Elements of Data Science, Machine Learning and Artificial Intelligence

  After a brief understanding of certain concepts now is the time to deep dive into the next topics related to the Applied statistics. To apply the concepts for the Statistics, we need data, from where can we get the data. Data sources are classified into Primary data and Secondary data.     Primary Data is collected continuously and Secondary data is one that is already collected, stored, and archived. Primary data are collected by the organization itself for a particular purpose. The benefits of primary data are that they fit the needs exactly and are UpToDate and reliable.   Secondary data are collected by other organizations or for other purposes. Any data, which are not collected by the organization for a specific purpose are secondary data. These might be published by other organizations, available from research studies, published by the government, web, social media, and so on.       This brings us to another aspect of data which are subdivided ...

  With the increase in the use of devices such as smartphones, apps, cloud, social media, online shopping, education, banking, etc. where more and more technology is expected to serve mankind statistics plays a key role in the area of science, finance, and industries (Right from Software to Construction industries). Processing and analyzing user data is very important. Predicting a patient's health who can from cardiac arrest, or monitoring the ones which have already undergone through it. Predicting the price of a stock in 6 months in the future, based on past and current financial performance. Identify the handwritten numbers. To actually dive deep into Data Science, Machine Learning, and Artificial Intelligence we need some insights into Statistics, Matrices, Vectors, and Probability. Some knowledge about the Differential Equations, Curve fitting, etc. would be an add-on. Mathematics is the mother of these concepts. We will look into these as pre-requisites   ...