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3 amazing books in 1 about analyzing and mining data! Book 1: Learn more about how to analyze data now! This elaborate guide will take you on a journey to multiple aspects of this skill. There is a trick, a science, to doing it the right way, and some of the most important secrets will be revealed in the chapters ahead of you. Youll learn, among others: What the big deal with big data is and what to do with them. Fundamentals, risks, and strategies to analyze data. Social network data analysis. Applications to health care, business, and industrial data. Tips on analyzing decision trees, regression, and sentiment. The most important basics each analytical geek wants to know before takes these practices to the next level. Book 2: Dive into the complicated matter of analyzing and mining for data correctly. Forget about intuition or assumptions. Data are facts you can rely on if you draw the right conclusions. Drawing those conclusions requires certain skills and a background in knowledge that leads to the proper steps. This guide will help you along the way by addressing topics such as: The what and why of data mining, and the different applications to business-related topics. Attributes, classifications, datasets, and types of learning you need to understand to fully be aware of what you are dealing with. Data quality and data quantity thoughts. Data mining process steps, including CRISP-DM and SEMMA.. Linear, probabilistic, and other models to use in the visualization and analysis of data you have found. Techniques such as clustering, viewing genetic algorithms, and neural networks. Evaluation analysis strategies, classification, and numeric predictions. Book 3: Explore the field of data science, and the way to analyze big and small data. This technical book goes over the main aspects of analyzing data correctly by using various strategies you need to implement in order to get results that are precise and beneficial. Learn about: Modeling data and visualization. The three Vs of big data and what to do with them. Software recommendations and applications. Machine algorithms and ting side notes regarding them. Rules, infrastructure, adaptation, and other techniques. Perception and cognition basics that apply to data. Efficient uses of regression, database querying, machine learning, and data warehousing.