Up-to-date with industry trends
- Analysis tools, like SAS or NoSQL
- Programming languages, like JavaScript or Python Programming
- Machine learning
- Comprehensive statistics knowledge
- On-the-job experience in using operating systems (Windows, Linux, etc.)
Now, let’s see which critical skills a DA must acquire:
- Excellent communication skills
- Comprehensive knowledge of programs like Python or R
- Presentation skills
- MS Excel
- Visualization expertise
- Problem-solving and critical thinking skills
- Machine learning
- Analysis tools, like SAS or NoSQL
- Comprehensive mathematics knowledge
- Strong awareness and understanding of the latest industry trends
Data Structure
Data mining specialists build algorithms to figure out a structure in the statistics systems, which will go through an interpretation process. It relies on scientific and mathematical concepts, making it more straightforward for organizations.
Meanwhile, DA specialists can perform analytics on unstructured, structured, or semi-structured statistics. They are not in charge of generating algorithms like DM professionals.
Data Quality
DM experts will receive and work with large datasets, extracting the most helpful info. Since they use free and enormous datasets, the information quality they work with is raw and not always good or top-notch.
Yet, DA experts are responsible for gathering and assessing statistics. Usually, an analytics team will work with high-quality raw info that is clean and useful.
Hypothesis Testing
One of the key differences between these two fields is that DM doesn’t require preconceived notions or hypotheses before handling the info.
A data mining expert will develop a statistical or mathematical model, relying on what he derives from the available information.
Meanwhile, data analysis specialists test a hypothesis and extract meaningful insights to conduct their research.
Forecasting
DM professionals will forecast information through clustering, deviations, correlations, and classification. On the contrary, DA workers lean more toward reaching decisions and conclusions from available information.
Responsibilities
While DM is in charge of identifying and extracting structure and patterns in the statistics systems, DA specialists develop models and test the hypothesis with the help of analytical methods.
DM workers’ duties are in conjunction with how they collect and present information. On the other hand, a DA team’s duties are more about info interpretation and less about building algorithms.
Career Opportunity
The data mining field gives you the chance to work as:
- Online marketing analyst
- Social media analyst
- Computer scientist
- Business analyst
- Statistics analyst
The data analysis field also offers plenty of career opportunities, such as:
- Organization product analyst
- Social media analyst
- Machine learning analyst
- Sales analytics
- Budget analyst
- Business analyst
- Financial Analyst
Data Analysis or Mining?
To conclude, we believe that DA will offer more opportunities and room for tech-loving grads because DM is quite restrictive to some specific tasks.
DM is the foundational process, while DA is a step further. DA is a thorough process required to reach decisions.
You should focus on both subjects, not just DA. Learning and improving yourself in both topics will undoubtedly bring you more employment opportunities.
You don’t have to worry about the future outlook of these two fields since they will remain trendy until humans stop dealing with digital statistics.
Conclusion
After walking around the overview, unique identities of the two subjects, and different subcomponents between them, the questions ‘data analysis or data mining?’ should be easier now.
Each term has its specific applications, yet they both function synergistically to process complex statistics into meaningful insights.
