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This book covers the two most widespread open-source platforms for data science: Python and R, so that readers may learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Written for the general reader with no previous analytics or programming experience, there are chapters dedicated to learning the basics and step-by-step instructions and walkthroughs for solving data science problems. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining.
Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This handy reference and tutorial covers topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis, including illustrative examples. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning.
Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use
Business Analytics Using R by Umesh R. Hodeghatta; Umesh NayakThis book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. This book will discuss and explore the following through examples and case studies: data management and R functions, business analytics project life cycle, descriptive analytics, data cleaning, data mining, predictive analytics, classification, association, clustering, and regression.
This book gives you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. Learn how to use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract insights. Learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. Confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. This book covers: techniques to use data to identify the exact problem to be solved; visualizing data using different graphs; and how to select an appropriate algorithm for data extraction.
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