Applied Statistics With Python
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Applied Statistics With Python
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Author : Leon Kaganovskiy
language : en
Publisher:
Release Date : 2025
Applied Statistics With Python written by Leon Kaganovskiy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with Python (Computer program language) categories.
"Applied Statistics with Python concentrates on applied and computational aspects of statistics, focussing on conceptual understanding and Python-based calculations. Based on years of experience teaching introductory and intermediate Statistics at Touro College and Brooklyn College, this book compiles multiple aspects of applied statistics, teaching the reader useful skills in statistics and computational science with a focus on conceptual understanding. This book does not require previous experience with statistics and Python, explaining the basic concepts before developing them into more advanced methods from scratch. Applied Statistics with Python is intended for undergraduate students in business, economics, biology, social sciences, and natural science, whilst also being useful as a supplementary text for more advanced students"--
Applied Statistics With Python
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Author : Leon Kaganovskiy
language : en
Publisher: CRC Press
Release Date : 2025-03-03
Applied Statistics With Python written by Leon Kaganovskiy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-03 with Mathematics categories.
Applied Statistics with Python: Volume I: Introductory Statistics and Regression concentrates on applied and computational aspects of statistics, focusing on conceptual understanding and Python-based calculations. Based on years of experience teaching introductory and intermediate Statistics courses at Touro University and Brooklyn College, this book compiles multiple aspects of applied statistics, teaching the reader useful skills in statistics and computational science with a focus on conceptual understanding. This book does not require previous experience with statistics and Python, explaining the basic concepts before developing them into more advanced methods from scratch. Applied Statistics with Python is intended for undergraduate students in business, economics, biology, social sciences, and natural science, while also being useful as a supplementary text for more advanced students. Key Features: Concentrates on more introductory topics such as descriptive statistics, probability, probability distributions, proportion and means hypothesis testing, as well as one-variable regression The book’s computational (Python) approach allows us to study Statistics much more effectively. It removes the tedium of hand/calculator computations and enables one to study more advanced topics Standardized sklearn Python package gives efficient access to machine learning topics Randomized homework as well as exams are provided in the author’s course shell on My Open Math web portal (free)
Applied Statistics With Python
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Author : Leon Kaganovskiy
language : en
Publisher: CRC Press
Release Date : 2025-12-29
Applied Statistics With Python written by Leon Kaganovskiy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-29 with Mathematics categories.
Applied Statistics with Python, Volume II focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support vector classifiers, nonlinear models, tree-based methods, clustering, and principal component analysis. As in Volume I, the Python programming language is used throughout due to its flexibility and widespread adoption in data science and machine learning. The book relies heavily on tools from the standard sklearn package, which are integrated directly into the discussion. Unlike many other resources, Python is not treated as an add-on, but as an organic part of the learning process. This book is based on the author’s 15 years of experience teaching statistics and is designed for undergraduate and first-year graduate students in fields such as business, economics, biology, social sciences, and natural sciences. However, more advanced students and professionals might also find it valuable. While some familiarity with basic statistics is helpful, it is not required - core concepts are introduced and explained along the way, making the material accessible to a wide range of learners. Key Features: Employs Python as an organic part of the learning process Removes the tedium of hand/calculator computations Weaves code into the text at every step in a clear and accessible way Covers advanced machine-learning topics Uses tools from Standardized sklearn Python package
Applied Statistics With Python
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Author : Leon Kaganovskiy
language : en
Publisher: CRC Press
Release Date : 2025-12-26
Applied Statistics With Python written by Leon Kaganovskiy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-26 with Computers categories.
This book focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support vector classifiers, nonlinear models, tree-based methods, clustering, and principal component analysis.
Applied Time Series Analysis And Forecasting With Python
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Author : Changquan Huang
language : en
Publisher: Springer Nature
Release Date : 2022-10-19
Applied Time Series Analysis And Forecasting With Python written by Changquan Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Mathematics categories.
This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.
Data Science With Applied Statistics In Python
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Author : Dr.A Manimaran
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-02-05
Data Science With Applied Statistics In Python written by Dr.A Manimaran and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-05 with Language Arts & Disciplines categories.
Dr.A Manimaran, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.A.Selvakumar, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.S. Ramesh, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.J.Chenni Kumaran, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.M.Sivaram, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Machine Learning And Deep Learning Using Python And Tensorflow
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Author : Venkata Reddy Konasani
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29
Machine Learning And Deep Learning Using Python And Tensorflow written by Venkata Reddy Konasani and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory
Applied Univariate Bivariate And Multivariate Statistics Using Python
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Author : Daniel J. Denis
language : en
Publisher: John Wiley & Sons
Release Date : 2021-05-11
Applied Univariate Bivariate And Multivariate Statistics Using Python written by Daniel J. Denis and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-11 with Mathematics categories.
Applied Univariate, Bivariate, and Multivariate Statistics Using Python A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in Python Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied. Most of the datasets used in the book are small enough to be easily entered into Python manually, though they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, making the book perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python. Readers will also benefit from the inclusion of: A review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors An introduction to Python, exploring how to communicate with Python A treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management An introduction to topics such as ANOVA, MANOVA and discriminant analysis, regression, principal components analysis, factor analysis, cluster analysis, among others, exploring the nature of what these techniques can vs. cannot do on a methodological level Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.
Statistics And Calculus With Python Workshop
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Author :
language : en
Publisher:
Release Date : 2020
Statistics And Calculus With Python Workshop written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Developing Analytic Talent
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Author : Vincent Granville
language : en
Publisher: John Wiley & Sons
Release Date : 2014-04-07
Developing Analytic Talent written by Vincent Granville and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-07 with Computers categories.
Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates.