Search Results for "data-analytics-for-beginners-a-beginner-s-guide-to-learn-and-master-data-analytics"

Data Analytics for Beginners

Data Analytics for Beginners

A Beginner's Guide to Learn and Master Data Analytics

  • Author: Robert J. Woz
  • Publisher: Createspace Independent Publishing Platform
  • ISBN: 9781977843135
  • Category: Big data
  • Page: 112
  • View: 7879
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If you are convinced that the world today is producing more data than the previous decades, then you understand that processing yesterday's data for today's use at times is not enough. The level of data analysis that is needed in highly competitive business environment needs to be processed, analyzed and used immediately for businesses to be ahead of their competition. Having this in mind, you need to understand from the ground up, what data is, the different types of data and how you should identify the right data for your business. To help you understand the simple basics of data and how it needs to be analyzed, then Data Analytics for Beginners is the book that you have been waiting for. The size and type of business you are running doesn't matter because after all, it will depend on your ability to understand the data that your business is exposed to so as to make better business decisions for the current working environment and the future. Are there patterns in your business that you cannot see? Do you want to make sense of the shopping trends of your clients to better enrich their experience? Do you want to know your target market even more? Do you want to better derive insights from the feedback your clients give you? These questions can only be answered when you perform a data analysis for your business. Collecting the data is one thing, analyzing them is another matter entirely as it is not something that can be done haphazardly by just looking at the data. If you hope to understand your data well, you need to understand the data you are collecting, the methods to use and the right tools to use when analyzing the data. Inside you will find valuable steps and tools that will help make your information work for you. Do not let yourself get complacent, stop looking at the data that you collect each day and start analyzing your data to move your business up. Get started by buying this book today! Inside you will find How data should be understood? Terms and concepts used in data analysis. Data mining and the different kinds of databases used to store data. How information can be retrieved and manipulated in the database to create a visual representation of what you want to know? The life cycle of data analysis. And more...

Data Science für Dummies

Data Science für Dummies

  • Author: Lillian Pierson
  • Publisher: John Wiley & Sons
  • ISBN: 352780675X
  • Category: Mathematics
  • Page: 382
  • View: 2478
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Daten, Daten, Daten? Sie haben schon Kenntnisse in Excel und Statistik, wissen aber noch nicht, wie all die Datensätze helfen sollen, bessere Entscheidungen zu treffen? Von Lillian Pierson bekommen Sie das dafür notwendige Handwerkszeug: Bauen Sie Ihre Kenntnisse in Statistik, Programmierung und Visualisierung aus. Nutzen Sie Python, R, SQL, Excel und KNIME. Zahlreiche Beispiele veranschaulichen die vorgestellten Methoden und Techniken. So können Sie die Erkenntnisse dieses Buches auf Ihre Daten übertragen und aus deren Analyse unmittelbare Schlüsse und Konsequenzen ziehen.

Data Analytics for Beginners

Data Analytics for Beginners

Your Ultimate Guide to Learn and Master Data Analysis - Get Your Business Intelligence Right and Accelerate Growth

  • Author: Victor Finch
  • Publisher: Createspace Independent Publishing Platform
  • ISBN: 9781546641919
  • Category:
  • Page: 128
  • View: 6959
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Data Analytics for Beginners Your Ultimate Guide To Learn and Master Data Analytics. Get Your Business Intelligence Right - Accelerate Growth and Close More Sales Leading companies must not only compete on faster ROI within the shortest time but also face stiff competition in this digital frontier age with no boundaries but continual evolution. Time is precious and marketing effort is worthless without information knowledge and precision execution. Have you ever pause and wonder why your marketing effort is not as successful as expected? Data analytics could be your answer to turn sales around. Data analytics provides the only hope for fact-based and insightful-driven decisions can help companies manage their strategic, operating and financial performance. That's why it's no longer tenable to ignore data analytics. This book has been written with a beginner in mind. If that sound good, you just need to pick this book up and get ready to dive into the basic of Data Analytics What you will learn in Data Analytics For Beginners: Your Ultimate Guide To Learn and Master Data Analytics. Get Your Business Intelligence Right - Accelerate Growth and Close More Sales You will be expose to the big picture of Business Intelligence Data Analytics and its competitive advantages You will learn what are the different types of Data Analytics You will what is data mining in details and how can it work for you You will have a practical introduction on the four important steps in Data Analytics and explore the data analytics patterns BONUS #1: 3 Case Studies on how companies implemented BI and Data Analytics to spur new growth in their business. BONUS #2: A NEW exciting frontier for Data Analytics And many more.. This Data Analytics For Beginners: Your Ultimate Guide To Learn and Master Data Analytics. Get Your Business Intelligence Right - Accelerate Growth and Close More Sales is your must have guide to open up the possibility of data analysis could matters to your business. Download Data Analytics For Beginners: Your Ultimate Guide To Learn and Master Data Analytics. Get Your Business Intelligence Right - Accelerate Growth and Close More Sales The Bottom Line: Most businesses are sitting on their huge sales or traffic data and doing nothing about them. If they have ever though of diving deeper into the data, potential "goldmines" could be discover and within easy reach. What are you waiting for? Start today by making the smartest investment you could possibly make. An investment in yourself, your knowledge and your growth. Don't hesitate to pick up your copy today by clicking the BUY NOW button at the top of this page!

Datenanalyse mit Python

Datenanalyse mit Python

Auswertung von Daten mit Pandas, NumPy und IPython

  • Author: Wes McKinney
  • Publisher: O'Reilly
  • ISBN: 3960102143
  • Category: Computers
  • Page: 542
  • View: 1491
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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 ComputingLernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennenSetzen Sie die Datenanalyse-Tools der pandasBibliothek einVerwenden Sie flexible Werkzeuge zum Laden, Bereinigen, Transformieren, Zusammenführen und Umformen von DatenErstellen Sie interformative Visualisierungen mit matplotlibWenden Sie die GroupBy-Mechanismen von pandas an, um Datensätzen zurechtzuschneiden, umzugestalten und zusammenzufassenAnalysieren und manipulieren Sie verschiedenste Zeitreihen-DatenFür 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.

Statistik-Workshop für Programmierer

Statistik-Workshop für Programmierer

  • Author: Allen B. Downey
  • Publisher: O'Reilly Germany
  • ISBN: 3868993436
  • Category: Computers
  • Page: 160
  • View: 2749
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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.

Data Analytics for Beginners

Data Analytics for Beginners

Practical Guide to Master Data Analytics

  • Author: Tech World
  • Publisher: Createspace Independent Publishing Platform
  • ISBN: 9781547016280
  • Category: Big data
  • Page: 78
  • View: 5735
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DATA ANALYTICS FOR BEGINNERS Are you ready to discover why data analytics is the only hope for fact based decisions? Would you like learn how insightful-driven decisions can help organizations manage their strategic, operation and financial performance that can help them increase their shareholder value? This book explores all the concepts about data analytics that can help any beginner to master data analytics and its applications in several industries. Chapter one provides an overview of data analytics where the foundations of data analytics are explained in details. If you want to get started right away, you'll also learn the requirements for data scientists in this chapter. In chapter two, a detailed discourse on conducting analytic data research is provided to give you a big picture view of how data analytics is done. In chapter three, descriptive statistics is explored where you'll learn measures of central tendency and measures of dispersion. Chapter four reviews all the charts and graphs that you can use to communicate your analytic results. In chapter five, you'll learn the applications of data analysis in organizations. Finally, in chapter six, you'll learn some of the valuable tools that can help you advance your professional career in data analytics. You'll also learn why smart contracts are emerging as the next technologies for smart data analysis. Take action today and discover the power of Data Analytics DOWNLOAD YOUR COPY TODAY

Mastering Python for Data Science

Mastering Python for Data Science

  • Author: Samir Madhavan
  • Publisher: Packt Publishing Ltd
  • ISBN: 1784392626
  • Category: Computers
  • Page: 294
  • View: 6632
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Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.

Big Data

Big Data

Die Revolution, die unser Leben verändern wird

  • Author: Viktor Mayer-Schönberger,Viktor; Cukier Mayer-Schönberger
  • Publisher: Redline Wirtschaft
  • ISBN: 3864144590
  • Category: Political Science
  • Page: 288
  • View: 1215
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Ob Kaufverhalten, Grippewellen oder welche Farbe am ehesten verrät, ob ein Gebrauchtwagen in einem guten Zustand ist – noch nie gab es eine solche Menge an Daten und noch nie bot sich die Chance, durch Recherche und Kombination in der Daten¬flut blitzschnell Zusammenhänge zu entschlüsseln. Big Data bedeutet nichts weniger als eine Revolution für Gesellschaft, Wirtschaft und Politik. Es wird die Weise, wie wir über Gesundheit, Erziehung, Innovation und vieles mehr denken, völlig umkrempeln. Und Vorhersagen möglich machen, die bisher undenkbar waren. Die Experten Viktor Mayer-Schönberger und Kenneth Cukier beschreiben in ihrem Buch, was Big Data ist, welche Möglichkeiten sich eröffnen, vor welchen Umwälzungen wir alle stehen – und verschweigen auch die dunkle Seite wie das Ausspähen von persönlichen Daten und den drohenden Verlust der Privatsphäre nicht.

Predictive Analytics für Dummies

Predictive Analytics für Dummies

  • Author: Anasse Bari,Mohamed Chaouchi,Tommy Jung
  • Publisher: John Wiley & Sons
  • ISBN: N.A
  • Category:
  • Page: 360
  • View: 9729
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Mastering Data Analysis with R

Mastering Data Analysis with R

  • Author: Gergely Daroczi
  • Publisher: Packt Publishing Ltd
  • ISBN: 1783982039
  • Category: Computers
  • Page: 396
  • View: 3122
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Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.

Beginning Data Science with R

Beginning Data Science with R

  • Author: Manas A. Pathak
  • Publisher: Springer
  • ISBN: 3319120662
  • Category: Mathematics
  • Page: 157
  • View: 2371
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“We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library. The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.

Data Science from Scratch with Python

Data Science from Scratch with Python

Step-By-Step Guide

  • Author: Peter Morgan
  • Publisher: Createspace Independent Publishing Platform
  • ISBN: 9781726020688
  • Category: Data mining
  • Page: 168
  • View: 1222
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***** BUY NOW (will soon return to 24.77 $) ***** MONEY BACK GUARANTEE BY AMAZON (See Below FAQ) *****Are you thinking of learning data science from scratch using Python? (For Beginners)If you are looking for a complete step-by-step guide to data science using Python from scratch, this book is for you. After his great success with his first book "Data Analysis from Scratch with Python," Peter Morgan publishes his second book focusing now in data science and machine learning. It is considered by practitioners as the easiest guide ever written in this domain. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations. Step by Step Guide and Visual Illustrations and ExamplesThe Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn, pandas, NumPy, IPython, and Jupiter in the Process. Target Users Beginners who want to approach data science, but are too afraid of complex math to start Newbies in computer science techniques and data science Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on data science What's Inside This Book? Part 1: Data Science Fundamentals, Concepts and Algorithms Introduction Statistics Probability Bayes' Theorem and Naïve Bayes Algorithm Asking the Right Question Data Acquisition Data Preparation Data Exploration Data Modelling Data Presentation Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and Underfitting The Bias-Variance Trade-off Feature Extraction and Selection Part 2: Data Science in Practice Overview of Python Programming Language Python Data Science Tools Jupyter Notebook Numerical Python (Numpy) Pandas Scientific Python (Scipy) Matplotlib Scikit-Learn K-Nearest Neighbors Naive Bayes Simple and Multiple Linear Regression Logistic Regression GLM models Decision Trees and Random forest Perceptrons Backpropagation Clustering Natural Language Processing Frequently Asked Questions Q: Does this book include everything I need to become a data science expert?A: Unfortunately, no. This book is designed for readers taking their first steps in data science and machine learning using Python and further learning will be required beyond this book to master all aspects. Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform.***** MONEY BACK GUARANTEE BY AMAZON ***** Editorial Reviews "This is a fantastic book on Python-based data science, data analysis, machine learning, Reinforcement learning and deep learning. As a data scientist with more than 10 years, Peter has had long experience in data science and give in this book the key elements.." - Lei Xia, Data Scientist Expert at Facebook

Completing Your Qualitative Dissertation: A Road Map From Beginning to End

Completing Your Qualitative Dissertation: A Road Map From Beginning to End

A Road Map From Beginning to End

  • Author: Linda Dale Bloomberg,Marie Volpe
  • Publisher: SAGE
  • ISBN: 1452202702
  • Category: Language Arts & Disciplines
  • Page: 312
  • View: 8705
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Graduate students often struggle with turning qualitative research projects into a master's thesis or doctoral dissertation because the research itself is inherently messy. Completing Your Qualitative Dissertation: A Roadmap From Beginning to End, Second Edition helps address that challenge. Linda Dale Bloomberg and Marie Volpe have distilled decades of experience of dissertation advisement and successful doctoral defenses into this highly practical reference for graduate students. They focus on finding and articulating a clear research problem, purpose, and questions; laying out a research design that will lead to gathering the right kind of data and support the right kind of analysis; and writing up and defending the study. To help, they provide working tools, an integrative summary discussion at the end of each chapter, comprehensive checkists, and an annotated bibliography in each chapter.

Data Science mit Python

Data Science mit Python

Das Handbuch für den Einsatz von IPython, Jupyter, NumPy, Pandas, Matplotlib und Scikit-Learn

  • Author: Jake VanderPlas
  • Publisher: MITP-Verlags GmbH & Co. KG
  • ISBN: 3958456979
  • Category: Computers
  • Page: 552
  • View: 5099
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Die wichtigsten Tools für die Datenanalyse und-bearbeitung im praktischen Einsatz Python effizient für datenintensive Berechnungen einsetzen mit IPython und Jupyter Laden, Speichern und Bearbeiten von Daten und numerischen Arrays mit NumPy und Pandas Visualisierung von Daten mit Matplotlib Python ist für viele die erste Wahl für Data Science, weil eine Vielzahl von Ressourcen und Bibliotheken zum Speichern, Bearbeiten und Auswerten von Daten verfügbar ist. In diesem Buch erläutert der Autor den Einsatz der wichtigsten Tools. Für Datenanalytiker und Wissenschaftler ist dieses umfassende Handbuch von unschätzbarem Wert für jede Art von Berechnung mit Python sowie bei der Erledigung alltäglicher Aufgaben. Dazu gehören das Bearbeiten, Umwandeln und Bereinigen von Daten, die Visualisierung verschiedener Datentypen und die Nutzung von Daten zum Erstellen von Statistiken oder Machine-Learning-Modellen. Dieses Handbuch erläutert die Verwendung der folgenden Tools: ● IPython und Jupyter für datenintensive Berechnungen ● NumPy und Pandas zum effizienten Speichern und Bearbeiten von Daten und Datenarrays in Python ● Matplotlib für vielfältige Möglichkeiten der Visualisierung von Daten ● Scikit-Learn zur effizienten und sauberen Implementierung der wichtigsten und am meisten verbreiteten Algorithmen des Machine Learnings Der Autor zeigt Ihnen, wie Sie die zum Betreiben von Data Science verfügbaren Pakete nutzen, um Daten effektiv zu speichern, zu handhaben und Einblick in diese Daten zu gewinnen. Grundlegende Kenntnisse in Python werden dabei vorausgesetzt. Leserstimme zum Buch: »Wenn Sie Data Science mit Python betreiben möchten, ist dieses Buch ein hervorragender Ausgangspunkt. Ich habe es sehr erfolgreich beim Unterrichten von Informatik- und Statistikstudenten eingesetzt. Jake geht weit über die Grundlagen der Open-Source-Tools hinaus und erläutert die grundlegenden Konzepte, Vorgehensweisen und Abstraktionen in klarer Sprache und mit verständlichen Erklärungen.« – Brian Granger, Physikprofessor, California Polytechnic State University, Mitbegründer des Jupyter-Projekts

Seven Steps to Mastering Business Analysis

Seven Steps to Mastering Business Analysis

  • Author: Barbara A. Carkenord
  • Publisher: J. Ross Publishing
  • ISBN: 1604270071
  • Category: Business & Economics
  • Page: 384
  • View: 3027
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"This book provides a "how to" approach to mastering business analysis work. It will help build the skill sets of new analysts and all those currently doing analysis work, from project managers to project team members such as systems analysts, product managers and business development professionals, to the experienced business analyst. It also covers the tasks and knowledge areas for the new 2008 v.2 of The Guide to the Business Analysis Body of Knowledge (BABOK) and will help prepare business analysts for the HBA CBAP certification exam."--BOOK JACKET.

Big Data Analytics Strategies for the Smart Grid

Big Data Analytics Strategies for the Smart Grid

  • Author: Carol L. Stimmel
  • Publisher: CRC Press
  • ISBN: 1482218291
  • Category: Computers
  • Page: 256
  • View: 7480
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By implementing a comprehensive data analytics program, utility companies can meet the continually evolving challenges of modern grids that are operationally efficient, while reconciling the demands of greenhouse gas legislation and establishing a meaningful return on investment from smart grid deployments. Readable and accessible, Big Data Analytics Strategies for the Smart Grid addresses the needs of applying big data technologies and approaches, including Big Data cybersecurity, to the critical infrastructure that makes up the electrical utility grid. It supplies industry stakeholders with an in-depth understanding of the engineering, business, and customer domains within the power delivery market. The book explores the unique needs of electrical utility grids, including operational technology, IT, storage, processing, and how to transform grid assets for the benefit of both the utility business and energy consumers. It not only provides specific examples that illustrate how analytics work and how they are best applied, but also describes how to avoid potential problems and pitfalls. Discussing security and data privacy, it explores the role of the utility in protecting their customers’ right to privacy while still engaging in forward-looking business practices. The book includes discussions of: SAS for asset management tools The AutoGrid approach to commercial analytics Space-Time Insight’s work at the California ISO (CAISO) This book is an ideal resource for mid- to upper-level utility executives who need to understand the business value of smart grid data analytics. It explains critical concepts in a manner that will better position executives to make the right decisions about building their analytics programs. At the same time, the book provides sufficient technical depth that it is useful for data analytics professionals who need to better understand the nuances of the engineering and business challenges unique to the utilities industry.

Applying Analytics

Applying Analytics

A Practical Introduction

  • Author: E. S. Levine
  • Publisher: CRC Press
  • ISBN: 1466557192
  • Category: Business & Economics
  • Page: 290
  • View: 8675
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Newcomers to quantitative analysis need practical guidance on how to analyze data in the real world yet most introductory books focus on lengthy derivations and justifications instead of practical techniques. Covering the technical and professional skills needed by analysts in the academic, private, and public sectors, Applying Analytics: A Practical Introduction systematically teaches novices how to apply algorithms to real data and how to recognize potential pitfalls. It offers one of the first textbooks for the emerging first course in analytics. The text concentrates on the interpretation, strengths, and weaknesses of analytical techniques, along with challenges encountered by analysts in their daily work. The author shares various lessons learned from applying analytics in the real world. He supplements the technical material with coverage of professional skills traditionally learned through experience, such as project management, analytic communication, and using analysis to inform decisions. Example data sets used in the text are available for download online so that readers can test their own analytic routines. Suitable for beginning analysts in the sciences, business, engineering, and government, this book provides an accessible, example-driven introduction to the emerging field of analytics. It shows how to interpret data and identify trends across a range of fields.

Statistics Essentials for Beginner in Data Science

Statistics Essentials for Beginner in Data Science

  • Author: Jay Mishra
  • Publisher: AI Sciences LLC
  • ISBN: 9781733570640
  • Category:
  • Page: 112
  • View: 6356
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***** BUY NOW (will soon return to 24.95 $) *****Are you thinking of learning Statistics fundamentals for Data Science? If you are looking for a beginner book to master Statistics Learning fundamentals for Data Science, this book is for you. Who Should Read this Book?Aspiring data scientists who are looking forward to begin their journey in the vast field of data science. People who are seeking to learn and understand data analysis from its very deep-rooted basics have found the right book. Clear basic concepts make the foundation of a good knowledge base, which ultimately helps to gain sharp insights into this topic further. This book will give you the practical exposure along with its theory explained comprehensively. This book is the perfect compilation for beginners as well as intermediate learners who intend to learn statistics and data analysis techniques. Why this book?This book will guide you step by step from the very basics to how you can start your own data science project. The best part about this book is its structure, it's structured in such a way that integrates practicals along with its theory to make the concepts easily understandable. It will help you to understand a basic concept like mean, median, mode, scatter plot and histograms. Thus ensures no prior knowledge is required to start learning from this book. The content of this book is specially designed to encompass all the concepts that come under the domain of data science. This book will guide you through the problems and concepts of statistics. What is statistics?h2>Most of the people think statistics in data science is something different and more profound than what we learnt in our mathematics classes but it's not. It is the same concept of data collection followed by its organization, interpretation and presentation. Statistics is the key to develop a desired model in machine learning. Using statistics you can convert your raw meaningless chunk of data to a well-structured informative data. What's Inside This Book? Probability & Bayes Theorem, Data Exploration and Analysis Structured Data Estimates Mean and Median Estimates Variability Exploring the data distribution Percentiles and Boxplots Frequency table and Histograms Density Estimates Mode Correlation Categorical and Numeric Data Visualizing Multiple Variables Regression Analysis Clustering Analysis Statistical tests and ANOVA Classification Naïve Bayes Discriminant Analysis Linear regression Logistic Regression Statistical Machine Learning K_Nearest Neighbor Trees Models Bagging and Random Forest Boosting algorithms Principal Component Analysis K_means Clustering Hierarchical Clustering Model Based Clustering Sources & References From AI Sciences PublishingOur books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations.Frequently Asked QuestionsQ: Does this book include everything I need to become a data analyst expert?A: Unfortunately, no. This book is designed for readers taking their first steps in statistics and data science and further learning will be required beyond this book to master all aspects. Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform.***** MONEY BACK GUARANTEE BY AMAZON *****

Routineaufgaben mit Python automatisieren

Routineaufgaben mit Python automatisieren

Praktische Programmierlösungen für Einsteiger

  • Author: Al Sweigart
  • Publisher: dpunkt.verlag
  • ISBN: 3864919932
  • Category: Computers
  • Page: 576
  • View: 6287
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Wenn Sie jemals Stunden damit verbracht haben, Dateien umzubenennen oder Hunderte von Tabelleneinträgen zu aktualisieren, dann wissen Sie, wie stumpfsinnig manche Tätigkeiten sein können. Wie wäre es, den Computer dazu zu bringen, diese Arbeiten zu übernehmen? In diesem Buch lernen Sie, wie Sie mit Python Aufgaben in Sekundenschnelle erledigen können, die sonst viel Zeit in Anspruch nehmen würden. Programmiererfahrung brauchen Sie dazu nicht: Wenn Sie einmal die Grundlagen gemeistert haben, werden Sie Python-Programme schreiben, die automatisch alle möglichen praktischen Aufgaben für Sie abarbeiten: • eine oder eine Vielzahl von Dateien nach Texten durchsuchen • Dateien und Ordner erzeugen, aktualisieren, verschieben und umbenennen • das Web durchsuchen und Inhalte herunterladen • Excel-Dateien aktualisieren und formatieren • PDF-Dateien teilen, zusammenfügen, mit Wasserzeichen versehen und verschlüsseln • Erinnerungsmails und Textnachrichten verschicken • Online-Formulare ausfüllen Schritt-für-Schritt-Anleitungen führen Sie durch jedes Programm und Übungsaufgaben am Ende jedes Kapitels fordern Sie dazu auf, die Programme zu verbessern und Ihre Fähigkeiten auf ähnliche Problemstellungen zu richten. Verschwenden Sie nicht Ihre Zeit mit Aufgaben, die auch ein gut dressierter Affe erledigen könnte. Bringen Sie Ihren Computer dazu, die langweilige Arbeit zu machen!