Statistics And Calculus With Python Workshop

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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.

The Statistics And Calculus With Python Workshop

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Author : Peter Farrell
language : en
Publisher:
Release Date : 2020-08-17

The Statistics And Calculus With Python Workshop written by Peter Farrell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-17 with Computers categories.

With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easy Key Features Discover how most programmers use the main Python libraries when performing statistics with Python Use descriptive statistics and visualizations to answer business and scientific questions Solve complicated calculus problems, such as arc length and solids of revolution using derivatives and integrals Book Description Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python. The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions. By the end of this book, you'll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges. What you will learn Get to grips with the fundamental mathematical functions in Python Perform calculations on tabular datasets using pandas Understand the differences between polynomials, rational functions, exponential functions, and trigonometric functions Use algebra techniques for solving systems of equations Solve real-world problems with probability Solve optimization problems with derivatives and integrals Who this book is for If you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you. To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Python.

Subject Guide To Books In Print

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Author :
language : en
Publisher:
Release Date : 1991

Subject Guide To Books In Print written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with American literature categories.

Foundations Of Statistics For Data Scientists

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Author : Alan Agresti
language : en
Publisher: CRC Press
Release Date : 2021-11-29

Foundations Of Statistics For Data Scientists written by Alan Agresti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-29 with Business & Economics categories.

Foundations 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 students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "how to do it." Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. Key Features: Shows the elements of statistical science that are important for students who plan to become data scientists. Includes Bayesian and regularized fitting of models (e.g., showing an example using the lasso), classification and clustering, and implementing methods with modern software (R and Python). Contains nearly 500 exercises. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website (http://stat4ds.rwth-aachen.de/) has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.

Choice

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Author :
language : en
Publisher:
Release Date : 2002

Choice written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Academic libraries categories.

Training Systems Using Python Statistical Modeling

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Author : Curtis Miller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-20

Training Systems Using Python Statistical Modeling written by Curtis Miller and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-20 with Computers categories.

Leverage the power of Python and statistical modeling techniques for building accurate predictive models Key FeaturesGet introduced to Python's rich suite of libraries for statistical modelingImplement regression, clustering and train neural networks from scratchIncludes real-world examples on training end-to-end machine learning systems in PythonBook Description Python's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics. You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them. By the end of this book, you will have all the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics. What you will learnUnderstand the importance of statistical modelingLearn about the various Python packages for statistical analysisImplement algorithms such as Naive Bayes, random forests, and moreBuild predictive models from scratch using Python's scikit-learn libraryImplement regression analysis and clusteringLearn how to train a neural network in PythonWho this book is for If you are a data scientist, a statistician or a machine learning developer looking to train and deploy effective machine learning models using popular statistical techniques, then this book is for you. Knowledge of Python programming is required to get the most out of this book.

Books In Print

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Author :
language : en
Publisher: Reed Reference Publishing
Release Date : 1993-09

Books In Print written by and has been published by Reed Reference Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-09 with Reference categories.

V. 1. Authors (A-D) -- v. 2. Authors (E-K) -- v. 3. Authors (L-R) -- v. 4. (S-Z) -- v. 5. Titles (A-D) -- v. 6. Titles (E-K) -- v. 7. Titles (L-Q) -- v. 8. Titles (R-Z) -- v. 9. Out of print, out of stock indefinitely -- v. 10. -- Publishers.

Python Analysis

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Author : Aaron Nelson Ph D
language : en
Publisher:
Release Date : 2021-01-05

Python Analysis written by Aaron Nelson Ph D and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-05 with categories.

Python is an increasingly popular tool for data analysis. In recent years, a number of libraries have reached maturity, allowing R and Stata users to take advantage of the beauty, flexibility, and performance of Python without sacrificing the functionality these older programs have accumulated over the years.In this course, you will learn how to analyze data in Python using multi-dimensional arrays in numpy, manipulate DataFrames in pandas, use library of mathematical routines, and perform machine learning!Before jumping right into the project ideas let us read how can Python projects help you as a Python developer and which platform you should consider before you start any Python projects.

Introduction To Data Science

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Author : Laura Igual
language : en
Publisher: Springer Nature
Release Date : 2024-04-12

Introduction To Data Science written by Laura Igual and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-12 with Computers categories.

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or sentiment analysis. Topics and features: Provides numerous practical case studies using real-world data throughout the book Supports understanding through hands-on experience of solving data science problems using Python Describes concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data science Reviews a range of applications of data science, including recommender systems and sentiment analysis of text data Provides supplementary code resources and data at an associated website This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses.

Cd Roms In Print

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Author :
language : en
Publisher:
Release Date : 2002

Cd Roms In Print written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with CD-ROMs categories.