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What is a Data Scientist?

  • Amruta Bhaskar
  • Mar 17, 2021
  • 0 comentario (s)
  • 1255 Puntos de vista

 Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.

Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover solutions to business challenges.

A data scientist’s work typically involves making sense of messy, unstructured data, from sources such as smart devices, social media feeds, and emails that don’t neatly fit into a database.

A data scientist is a professional responsible for collecting, analyzing and interpreting extremely large amounts of data. The data scientist role is an offshoot of several traditional technical roles, including mathematician, scientist, statistician and computer professional. This job requires the use of advanced analytics technologies, including machine learning and predictive modelling.

A data scientist requires large amounts of data to develop hypotheses, make inferences and analyze customer and market trends. Basic responsibilities include gathering and analyzing data, using various types of analytics and reporting tools to detect patterns, trends and relationships in data sets.

In business, data scientists typically work in teams to mine big data for information that can be used to predict customer behaviour and identify new revenue opportunities. In many organizations, data scientists are also responsible for setting best practices for collecting data, using analysis tools and interpreting data.

The demand for data science skills has grown significantly over the years, as companies look to glean useful information from big data, the voluminous amounts of structured, unstructured and semi-structured data that a large enterprise or internet of things produces and collects.

Data science is a highly interdisciplinary practice involving a large scope of information and one that usually takes into account the big picture more than other analytical fields. In business, the goal of data science is to provide intelligence about consumers and campaigns and help companies create strong plans to engage their audience and sell their products.

Data scientists must rely on creative insights using big data, the large amounts of information collected through various collection processes, like data mining. On an even more fundamental level, big data analytics can help brands understand the customers who ultimately help determine the long-term success of a business or initiative. In addition to targeting the right audience, data science can be used to help companies control the stories of their brands.

Because big data is a rapidly growing field, there are constantly new tools available, and those tools need experts who can quickly learn their applications. Data scientists can help companies create a business plan to achieve goals based on research and not just intuition.

Data science plays a very important role in security and fraud detection because the massive amounts of information allow for drilling down to find slight irregularities in data that can expose weaknesses in security systems. Data science is a driving force between highly specialized user experiences created through personalization and customization. The analysis can be used to make customers feel seen and understood by a company.

There's not a definitive job description when it comes to a data scientist role. But here are a few things you'll likely be doing:

  • Collecting large amounts of unruly data and transforming it into a more usable format.
  • Solving business-related problems using data-driven techniques.
  • Working with a variety of programming languages, including SAS, R and Python.
  • Having a solid grasp of statistics, including statistical tests and distributions.
  • Staying on top of analytical techniques such as machine learning, deep learning and text analytics.
  • Communicating and collaborating with both IT and business.
  • Looking for order and patterns in data, as well as spotting trends that can help a business’s bottom line.

Technical skills are not the only thing that matters, however. Data scientists often exist in business settings and are charged with communicating complex ideas and making data-driven organizational decisions. As a result, it is highly important for them to be effective communicators, leaders and team members as well as high-level analytical thinkers.

Experienced data scientists and data managers are tasked with developing a company’s best practices, from cleaning to processing and storing data. They work cross-functionally with other teams throughout their organization, such as marketing, customer success, and operations. They are highly sought after in today’s data and tech-heavy economy and their salaries and job growth reflect that.

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