Covering the essentials of business intelligence, explore the features & functions for an overview.
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Data science uses scientific methods, algorithms, processes and systems to gain insights from data. The end result of their methods are usually charts or other visualizations that reveal trends and produce insights to inform the decision-making process and thus help businesses develop better products and services.
Data scientists analyze data from the internet, customer records from CRM applications, smartphones and other sources. Though data is invaluable for innovation, the key lies in the data scientist’s ability to interpret and glean insights from the data and communicate them effectively. To do this requires knowledge of statistics, business subject matters, computer science and other related skills.
Though data scientists come from different backgrounds and use an array of skills, working in this field requires a knowledge of statistics and math. Curiosity is also an important trait, as are creativity and critical thinking. It helps to have a mind that seeks out patterns, finds trends and asks questions.
Data scientists are highly educated, with the majority holding a master’s degree or higher. They typically have a background in computer programming that informs the modeling and algorithm necessary to interpret large data sets. The two main programming languages for data science are Python and R language.
In addition to the scientific and analytical skills, data scientists working in a business environment should have an understanding of business strategy. Even with a large team of specialists, data scientists need to be able to apply the insights from data to improve business processes, develop products and inform other business-related processes.
Finally, data scientists need to be able to communicate their insights and ideas effectively to nontechnical team members to ensure that the connections are made. Without that, it may not be possible for the nontechnical team members to use the data to inform business strategies.