Data Scientist

What is a data scientist?

A data scientist sifts through, examines and interprets large amounts of both structured and unstructured data to identify patterns. Based on these patterns, data scientists can then make predictions about the future, as well as develop new methods for analysis and machine-learning models.

With the onslaught of Big Data in recent years, data scientists are currently in high demand. Companies need professionals who can not only make sense of all the data they collect, but also weave together the story of that data in a compelling, useful way.

The role of a data scientist requires a diverse range of skills, including:

  • programming
  • mathematics and statistics
  • an understanding of machine learning and algorithms
  • data handling and data visualisation skills
  • an aptitude for bringing data to life through their analysis

The job of data scientists essentially involves creating order from chaos - combing through large amounts of data, organising it in a meaningful way, analysing trends, building predictive models, showing cause and effect, and gleaning important insights from which crucial business decisions can be made.

Beyond dealing with the data, technology and analyses, data scientists must also possess a degree of business acumen. They have to be able to demonstrate how the available data can ultimately generate better outcomes for their business - and they’re often responsible for these improved results.

What’s the difference between a data scientist and a data analyst?

Although data analysts do share a lot in common with data scientists, the roles are different. Like data scientists, data analysts collect, process and summarise data to uncover insights and solve problems. And like data scientists, they need strong programming, mathematical, statistical, data-wrangling and data-visualisation skills to do their jobs.

However, data scientists typically spearhead business-critical Big Data projects - devising the means for addressing the questions that arise and carrying them out. On the other hand, data analysts generally look for answers to questions or problems based on guidance they receive from their business counterparts. What’s more, data analysts generally don’t build statistical models, nor are they involved in advanced programming and machine learning, working instead with simpler databases and other business-intelligence tools and services.

Finally, data scientists also draw on their storytelling skills to convert data into meaningful (and visual) insights, creating a business plan with these learnings, whereas data analysts normally don’t have to perform these tasks.

Why is data analysis important?

In this day and age, robust data analysis is critical to business success, as it can provide invaluable industry, competitor and customer information.

With the help of a data scientist and the right data, companies can:

  • analyse the industry and competitive landscape to support strategic planning
  • gain business intelligence to strengthen brands, increase productivity and drive greater revenues
  • accurately assess trends and identify risks
  • respond proactively and quickly to market opportunities or disruption
  • enhance predictive modelling, machine learning and other Big Data initiatives

Of course, for data scientists to do their jobs well, they need two essentials:

  • relevant data
  • the right tools and technology to process the vast amount of data available

Companies need to make sure they have the appropriate data streams for generating reliable business modelling and predictive analysis. And fortunately, innovative technologies are available that enable data scientists to analyse and evaluate the never-ending stream of data in today’s complex world. This empowers companies to make better informed, more effective business decisions.

LexisNexis Data as a Service for data scientists

LexisNexis Data as a Service allows data scientists to easily integrate near-real-time news and public data streams into applications to support business-critical analytics projects. The application programming interface (API) can deliver billions of relevant documents and data points, enabling data scientists to:
  • Integrate into your platforms and applications unstructured data from the most comprehensive, global content collection in the industry - including both open web and licensed content, with news archives going back more than 40 years.
  • Leverage our metadata and powerful content enrichment based on a combination of human curation, smart indexing, tagging and text normalisation to refine data feed results for greater relevance.

LexisNexis Data as a Service solutions include:

  • Metabase, which makes it easy to extract business intelligence from the most comprehensive, global content collection of print, online news and social media sources in the industry. New sources are added every week, including the ability to integrate custom sources.
  • WSK (Web Services Kit), which performs dynamic search retrieval across LexisNexis content to integrate highly relevant data - filtered by topic - into Big Data projects and business-critical platforms.

Ultimately, LexisNexis Data as a Service solutions enhance your data analysis by complementing your existing data sources with a stream of open web and licensed content. As a result, you can deliver valuable intelligence to increase decision-making accuracy and business gains.

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