Artificial Intelligence also known as AI, is the imitation or cloning of human intelligence, that allows machines, more specifically computer systems, to perform in an extremely intelligent manner. AI is the ability of computer programs to think and learn like humans, such as visual perception, speech recognition, decision-making, and translation between languages. In this article, you will learn the concept of AI, it's need, the types and benefits of artificial intelligence, few examples of AI and last but not the least career opportunities in artificial intelligence.
2019 was a big year across the big data landscape. After starting the year with the Cloudera and Hortonworks merger, we’ve seen massive upticks in Big Data use around the globe, with companies flocking to embrace the importance of data operations and orchestration to their business success. The big data industry is now worth $189 Billion, an increase of $20 Billion over 2018, and is set to continue its rapid growth and reach $247 Billion by 2022. As quickly as the year began, it’s nearly over, which means it’s time for us to once again put on our thinking caps and make our predictions for 2020. But before we do, let’s take a look back at the 2019 big data trends predicted by Todd Goldman and Ramesh Menon of Infoworks, and then see what actually came true.
Nearly every business leader (99% according to an HBR study) recognizes the need to build a data driven organization. However, so many struggle to use data for a variety of reasons, a big one of them being that data exists in all sorts of places. This is especially the case with startups, who use a number of out of the box tools that don’t connect to each other, and that only offer so much granularity and flexibility. The solution? A data warehouse.
Data science is the field of applying advanced mathematical and statistical techniques to large sets of data to uncover insights that can improve the performance of different parts of a business. Data science is used in growth to uncover commonalities between types of users that are either successful or unsuccessful with a product. Marketers and growth teams use data science findings to improve the performance of their product, user acquisition funnel, user retention and more. These are the best articles on how to use data science to grow your business. From how to analyze data using R, pandas for Python, regression, model building, SQL and more, you'll learn how to apply data science concepts to growth.
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