The term ‘Data Science‘ has been traced back to 1974 when Peter Naur proposed it as an alternative name for computer science. The modern conception of data science as an independent discipline is sometimes attributed to William S. Cleveland. In a 2001 paper, he advocated an expansion of statistics beyond theory into technical areas; because this would significantly change the field, it warranted a new name.
Data Analytics is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories, and hypotheses.
Data Analytics is a vast subject. Some of the common terms include Business Intelligence (BI), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Big Data Analytics, etc.
Descriptive analytics is the interpretation of historical data to better understand changes that have occurred in the business. It describes the use of a range of historic data to draw comparisons.
Being the conventional form of Business Intelligence and Data Visualization, Descriptive Analytics seeks to provide a depiction, or a “Summary View” of facts and figures in an understandable format, to either inform or prepare data for further analysis. It uses two primary techniques, namely Data Aggregation, and Data Mining to report past events.
Most commonly reported financial metrics are a product of descriptive analytics—for example, year-over-year pricing changes, month-over-month sales growth, the number of users, or the total revenue per subscriber. These measures all describe what has occurred in a business during a set period.
Dr. Michael Wu, the chief scientist of San Francisco-based Lithium Technologies, which develops social customer experience management software for businesses, says that “Once you have enough data, you start to see Patterns. (This helps so) You can build a model of how these data work. Once you build a model, you can predict.“
As technologies rise in the Hype-cycle towards their “Peak of Inflated Expectations”, they tend to lose their precise technical definitions. This is because non-partitioners introduce a more colloquial interpretation, which can confuse the space. AI is certainly one such example, reaching its peak on the Gartner Hype Cycle, it has been interpreted in a variety of different ways, from meaning mere automation to including Arnold Schwarzenegger’s Terminator.
But, is there a significant difference between artificial intelligence, machine learning, and deep learning? Well, it’s quite COMPLICATED!
Andrey Bulezyuk, who is a German-based computer expert and has more than five years of experience in teaching people how artificial intelligence systems work, says that “practitioners in this field can clearly articulate the differences between the three closely-related terms.”
Click here to Register for our Free Data Science Workshop to learn more.