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A data scientist has a similar position to that of a data engineer. They organise and process large amounts of data in order to provide valuable insights for business improvements. Therefore, their main role within a company is helping transform information into action. Data science is considered to be a more advanced form of data analysis. This is because of the large volume of data that is involved, and the need for sophisticated analytical methods to process it.
In more detail
Data scientists use a variety of different techniques to convert raw data into relevant insights. Their tasks include gathering data via web scraping, APIs, and surveys, and thoroughly cleaning it to discard irrelevant information. They must also prepare the data for modeling and perform exploratory data analyses (EDA) to account for missing data. Only then are they able to identify patterns and opportunities. Sometimes, data scientists may be required to develop new algorithms to solve data problems as they arise.
Not only do they rely on statistics and analytical approaches, but they may also use machine learning to treat large datasets and gain insights into business problems. Finally, data scientists must communicate their findings and predictions to their company’s IT department and management. They subsequently provide recommendations to change and improve processes.
Data scientists must have a deep understanding of statistics and be highly proficient programmers. Knowledge of programming languages such as SAS, R, and Python is usually required, as is staying on top of recent developments in analytical techniques (deep learning, machine learning, text analytics).
A Bachelor of Science in Mathematics, Computer Science, or Statistics is a minimum requirement for this position. Experience working with big data platforms is necessary, and data visualisation skills are a plus for this position. Finally, it is important to have a solid understanding of business concepts, and good communication and collaboration skills.