@article{1463107,
      recid = {1463107},
      author = {Hodeghatta, Umesh R., and Nayak, Umesha,},
      title = {Practical business analytics using R and Python : solve  business problems using a data-driven approach /},
      pages = {1 online resource (716 pages) :},
      abstract = {This book illustrates how data can be useful in solving  business problems. It explores various analytics techniques  for using data to discover hidden patterns and  relationships, predict future outcomes, optimize efficiency  and improve the performance of organizations. You’ll learn  how to analyze data by applying concepts of statistics,  probability theory, and linear algebra. In this new  edition, both R and Python are used to demonstrate these  analyses. Practical Business Analytics Using R and Python  also features new chapters covering databases, SQL, Neural  networks, Text Analytics, and Natural Language Processing.  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  using statistical machine learning and discusses concepts  like regression, classification, and neural networks. Part  three covers two of the most popular unsupervised learning  techniques, clustering and association mining, as well as  text mining and natural language processing (NLP). The book  concludes with an overview of big data analytics, R and  Python essentials for analytics including libraries such as  pandas and NumPy. Upon completing this book, you will  understand how to improve business outcomes by leveraging R  and Python for data analytics.},
      url = {http://library.usi.edu/record/1463107},
      doi = {https://doi.org/10.1007/978-1-4842-8754-5},
}