Practical Business Analytics Using R and Python

This updated book builds on the basic foundations established in the first edition, illustrating how data can predict future outcomes, optimize the efficiency and performance of organizations, and detect relevant patterns and relationships. It will also teach you to analyze and understand data by applying concepts of probability theory, statistics, and machine learning. This new edition also uses Python coding examples in addition to R. It also features new chapters, including coverage of SQL, NLP, and optimization models in R and Python.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models, statistical learning models, and machine learning models. It discusses concepts like regression, classification, and neural networks. Part three covers the two popular unsupervised learning techniques used in text mining and natural language processing (NLP). It explains how to utilize clustering techniques and mine text to discover insights. The book concludes with an overview of big data analytics, R and Python for analytics, and explores Jupyter Notebook, pandas, and NumPy python libraries used in data analytics.