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Data scientists are people who create programming code, use them to form a rich set of combinations of statistics, and use their knowledge to develop and generate business-related insights on data. Data science is, in essence, an interdisciplinary area of systems and processes that extracts insights and knowledge from data in different forms.
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The value of data and client confidentiality concerning security is increasing daily. Therefore, it becomes an urgent need to deploy data scientists as they not only aim to protect your data but also provide meaningful analysis and extractions to foster your organization and business with the future trends and how the company can improve from what they are today maintaining various bar charts, pie charts and other forms of histograms. Data scientists are different from data developers in a way that the Data developers, be they ETL developers or big data developers, aim to transform the data and mold the data in the form needed by a data scientist to apply his techniques.Head to Head Differences Between Data Scientist vs Data Mining
Below is the Top 7 Comparison Between Data Scientist and Data Mining:Key Differences Between Data Scientist and Data Mining
A data scientist possesses a strong technical skillset and the correct set of tools to work and derive relevant information by applying mathematical functions such as collinearity, regression analysis, etc. He also applies the algorithms and periodically conducts the socio- computational research. Data mining techniques also operate the potential to apply algorithms to remove past trends from current and legacy systems.
The roles and responsibilities of a data scientist include undirected research, creating open-ended company-based questions, and extraction of vast volumes of data from multiple external as well as internal sources. He also employs sophisticated analytics programs and statistical and machine learning methods to create data later for prescriptive and predictive modeling. In contrast, data mining includes design, implementation of persistent data stores, performance tuning methods, 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 are interested in which sweets received the most positive feedback. In this kind of case, 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 historical data from all relevant sources, 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, then 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 remotely relevant.
He will also likely invent new algorithms to efficiently solve complex problems by building new tools to automate work. In contrast, data mining focuses on implementing the system based on customer needs and industry requirements. It also presents a tool for analyzing various data sources to discover fraud patterns and possible security breaches.Data Scientist and Data Mining Comparison Table
Below are the lists of points that describe the comparison table Between Data Scientists 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 since the early 80s, but their prime requirement is seen in today’s scenario when the world has a huge amount of data to maintain The term data mining has evolved in parallel and became much more prevalent in the 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 dataConclusion
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