Tasks of a Data Science Specialist

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The tasks that a data scientist solves may vary depending on the company. In large corporations, they may work on several areas at the same time. For example, in a bank, a data scientist may work on credit assessment tasks and develop speech recognition processes.

Tasks of a Data Science Specialist
Tasks of a Data Science Specialist
The stages of working on a task for specialists from different fields are similar:

Clarification of customer requirements.
Deciding on the feasibility of using machine learning methods to solve the problem.
Data preparation and markup.
Selecting metrics to evaluate model performance.
Developing and training a machine learning model.
Evaluation of the economic effect of the model implementation.
Implementation of the model into production processes and products.
Model accompaniment.
Each new iteration allows us to better understand the business problems and refine the thailand rcs data solution. Therefore, each stage is repeated again and again to improve the model and update the data.

Stages of working with data in Data Science
Typically, Data Scientists have a standard workflow consisting of 5 steps:

Data mining is the process of collecting both structured and unstructured data from all relevant sources. Various tools are used for this, ranging from manual entry and web scraping to extracting metrics from proprietary systems.
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Storage and verification is the process of storing data in a suitable format for further processing using pre-defined mechanisms, and removing duplicates, filtering out unnecessary data, etc.
Analysis is the process of examining the relationships between different pieces of data, identifying patterns, and checking the consistency of the information obtained.
Processing and visualization - using various tools such as artificial intelligence, machine learning models and analytical algorithms to process data and visualize it.
Communication is the process of presenting data in the form of tables, graphs, lists or any other form convenient for demonstrating information to different categories of users. The goal is to make decisions based on data, such as changing a marketing strategy or increasing a company's budget.
Why does business need Data Science?
According to professional social network Kaggle, the use of Data Science is a popular practice in companies of all sizes. Research by IDC and Hitachi confirms that 78% of enterprises have increased the amount of data they process recently. Businesses understand that unstructured information can contain important knowledge for the company and affect business results, so they use Data Science to analyze it.