Differences Between Data Scientist vs Data Mining
Data scientists are people who create programming code, uses them to form a rich set of combination of statistics and use its knowledge to create and generate business-related insights on data. Data science is, in essence, an interdisciplinary area about systems and processes which extracts insights and knowledge from data in different forms.
Data mining, on the other hand, is the process of discovering and finding patterns in the form of large data sets involving functions at the intersection of statistics, machine learning and database systems. Intelligent processes and extraction tools are used to extract data patterns. The overall goal is to extract relevant information from a data set and transform it into the recognizable structure for further use. It involves data management tools, inference considerations, complexity considerations, interesting metrics, post-processing of discovered structures, etc. The idea is to extract patterns and knowledge from a huge amount of data and not the extraction of data itself. It also supports any application of decision support systems which includes the ones related to artificial intelligence, business intelligence, and machine learning.
The value of data and client confidentiality with respect to security is increasing day by day and therefore it becomes an urgent need to deploy the data scientists as they not only aim to protect your data but also provides meaningful analysis and extractions so as to foster your organization and business with the future trends and how the company can improve from what they are today by maintaining various bar charts, pie charts and other forms of histograms. The data scientists are different from data developers in a way that the Data developers, be it ETL developer or a big data developer aims to transform the data and mold the data in the form needed by a data scientist to apply his techniques.
The actual mining tasks include the use of interesting patterns such as groups of data records such as cluster analysis, anomaly detection like unusual records and dependencies such as sequential pattern mining, association rule mining. A spatial index is the database technique that is widely used.
Head to Head Differences Between Data Scientist and Data Mining
Below is the Top 7 Comparison Between Data Scientist and Data Mining:
Key Differences Between Data Scientist and Data Mining
Below are the lists of points, describe the key Differences Between Data Scientist and Data Mining:
- A data scientist possesses strong technical skillset and the right set of tools to work and derive the relevant information by applying mathematical functions such as collinearity, regression analysis, etc. He also applies the algorithms and periodically conduct the socio- computational analysis whereas data mining employs the use of metadata which is data about data and that metadata is used to extract the information based upon your keywords and query. Data mining techniques also use the potential to apply algorithms to extract past trends from the current as well as from legacy systems.
- Roles and responsibilities of a data scientist include undirected research, create an open-ended company based questions, extraction of huge volumes of data from multiple external as well as internal sources. He also employs sophisticated analytics programs, statistical and machine learning methods to create data later to be used in prescriptive modeling and predictive modeling whereas data mining includes design, implementation of persistent data stores, performance tuning methods, create automatic backup and capacity planning by managing integrity, confidentiality and availability of data stores and databases.
- Let us understand the role of a data scientist with the help of an example. Consider a scenario where you are running a sweet shop and you are interested to know which sweets received the most positive feedback. In this kind of cases, your sources of data will not be limited to just databases, they could also extend to social media websites and customer feedback messages. In such cases, a Data Scientist is the person who would come to your rescue. He is the right person for you as he has the historical data from all the relevant sources and not just from a single database. whereas if there is the same situation but you are more interested in finding out the last 8 years’ data about the sweets than you would need a technique known as mining. In data mining, you dig deep into the data history and find all the information that seems to be remotely relevant.
- A data scientist is expected to devise data-driven solutions to the latest challenges encountered in the organization. He is also expected to invent new algorithms which can efficiently solve complex problems by building new tools to automate work whereas data mining focuses majorly on implementing the system based on customer needs and industry requirements. It also presents a tool for analysis of various data sources in order to discover fraud patterns and the possible security breaches.
Data Scientist and Data Mining Comparison Table
Below are the lists of points, describe the comparison table Between Data Scientist and Data Mining.
|Basis for comparison||Data scientist||Data mining|
|What is it||A person||A technique|
|Definition||A data scientist is good at statistics than any random software engineering analyst and way better at software development skills than any statistician.||Data mining is the method of acquiring or collecting the information that is stored in the database which was previously unknown and obscure. The information can then be used to make relevant business decisions.|
|Data from||The data can be in the form of structured, semi-structured as well as unstructured. This is in continuation of data analytics fields such as data mining, statistics, and predictive analysis.||This buzzword is often applied to large-scale data or information generation and processing using collection, extraction, analysis, statistics, and warehousing.|
|Need and Origin||The word data scientists have been around in the early 80s but their prime requirement is seen in today’s scenario when the world has a huge data to maintain||The term data mining has been evolved in parallel and became much prevalent in 90s. It owes its origin to KDD (Knowledge Discovery in Databases) which is a process of finding knowledge from the data already present in the databases.|
|Area of Working||Scientific study and research||Business processes|
|Target||To produce client-centric relevant data||To create usable data|
|Aim||He aims to build predictive models, social media analysis trends and derive unknown facts||The aim is to search and find previously known hidden data|
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