Data Science And Analytics With Python

Data Science and Analytics with Python PDF
Author: Jesus Rogel-Salazar
Publisher: CRC Press
Size: 23.50 MB
Category : Computers
Languages : en
Pages : 400
View: 4823

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Data Science And Analytics With Python

by Jesus Rogel-Salazar, Data Science And Analytics With Python Books available in PDF, EPUB, Mobi Format. Download Data Science And Analytics With Python books, Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.

Data Science And Analytics With Python R And Spss Programming

Data Science and Analytics  with Python  R and SPSS Programming  PDF
Author: V.K. Jain
Publisher: KHANNA PUBLISHING HOUSE
Size: 26.86 MB
Category : Computers
Languages : en
Pages : 276
View: 2328

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Data Science And Analytics With Python R And Spss Programming

by V.K. Jain, Data Science And Analytics With Python R And Spss Programming Books available in PDF, EPUB, Mobi Format. Download Data Science And Analytics With Python R And Spss Programming books, The Book has been written completely as per AICTE recommended syllabus on "Data Sciences". SALIENT FEATURES OF THE BOOK: Explains how data is collected, managed and stored for data science. With complete courseware for understand the key concepts in data science including their real-world applications and the toolkit used by data scientists. Implement data collection and management. Provided with state of the arts subjectwise. With all required tutorials on R, Python and Bokeh, Anaconda, IBM SPSS-21 and Matplotlib.

Datenanalyse Mit Python

Datenanalyse mit Python PDF
Author: Wes McKinney
Publisher: O'Reilly
Size: 79.14 MB
Category : Computers
Languages : de
Pages : 542
View: 1266

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Datenanalyse Mit Python

by Wes McKinney, Datenanalyse Mit Python Books available in PDF, EPUB, Mobi Format. Download Datenanalyse Mit Python books, Erfahren Sie alles über das Manipulieren, Bereinigen, Verarbeiten und Aufbereiten von Datensätzen mit Python: Aktualisiert auf Python 3.6, zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele, wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lösen. Gleichzeitig lernen Sie die neuesten Versionen von pandas, NumPy, IPython und Jupyter kennen. Geschrieben von Wes McKinney, dem Begründer des pandas-Projekts, bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl für Datenanalysten, für die Python Neuland ist, als auch für Python-Programmierer, die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und zugehöriges Material des Buchs sind auf GitHub verfügbar. Aus dem Inhalt: Nutzen Sie die IPython-Shell und Jupyter Notebook für das explorative Computing Lernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennen Setzen Sie die Datenanalyse-Tools der pandasBibliothek ein Verwenden Sie flexible Werkzeuge zum Laden, Bereinigen, Transformieren, Zusammenführen und Umformen von Daten Erstellen Sie interformative Visualisierungen mit matplotlib Wenden Sie die GroupBy-Mechanismen von pandas an, um Datensätzen zurechtzuschneiden, umzugestalten und zusammenzufassen Analysieren und manipulieren Sie verschiedenste Zeitreihen-Daten Für diese aktualisierte 2. Auflage wurde der gesamte Code an Python 3.6 und die neuesten Versionen der pandas-Bibliothek angepasst. Neu in dieser Auflage: Informationen zu fortgeschrittenen pandas-Tools sowie eine kurze Einführung in statsmodels und scikit-learn.

Advanced Data Science And Analytics With Python

Advanced Data Science and Analytics with Python PDF
Author: Jesus Rogel-Salazar
Publisher: CRC Press
Size: 23.96 MB
Category : Business & Economics
Languages : en
Pages : 384
View: 2957

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Advanced Data Science And Analytics With Python

by Jesus Rogel-Salazar, Advanced Data Science And Analytics With Python Books available in PDF, EPUB, Mobi Format. Download Advanced Data Science And Analytics With Python books, Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app. About the Author Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Data Science Using Python And R

Data Science Using Python and R PDF
Author: Chantal D. Larose
Publisher: Wiley
Size: 23.40 MB
Category : Computers
Languages : en
Pages : 240
View: 5237

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Data Science Using Python And R

by Chantal D. Larose, Data Science Using Python And R Books available in PDF, EPUB, Mobi Format. Download Data Science Using Python And R books, Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

Hands On Data Science And Python Machine Learning

Hands On Data Science and Python Machine Learning PDF
Author: Frank Kane
Publisher:
Size: 53.58 MB
Category : Computers
Languages : en
Pages : 420
View: 4275

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Hands On Data Science And Python Machine Learning

by Frank Kane, Hands On Data Science And Python Machine Learning Books available in PDF, EPUB, Mobi Format. Download Hands On Data Science And Python Machine Learning books, This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark.About This Book* Take your first steps in the world of data science by understanding the tools and techniques of data analysis* Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods* Learn how to use Apache Spark for processing Big Data efficientlyWho This Book Is ForIf you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book.What You Will Learn* Learn how to clean your data and ready it for analysis* Implement the popular clustering and regression methods in Python* Train efficient machine learning models using decision trees and random forests* Visualize the results of your analysis using Python's Matplotlib library* Use Apache Spark's MLlib package to perform machine learning on large datasetsIn DetailJoin Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them.Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.Style and approachThis comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.

Data Science For Marketing Analytics

Data Science for Marketing Analytics PDF
Author: Tommy Blanchard
Publisher: Packt Publishing Ltd
Size: 60.37 MB
Category : Computers
Languages : en
Pages : 420
View: 2716

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Data Science For Marketing Analytics

by Tommy Blanchard, Data Science For Marketing Analytics Books available in PDF, EPUB, Mobi Format. Download Data Science For Marketing Analytics books, Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features Study new techniques for marketing analytics Explore uses of machine learning to power your marketing analyses Work through each stage of data analytics with the help of multiple examples and exercises Book Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions. What you will learn Analyze and visualize data in Python using pandas and Matplotlib Study clustering techniques, such as hierarchical and k-means clustering Create customer segments based on manipulated data Predict customer lifetime value using linear regression Use classification algorithms to understand customer choice Optimize classification algorithms to extract maximal information Who this book is for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.

Python For Data Analysis Analytics

Python for Data Analysis   Analytics PDF
Author: Hacktech Academy
Publisher: Hacktech Academy
Size: 69.54 MB
Category :
Languages : en
Pages : 112
View: 6398

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Python For Data Analysis Analytics

by Hacktech Academy, Python For Data Analysis Analytics Books available in PDF, EPUB, Mobi Format. Download Python For Data Analysis Analytics books, ⭐️ 55% OFF for Bookstores! NOW at $ 34.95 instead of $ 54.19 ⭐️ Are You Looking For The Best Beginners Guide To Discovering Data Analysis And Analytics With Python?Do You Want To Enter The World Of Data Science And How To Leverage Python For It?Do Want To Get A Thorough Introduction To Machine Learning? If yes, then this Guide is for you! This is the Top Guide to learning Data Analysis & Analytics. Talking about the IT world, there are many options when you have to choose language programming to learn and then to use for developing your career, especially if you want to become a Data Scientist. This Handbook will not only give you reasons on why you need to learn data science, but it will also tell you why learning data science with Python training is the better option. In this book you will: Have a Clear and Exhaustive Explanation About Data Analysis and Why It Is So Important Today in The Business World; organizations of all sizes rely on the insights they extract from the data they have to measure progress, make informed decisions, plan for the future, and so on. Data scientists are the people who process and organize the data with scientific methods, algorithms, and other techniques. Understand Why Python is Preferred to Use For Data Analysis Over Other Tools and the reasons why all the benefits of using Python made it the best tool to learn data science. Learn How to Carry Out Work More and More Complex and Difficult to be updated on new themes and trends in the sector and carry out small independent jobs to finance your projects. ...& Lot More! Your Customers will never stop to use this book. Are you completely new to programming and want to learn how to code, but don't know where to begin? Are you looking to upgrade your data wrangling skills to future-proof your career and break into Data Science and Analytics? Python is one of the most valuable and interesting languages for data analysis. Therefore, the popularity of Python is growing day by day, especially in the world of data analysis or data sciences. This Definitive Guide will combine Data Analysis and Python to to help your customer build amazing products and help businesses Buy it NOW and let your customers get addicted to this amazing book!

Data Science And Analytics With Python

Data Science and Analytics with Python PDF
Author: Jesus Rogel-Salazar
Publisher: CRC Press
Size: 22.53 MB
Category : Computers
Languages : en
Pages : 400
View: 333

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Data Science And Analytics With Python

by Jesus Rogel-Salazar, Data Science And Analytics With Python Books available in PDF, EPUB, Mobi Format. Download Data Science And Analytics With Python books, Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.

Statistik Workshop F R Programmierer

Statistik Workshop f  r Programmierer PDF
Author: Allen B. Downey
Publisher: O'Reilly Germany
Size: 77.47 MB
Category : Computers
Languages : de
Pages : 160
View: 4586

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Statistik Workshop F R Programmierer

by Allen B. Downey, Statistik Workshop F R Programmierer Books available in PDF, EPUB, Mobi Format. Download Statistik Workshop F R Programmierer books, Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Practical Statistics For Data Scientists

Practical Statistics for Data Scientists PDF
Author: Peter Bruce
Publisher: "O'Reilly Media, Inc."
Size: 79.56 MB
Category : Computers
Languages : en
Pages : 368
View: 3112

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Practical Statistics For Data Scientists

by Peter Bruce, Practical Statistics For Data Scientists Books available in PDF, EPUB, Mobi Format. Download Practical Statistics For Data Scientists books, Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data

Python For Data Analysis

Python For Data Analysis PDF
Author: Matt Algore
Publisher:
Size: 16.16 MB
Category : Computers
Languages : en
Pages : 82
View: 7009

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Python For Data Analysis

by Matt Algore, Python For Data Analysis Books available in PDF, EPUB, Mobi Format. Download Python For Data Analysis books, Talking about the IT world, there are many options when you have to choose language programming to learn and then to use for developing your career, especially if you want to become a Data Scientist. Python is one of the topmost languages and is becoming more and more popular because of plenty of reasons and one of the key reasons is that it is the best language to master if you want to analyze the data or get into the field of data analysis and data sciences. This Handbook will not only give you reasons on why you need to learn data science, but it will also tell you why learning data science with Python training is the better option. In this book you will: - Have a Clear and Exhaustive Explanation About Data Analysis and Why It Is So Important Today in The Business World; organizations of all sizes rely on the insights they extract from the data they have to measure progress, make informed decisions, plan for the future, and so on. Data scientists are the people who process and organize the data with scientific methods, algorithms, and other techniques. - Understand Why Python is Preferred to Use For Data Analysis Over Other Toolsand the reasons why all the benefits of using Python made it the best tool to learn data science. - Find a Step by Step Process to Install Python on Your Computer and a complete analysis of its hundreds of different libraries and frameworks which is a great addition to your development process. There's one library and framework for every need! - Have a Complete and Exhaustive List of Python Application to realize how this tool is flexible if you want to try something creative that's never done before. Due to that, it's possible to build data models, systematize data sets, create ML-powered algorithms, web services, and apply data mining to accomplish different tasks in a brief time for any kind of business organization - Learn How to Carry Out Work More and More Complex and Difficult to be updated on new themes and trends in the sector and carry out small independent jobs to finance your projects. - & Lot More! Are you completely new to programming and want to learn how to code, but don't know where to begin? Are you looking to upgrade your data wrangling skills to future-proof your career and break into Data Science and Analytics? Python is one of the most valuable and interesting languages for data analysis. Therefore, the popularity of Python is growing day by day, especially in the world of data analysis or data sciences. This Definitive Guide will combine Data Analysis and Python to give you the best information you could find. This guide is perfect to help you build amazing products and help businesses Order Your Copy Now and Start Becoming a Successful Python Expert!

Data Science With Python

Data Science with Python PDF
Author: Craig Berg
Publisher:
Size: 24.41 MB
Category :
Languages : en
Pages : 138
View: 4392

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Data Science With Python

by Craig Berg, Data Science With Python Books available in PDF, EPUB, Mobi Format. Download Data Science With Python books, You Are About To Venture Into The World Of Data Science With Confidence By Learning The Ins And Outs Of Data Science With Python! Are you a practicing or aspiring data scientist who's been wondering; What's the best language for a data scientist's rigorous work? Is there one that is also easy to understand and learn, and easy to use for the less technically-inclined person? ...and one that also builds better analytics tools? If you've been having those questions, you've clearly been looking for Python. Individual data scientists and data science consulting companies are increasingly adopting Python as a programming language of choice, as they realize its amazing features like large sets of libraries and unique capabilities in handling big data. The fact that it' also a language that you can easily integrate with other programming languages, and it's easily scalable and future-oriented makes it particularly valuable and practical for data science. As a new data scientist, you'd find it easy to use it because of its simple syntax, excellent readability and its increased availability of mining tools that make it easy to handle data. But how would you get started with it? How do you work with it? How do you set it up? If you still have such questions, then this book is all you need. It gives you the ins and outs of python as a data science solution, including how you can set it up, use it and what you need to get started. Here's a bit of what you can expect to learn from it: What data science is and why we need to learn and apply it in daily life The different types of data scientists we have The requirement and tools for data science The difference between data science and business intelligence The lifecycle of data science The components of data science How data science is being used today How to set up the Python environment in Linux, Mac and Windows How the Anaconda environment looks and works Basic Python concepts to refresh your mind How to work with Python for data analysis How to work out data visualization with Matplotlib and Seaborn ...And much more! Are you ready to enjoy data science more by achieving better insight, understanding patterns and correlating data from big datasets? Do you want to be able to develop machine learning models, web services, data mining, classification and so much more, while solving problems end to end effortlessly and within an incredibly short duration? If you do, then this simple, straightforward and comprehensive beginners' book will help you get there in just a few hours! Even if the concept of data science seems too complex and out there right now, this book will break the topic using simple, straightforward language that you can start using right away to your advantage! Scroll up and click Buy Now With 1-Click or Buy Now to get started!

Marketing Data Science

Marketing Data Science PDF
Author: Thomas W. Miller
Publisher: FT Press
Size: 36.50 MB
Category : Business & Economics
Languages : en
Pages : 225
View: 3070

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Marketing Data Science

by Thomas W. Miller, Marketing Data Science Books available in PDF, EPUB, Mobi Format. Download Marketing Data Science books, Now , a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.

Grundkurs K Nstliche Intelligenz

Grundkurs K  nstliche Intelligenz PDF
Author: Wolfgang Ertel
Publisher: Springer-Verlag
Size: 45.16 MB
Category : Computers
Languages : de
Pages : 334
View: 133

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Grundkurs K Nstliche Intelligenz

by Wolfgang Ertel, Grundkurs K Nstliche Intelligenz Books available in PDF, EPUB, Mobi Format. Download Grundkurs K Nstliche Intelligenz books, Alle Teilgebiete der KI werden mit dieser Einführung kompakt, leicht verständlich und anwendungsbezogen dargestellt. Hier schreibt jemand, der das Gebiet nicht nur bestens kennt, sondern auch in der Lehre engagiert und erfolgreich vertritt. Von der klassischen Logik über das Schließen mit Unsicherheit und maschinelles Lernen bis hin zu Anwendungen wie Expertensysteme oder lernfähige Roboter. Sie werden von dem sehr guten Überblick in dieses faszinierende Teilgebiet der Informatik profitieren. Und Sie gewinnen vertiefte Kenntnisse, z. B. hinsichtlich der wichtigsten Verfahren zur Repräsentation und Verarbeitung von Wissen. Vor allem steht der Anwendungsbezug im Fokus der Darstellung. Viele Übungsaufgaben mit Lösungen sowie eine strukturierte Liste mit Verweisen auf Literatur und Ressourcen im Web ermöglichen ein effektives und kurzweiliges Selbststudium. "Wolfgang Ertel [...] schafft es auf rund 300 Seiten verständlich zu erklären, wie Aussagenlogik, maschinelles Lernen und neuronale Netze die Grundlagen für künstliche Intelligenz bilden." Technology Review 04/2008

Applied Text Analysis With Python

Applied Text Analysis with Python PDF
Author: Benjamin Bengfort
Publisher: "O'Reilly Media, Inc."
Size: 33.91 MB
Category : Computers
Languages : en
Pages : 332
View: 3112

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Applied Text Analysis With Python

by Benjamin Bengfort, Applied Text Analysis With Python Books available in PDF, EPUB, Mobi Format. Download Applied Text Analysis With Python books, From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity