Foundations of Statistics for Data Scientists-Test Bank

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1. ISBN-10

   0367748452
2. ISBN-13

   978-0367748456Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for school college students teaching to grow to be data scientists. It is an in-depth presentation of the issues in statistical science with which any data scientist have to be acquainted, along with likelihood distributions, descriptive and inferential statistical methods, and linear modeling. The e guide assumes information of elementary calculus, so the presentation can cope with “why it actually works” along with “strategies to do it.” Compared with typical “mathematical statistics” textbooks, however, the e guide has a lot much less emphasis on likelihood concept and additional emphasis on using software program program to implement statistical methods and to conduct simulations for occasion key concepts. All statistical analyses inside the e guide use R software program program, with an appendix exhibiting the equivalent analyses with Python.

   Key Choices:

   • Reveals the climate of statistical science which is perhaps important for school college students who plan to grow to be data scientists.
   • Incorporates Bayesian and regularized changing into of fashions (e.g., exhibiting an occasion using the lasso), classification and clustering, and implementing methods with fashionable software program program (R and Python).
   • Incorporates virtually 500 exercises.

   The e guide moreover introduces fashionable issues that do not often appear in mathematical statistics texts nonetheless are extraordinarily associated for data scientists, similar to Bayesian inference, generalized linear fashions for non-normal responses (e.g., logistic regression and Poisson loglinear fashions), and regularized model changing into. The virtually 500 exercises are grouped into “Data Analysis and Functions” and “Methods and Concepts.” Appendices introduce R and Python and comprise choices for odd-numbered exercises.

   Alan Agresti, Distinguished Professor Emeritus on the School of Florida, is the creator of seven books, along with Categorical Data Analysis (Wiley) and Statistics: The Paintings and Science of Finding out from Data (Pearson), and has launched fast applications in 35 nations. His awards embody an honorary doctorate from De Montfort School (UK) and Statistician of the 12 months from the American Statistical Affiliation (Chicago chapter).

   Maria Kateri, Professor of Statistics and Data Science on the RWTH Aachen School, authored the monograph Contingency Desk Analysis: Methods and Implementation Using R (Birkhäuser/Springer) and a textbook on arithmetic for economists (in German). She has long-term experience in educating statistics applications to school college students of Data Science, Arithmetic, Statistics, Computer Science, Enterprise Administration, and Engineering.