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\u003cp\u003e\u003cb\u003eGet your statistics basics right before diving into the world of data science\u003c/b\u003e\u003c/p\u003e\u003ch2\u003eAbout This Book\u003c/h2\u003e\u003cul\u003e\u003cli\u003eNo need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs;\u003c/li\u003e\u003cli\u003eImplement statistics in data science tasks such as data cleaning, mining, and analysis\u003c/li\u003e\u003cli\u003eLearn all about probability, statistics, numerical computations, and more with the help of R programs\u003c/li\u003e\u003c/ul\u003e\u003ch2\u003eWho This Book Is For\u003c/h2\u003e\u003cp\u003eThis book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful.\u003c/p\u003e\u003ch2\u003eWhat You Will Learn\u003c/h2\u003e\u003cul\u003e\u003cli\u003eAnalyze the transition from a data developer to a data scientist mindset\u003c/li\u003e\u003cli\u003eGet acquainted with the R programs and the logic used for statistical computations\u003c/li\u003e\u003cli\u003eUnderstand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more\u003c/li\u003e\u003cli\u003eLearn to implement statistics in data science tasks such as data cleaning, mining, and analysis\u003c/li\u003e\u003cli\u003eLearn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks\u003c/li\u003e\u003cli\u003eGet comfortable with performing various statistical computations for data science programmatically\u003c/li\u003e\u003c/ul\u003e\u003ch2\u003eIn Detail\u003c/h2\u003e\u003cp\u003eData science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.\u003c/p\u003e\u003cp\u003eThis book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.\u003c/p\u003e\u003cp\u003eBy the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.\u003c/p\u003e\u003ch2\u003eStyle and approach\u003c/h2\u003e\u003cp\u003eStep by step comprehensive guide with real world examples\u003c/p\u003e |
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