While the terms 'data analyst' and 'data engineer' are fairly well known, the work of a data scientist is for many people still a bit of a mystery. It is becoming increasingly important though, which is why below we answer a few of the questions readers might have.
Data scientists collect and analyze what we call big data, i.e. huge sets of both structured and unstructured data. A data scientist’s job, therefore, has to do not only with computer science but also with mathematics and statistics.
Data scientists model, process and analyze data and then go on to interpret the results. The final aim is to set up actionable plans based on solid data for businesses and other organizations
The typical data scientist is an analytical expert who uses his or her expertise in social sciences, statistics, mathematics, computer science, and technology to manage large amounts of data and discover trends others might never have picked up. In the process, they use contextual understanding, industry knowledge, and the ability to challenge existing assumptions in order to find solutions to challenges.
A data scientist would, for example, take unstructured and often messy data from sources as varied as social media feeds, smart devices, and emails stuff that typically won’t easily fit into a conventional database and try to make sense of this. In the process, they often discover underlying patterns and trends that would be near impossible to find with traditional methods.
Data scientists increasingly use tools such as AI (Artificial Intelligence) and Machine Learning to make their work easier.
Below are some areas where data scientists are playing an increasingly important role:
Personalized customer experience. One of the most popular fields data science is currently used in is helping marketing and sales departments to better understand their target audience with the aim of creating a better customer experience.
In-depth market analysis. A data scientist can help to pinpoint where and when certain products sell best. This helps the firm to have the right products ready at the right time, and to develop new products to meet newly discovered needs.
Reducing risk and chances of fraud. Data scientists are specialists in identifying data that in some or other way stands out. They create network, statistical, and big data methodologies to build fraud prediction models that help the business to respond immediately the moment anything strange or unusual comes up in data.
Improved decision making. A data scientist can use his or her employer’s data to improve the decision making process across the whole organization. This improves performance at all levels, including customer engagement, with the ultimate aim of boosting profitability. Identify opportunities. The job of a data scientist requires him or her to always look at existing data as if they see it for the first time, and to always question old assumptions. In the process, they often discover opportunities (or threats) that have until now remained hidden.
Making risk quantifiable. Using existing data, data scientists can build models that simulate the outcomes of a large number of different actions or reactions. This makes it possible for top management to make decisions based on statistical probabilities instead of taking huge risks without knowing the chances of success or failure.
Helping to recruit the right talent. By processing the massive amounts of data available via, for example, corporate databases, social media, and job search websites, data scientists can build models to predict which type of candidates will best fit the firm’s needs. By using AI and machine learning they can then quickly process large numbers of resumes to determine whether one or more candidates meet these criteria.
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