According to the motto: “A picture says more than a thousand words” some useful slides with a short explanation are shown below.
1. Evolution of Analytics
Analytics is the discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making. In other words, analytics can be understood as the connective tissue between data and effective decision making, within an organization. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.
Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
2. Future of Data Science
Sebastian Raschka, researcher of applied Machine Learning and Deep Learning at Michigan State University, thinks that the future of Data Science does not indicate machines taking over humans, but rather human data professionals embracing open-source technologies.
It is common understanding that future Data Science projects, thanks to advanced tools, will scale to new heights where more human experts will be required to handle highly complex tasks very efficiently. However, according to McKinsey Global Institute (MGI), the next decade will witness a sharp shortage of around 250,000 Data Scientists in the U.S. alone. The question is whether machines can ever enable seamless collaboration between technologies, tools, processes, and end users. Automated tools and assistants can aid the human mind to accomplish tasks more quickly and accurately, but machines cannot ever be expected to substitute for human thinking. The core of problem-solving is intellectual thinking, which no machine, no matter how sophisticated it is, can replicate.