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Books > Computing & IT > Applications of computing > Databases > Data capture & analysis
Informationsmanagement hat die Aufgabe, den im Hinblick auf das
Unternehmensziel bestmoeglichen Einsatz der Ressource Information
zu gewahrleisten. Dieses Buch vermittelt die zentrale Einsicht,
dass Informations- und Kommunikationstechniken nicht nur
Rationalisierungsmoeglichkeiten eroeffnen, sondern vor allem
Gestaltungsmoeglichkeiten fur Organisation und neue
Geschaftsmodelle bieten. Somit kann der Leser die unternehmerische
und gesellschaftliche Bedeutung von Information sowie die
Potenziale informationsverarbeitender Systeme einschatzen. Hierzu
erhalt er einen fundierten Einblick in die Systeme, die
Informationen verarbeiten, speichern und ubertragen, aber auch in
die Techniken, auf denen sie beruhen. Daruber hinaus werden dem
Leser auch die Fuhrungsaufgaben des Informationsmanagements
verstandlich gemacht. Neben den theoretischen Grundlagen vermittelt
dieses Buch konkretes Methodenwissen und richtet sich somit an
Studierende wie Praktiker. Unterstutzung leistet eine an die
Struktur des Buches angelehnte UEbungsfallstudie.
Reinforce your understanding of data science and data analysis from
a statistical perspective to extract meaningful insights from your
data using Python programming Key Features Work your way through
the entire data analysis pipeline with statistics concerns in mind
to make reasonable decisions Understand how various data science
algorithms function Build a solid foundation in statistics for data
science and machine learning using Python-based examples Book
DescriptionStatistics remain the backbone of modern analysis tasks,
helping you to interpret the results produced by data science
pipelines. This book is a detailed guide covering the math and
various statistical methods required for undertaking data science
tasks. The book starts by showing you how to preprocess data and
inspect distributions and correlations from a statistical
perspective. You'll then get to grips with the fundamentals of
statistical analysis and apply its concepts to real-world datasets.
As you advance, you'll find out how statistical concepts emerge
from different stages of data science pipelines, understand the
summary of datasets in the language of statistics, and use it to
build a solid foundation for robust data products such as
explanatory models and predictive models. Once you've uncovered the
working mechanism of data science algorithms, you'll cover
essential concepts for efficient data collection, cleaning, mining,
visualization, and analysis. Finally, you'll implement statistical
methods in key machine learning tasks such as classification,
regression, tree-based methods, and ensemble learning. By the end
of this Essential Statistics for Non-STEM Data Analysts book,
you'll have learned how to build and present a self-contained,
statistics-backed data product to meet your business goals. What
you will learn Find out how to grab and load data into an analysis
environment Perform descriptive analysis to extract meaningful
summaries from data Discover probability, parameter estimation,
hypothesis tests, and experiment design best practices Get to grips
with resampling and bootstrapping in Python Delve into statistical
tests with variance analysis, time series analysis, and A/B test
examples Understand the statistics behind popular machine learning
algorithms Answer questions on statistics for data scientist
interviews Who this book is forThis book is an entry-level guide
for data science enthusiasts, data analysts, and anyone starting
out in the field of data science and looking to learn the essential
statistical concepts with the help of simple explanations and
examples. If you're a developer or student with a non-mathematical
background, you'll find this book useful. Working knowledge of the
Python programming language is required.
This report discusses the role computer-assisted personal
interviewing (CAPI) can play in transforming survey data collection
to allow better monitoring of the Sustainable Development Goals.
The first part of this publication provides rigorous quantitative
evidence on why CAPI is a better alternative to the traditional pen
and paper interviewing method, particularly in the context of
nationally representative surveys. The second part discusses the
benefits of delivering CAPI training to statisticians using the
popular massive online open course format. The final part provides
a summary of existing CAPI platforms and offers some preliminary
advice for NSOs to consider when selecting a CAPI platform for
their institution. This is a Special Supplement to the Key
Indicators for Asia and the Pacific 2019.
Als Stahl bezeichnet man heute alle Eisenlegierungen - mit Ausnahme
der nicht schmiedbaren hochkohlenstoffhaltigen Gu sorten wie
Grauguli, Hartguf und Ternperguf - ohne Riicksichr auf ihre
Eigenschaften. Friiher wurde als wesentliches Merkmal des Stahles
die Hartbarkeit angesehen. Es gibt aber eine ganze Reihe von
Stahlen, die sich nicht harten lassen, die durch das Abschrecken
aus hohen Temperaturen im Gegenteil sogar weicher, zaher werden.
Edelstdble werden vielfach solche Stahle genannt, die au er mit
Kohlenstoff auch noch mit anderen Grundstoffen, z. B. mit Chrom,
Nickel, Wolfram, Vanadin usw. legiert sind. Diese Begriffsbestim-
mung ist jedoch nicht erschopfend und auch anfechtbar, Denn man
wird einen reinen Kohlenstoffstahl, der sorgfaltig erzeugt und auf
dem ganzen Wege der Herstellung - vom Gu bis zum Versand - immer
wieder gewissenhaft gepriift worden ist, zweifellos auch zu den
Edelstahlen rechnen miissen. Andererseits enthalten manchmal
Massenstahle - auch als unbeabsichtigte Verunreinigungen - ge-
wisse Mengen von Legierungselementen. Das Richtige wird man
treffen, wenn man die bei den grofsen Hiittenwerken in grofien
Mengen erzeugten billigen Stahle als .Mas- senstahle bezeichnet,
die von einem Edelstahlwerk mit Sorgfalt und unter scharfster
Kontrolle hergestellten Stahle dagegen als Edelstahle. Die billigen
Massenstahle werden meistens nach Festigkeit ver- kauft, die
Edelstahle dagegen nach dem Verwendungszweck und unter einer
Markenbezeichnung.
Work through practical recipes to learn how to solve complex
machine learning and deep learning problems using Python Key
Features Get up and running with artificial intelligence in no time
using hands-on problem-solving recipes Explore popular Python
libraries and tools to build AI solutions for images, text, sounds,
and images Implement NLP, reinforcement learning, deep learning,
GANs, Monte-Carlo tree search, and much more Book
DescriptionArtificial intelligence (AI) plays an integral role in
automating problem-solving. This involves predicting and
classifying data and training agents to execute tasks successfully.
This book will teach you how to solve complex problems with the
help of independent and insightful recipes ranging from the
essentials to advanced methods that have just come out of research.
Artificial Intelligence with Python Cookbook starts by showing you
how to set up your Python environment and taking you through the
fundamentals of data exploration. Moving ahead, you'll be able to
implement heuristic search techniques and genetic algorithms. In
addition to this, you'll apply probabilistic models, constraint
optimization, and reinforcement learning. As you advance through
the book, you'll build deep learning models for text, images,
video, and audio, and then delve into algorithmic bias, style
transfer, music generation, and AI use cases in the healthcare and
insurance industries. Throughout the book, you'll learn about a
variety of tools for problem-solving and gain the knowledge needed
to effectively approach complex problems. By the end of this book
on AI, you will have the skills you need to write AI and machine
learning algorithms, test them, and deploy them for production.
What you will learn Implement data preprocessing steps and optimize
model hyperparameters Delve into representational learning with
adversarial autoencoders Use active learning, recommenders,
knowledge embedding, and SAT solvers Get to grips with
probabilistic modeling with TensorFlow probability Run object
detection, text-to-speech conversion, and text and music generation
Apply swarm algorithms, multi-agent systems, and graph networks Go
from proof of concept to production by deploying models as
microservices Understand how to use modern AI in practice Who this
book is forThis AI machine learning book is for Python developers,
data scientists, machine learning engineers, and deep learning
practitioners who want to learn how to build artificial
intelligence solutions with easy-to-follow recipes. You'll also
find this book useful if you're looking for state-of-the-art
solutions to perform different machine learning tasks in various
use cases. Basic working knowledge of the Python programming
language and machine learning concepts will help you to work with
code effectively in this book.
Get up to speed with the new features added to Microsoft SQL Server
2019 Analysis Services and create models to support your business
Key Features Explore tips and tricks to design, develop, and
optimize end-to-end data analytics solutions using Microsoft's
technologies Learn tabular modeling and multi-dimensional cube
design development using real-world examples Implement Analysis
Services to help you make productive business decisions Book
DescriptionSQL Server Analysis Services (SSAS) continues to be a
leading enterprise-scale toolset, enabling customers to deliver
data and analytics across large datasets with great performance.
This book will help you understand MS SQL Server 2019's new
features and improvements, especially when it comes to SSAS. First,
you'll cover a quick overview of SQL Server 2019, learn how to
choose the right analytical model to use, and understand their key
differences. You'll then explore how to create a multi-dimensional
model with SSAS and expand on that model with MDX. Next, you'll
create and deploy a tabular model using Microsoft Visual Studio and
Management Studio. You'll learn when and how to use both tabular
and multi-dimensional model types, how to deploy and configure your
servers to support them, and design principles that are relevant to
each model. The book comes packed with tips and tricks to build
measures, optimize your design, and interact with models using
Excel and Power BI. All this will help you visualize data to gain
useful insights and make better decisions. Finally, you'll discover
practices and tools for securing and maintaining your models once
they are deployed. By the end of this MS SQL Server book, you'll be
able to choose the right model and build and deploy it to support
the analytical needs of your business. What you will learn
Determine the best analytical model using SSAS Cover the core
aspects involved in MDX, including writing your first query
Implement calculated tables and calculation groups (new in version
2019) in DAX Create and deploy tabular and multi-dimensional models
on SQL 2019 Connect and create data visualizations using Excel and
Power BI Implement row-level and other data security methods with
tabular and multi-dimensional models Explore essential concepts and
techniques to scale, manage, and optimize your SSAS solutions Who
this book is forThis Microsoft SQL Server book is for BI
professionals and data analysts who are looking for a practical
guide to creating and maintaining tabular and multi-dimensional
models using SQL Server 2019 Analysis Services. A basic working
knowledge of BI solutions such as Power BI and database querying is
required.
Analyzing data sets has continued to be an invaluable application
for numerous industries. By combining different algorithms,
technologies, and systems used to extract information from data and
solve complex problems, various sectors have reached new heights
and have changed our world for the better. The Handbook of Research
on Engineering, Business, and Healthcare Applications of Data
Science and Analytics is a collection of innovative research on the
methods and applications of data analytics. While highlighting
topics including artificial intelligence, data security, and
information systems, this book is ideally designed for researchers,
data analysts, data scientists, healthcare administrators,
executives, managers, engineers, IT consultants, academicians, and
students interested in the potential of data application
technologies.
Ask questions of your data and gain insights to make better
business decisions using the open source business intelligence
tool, Metabase Key Features Deploy Metabase applications to let
users across your organization interact with it Learn to create
data visualizations, charts, reports, and dashboards with the help
of a variety of examples Understand how to embed Metabase into your
website and send out reports automatically using email and Slack
Book DescriptionMetabase is an open source business intelligence
tool that helps you use data to answer questions about your
business. This book will give you a detailed introduction to using
Metabase in your organization to get the most value from your data.
You'll start by installing and setting up Metabase on your local
computer. You'll then progress to handling the administration
aspect of Metabase by learning how to configure and deploy
Metabase, manage accounts, and execute administrative tasks such as
adding users and creating permissions and metadata. Complete with
examples and detailed instructions, this book shows you how to
create different visualizations, charts, and dashboards to gain
insights from your data. As you advance, you'll learn how to share
the results with peers in your organization and cover
production-related aspects such as embedding Metabase and auditing
performance. Throughout the book, you'll explore the entire data
analytics process-from connecting your data sources, visualizing
data, and creating dashboards through to daily reporting. By the
end of this book, you'll be ready to implement Metabase as an
integral tool in your organization. What you will learn Explore
different types of databases and find out how to connect them to
Metabase Deploy and host Metabase securely using Amazon Web
Services Use Metabase's user interface to filter and aggregate data
on single and multiple tables Become a Metabase admin by learning
how to add users and create permissions Answer critical questions
for your organization by using the Notebook editor and writing SQL
queries Use the search functionality to search through tables,
dashboards, and metrics Who this book is forThis book is for
business analysts, data analysts, data scientists, and other
professionals who want to become well-versed with business
intelligence and analytics using Metabase. This book will also
appeal to anyone who wants to understand their data to extract
meaningful insights with the help of practical examples. A basic
understanding of data handling and processing is necessary to get
started with this book.
FINALIST: Business Book Awards 2019 - HR and Management Category
Traditionally seen as a purely people function unconcerned with
numbers, HR is now uniquely placed to use company data to drive
performance, both of the people in the organization and the
organization as a whole. Data-Driven HR is a practical guide which
enables HR professionals to leverage the value of the vast amount
of data available at their fingertips. Covering how to identify the
most useful sources of data, collect information in a transparent
way that is in line with data protection requirements and turn this
data into tangible insights, this book marks a turning point for
the HR profession. Covering all the key elements of HR including
recruitment, employee engagement, performance management, wellbeing
and training, Data-Driven HR examines the ways data can contribute
to organizational success by, among other things, optimizing
processes, driving performance and improving HR decision making.
Packed with case studies and real-life examples, this is essential
reading for all HR professionals looking to make a measurable
difference in their organizations.
People have described nature since the beginning of human history.
They do it for various purposes, including to communicate about
economic, social, governmental, meteorological,
sustainability-related, strategic, military, and survival issues as
well as artistic expression. As a part of the whole world of living
beings, we use various types of senses, known and unknown, labeled
and not identified, to both communicate and create. Describing
Nature Through Visual Data is a collection of impactful research
that discusses issues related to the visualization of scientific
concepts, picturing processes, and products, as well as the role of
computing in advancing visual literacy skills. Organized into four
sections, the book contains descriptions, theories, and examples of
visual and music-based solutions concerning the selected natural or
technological events that are shaping present-day reality. The
chapters pertain to selected scientific fields, digital art,
computer graphics, and new media and confer the possible ways that
visuals, visualization, simulation, and interactive knowledge
presentation can help us to understand and share the content of
scientific thought, research, artistic works, and practice.
Featuring coverage on topics that include mathematical thinking,
music theory, and visual communication, this reference is ideal for
instructors, professionals, researchers, and students keen on
comprehending and enhancing the role of knowledge visualization in
computing, sciences, design, media communication, film,
advertising, and marketing.
Oberlegungen iiber die Automatisierung der Verwaltungstatigkeit in
indu striellen Unternehmungen lie en vermuten, daB die
verschiedenartigen ein zelnen Arbeiten auf eine gleichartige
Grundfunktion zuriickgefiihrt werden konnen. Zu dieser
Fragestellung gab Herr Professor Dr. Dr. Beste dankens werterweise
die Anregung, die Untersuchung in der vorliegenden allgemei nen
Fassung durchzufiihren. Der Verfasser hat sich bemiiht, in
mehrjahriger praktischer Tatigkeit die dargestellten theoretischen
Erkenntnisse aus den in der industriellen Praxis vorgefundenen
Gegebenheiten heraus zu entwickeln. Bei der Durchsprache einzelner
Probleme erhielt der Verfasser dariiber hin aus von Herrn Professor
Dr. Dr. Beste und Herrn Professor Dr. von Kortz fleisch wertvolle
Anregungen, fUr die er auch an dieser Stelle seinen beson deren
Dank aussprechen mochte. Die Arbeit wurde im Rahmen des
Industrieseminars der Universitat Koln angefertigt. Essen, den 1.
November 1962 INHALT Seite 5 Vorwort I. Begriffe und Bereich einer
betriebswirtschaftlichen Untersuchung uber die Information in der
industriellen Unternehmung . . . . . . . 9 A. Unterschiedliche
Produktivit1it bei der Materialverarbeitung und der
Informationsverarbeitung . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . 10 B. Zum Wesen der Information als
Tatigkeitsgegenstand in der industriellen Unternehmung und zur
Kommunikation . . . . . . . 12 C. Bereich und Ziele der
Untersuchung . . . . . . . . . . . . . . . . . . . . . . . . 14 II.
Die Grundbausteine der Information und ihrer Verarbeitung . . . 17
A. Die elementare Struktur der Information . . . . . . . . . . . .
. . . . . . . . 17 1. Der formale Gehalt der Information . . . . .
. . . . . . . . . . . . . . . . . . 18 2. Der informative Gehalt
cler Information . . . . . . . . . . . . . . . . . . . . 23 3. Die
betriebswirtschaftliche MaBeinheit der Information . . . . . . . 26
B. Die elementaren Kommunikationswege . . . . . . . . . . . . . . .
. . . . . . . 28 1. Die vertikale und die horizontale Anordnung der
Kommunikationswege . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 28 2. Das geschlossene Kommunikationssystem
als grundsatzliche or- nisatorische Struktur . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 32 C. Die
elementaren Verarbeitungsvorgange . . . . . . . . . . . . . . . . .
. . . ."
Gain expert guidance on how to successfully develop machine
learning models in Python and build your own unique data platforms
Key Features Gain a full understanding of the model production and
deployment process Build your first machine learning model in just
five minutes and get a hands-on machine learning experience
Understand how to deal with common challenges in data science
projects Book DescriptionWhere there's data, there's insight. With
so much data being generated, there is immense scope to extract
meaningful information that'll boost business productivity and
profitability. By learning to convert raw data into game-changing
insights, you'll open new career paths and opportunities. The Data
Science Workshop begins by introducing different types of projects
and showing you how to incorporate machine learning algorithms in
them. You'll learn to select a relevant metric and even assess the
performance of your model. To tune the hyperparameters of an
algorithm and improve its accuracy, you'll get hands-on with
approaches such as grid search and random search. Next, you'll
learn dimensionality reduction techniques to easily handle many
variables at once, before exploring how to use model ensembling
techniques and create new features to enhance model performance. In
a bid to help you automatically create new features that improve
your model, the book demonstrates how to use the automated feature
engineering tool. You'll also understand how to use the
orchestration and scheduling workflow to deploy machine learning
models in batch. By the end of this book, you'll have the skills to
start working on data science projects confidently. By the end of
this book, you'll have the skills to start working on data science
projects confidently. What you will learn Explore the key
differences between supervised learning and unsupervised learning
Manipulate and analyze data using scikit-learn and pandas libraries
Understand key concepts such as regression, classification, and
clustering Discover advanced techniques to improve the accuracy of
your model Understand how to speed up the process of adding new
features Simplify your machine learning workflow for production Who
this book is forThis is one of the most useful data science books
for aspiring data analysts, data scientists, database engineers,
and business analysts. It is aimed at those who want to kick-start
their careers in data science by quickly learning data science
techniques without going through all the mathematics behind machine
learning algorithms. Basic knowledge of the Python programming
language will help you easily grasp the concepts explained in this
book.
Build a solid foundation in algorithmic trading by developing,
testing and executing powerful trading strategies with real market
data using Python Key Features Build a strong foundation in
algorithmic trading by becoming well-versed with the basics of
financial markets Demystify jargon related to understanding and
placing multiple types of trading orders Devise trading strategies
and increase your odds of making a profit without human
intervention Book DescriptionIf you want to find out how you can
build a solid foundation in algorithmic trading using Python, this
cookbook is here to help. Starting by setting up the Python
environment for trading and connectivity with brokers, you'll then
learn the important aspects of financial markets. As you progress,
you'll learn to fetch financial instruments, query and calculate
various types of candles and historical data, and finally, compute
and plot technical indicators. Next, you'll learn how to place
various types of orders, such as regular, bracket, and cover
orders, and understand their state transitions. Later chapters will
cover backtesting, paper trading, and finally real trading for the
algorithmic strategies that you've created. You'll even understand
how to automate trading and find the right strategy for making
effective decisions that would otherwise be impossible for human
traders. By the end of this book, you'll be able to use Python
libraries to conduct key tasks in the algorithmic trading
ecosystem. Note: For demonstration, we're using Zerodha, an Indian
Stock Market broker. If you're not an Indian resident, you won't be
able to use Zerodha and therefore will not be able to test the
examples directly. However, you can take inspiration from the book
and apply the concepts across your preferred stock market broker of
choice. What you will learn Use Python to set up connectivity with
brokers Handle and manipulate time series data using Python Fetch a
list of exchanges, segments, financial instruments, and historical
data to interact with the real market Understand, fetch, and
calculate various types of candles and use them to compute and plot
diverse types of technical indicators Develop and improve the
performance of algorithmic trading strategies Perform backtesting
and paper trading on algorithmic trading strategies Implement real
trading in the live hours of stock markets Who this book is forIf
you are a financial analyst, financial trader, data analyst,
algorithmic trader, trading enthusiast or anyone who wants to learn
algorithmic trading with Python and important techniques to address
challenges faced in the finance domain, this book is for you. Basic
working knowledge of the Python programming language is expected.
Although fundamental knowledge of trade-related terminologies will
be helpful, it is not mandatory.
Quickly build and deploy machine learning models without managing
infrastructure, and improve productivity using Amazon SageMaker's
capabilities such as Amazon SageMaker Studio, Autopilot,
Experiments, Debugger, and Model Monitor Key Features Build, train,
and deploy machine learning models quickly using Amazon SageMaker
Analyze, detect, and receive alerts relating to various business
problems using machine learning algorithms and techniques Improve
productivity by training and fine-tuning machine learning models in
production Book DescriptionAmazon SageMaker enables you to quickly
build, train, and deploy machine learning (ML) models at scale,
without managing any infrastructure. It helps you focus on the ML
problem at hand and deploy high-quality models by removing the
heavy lifting typically involved in each step of the ML process.
This book is a comprehensive guide for data scientists and ML
developers who want to learn the ins and outs of Amazon SageMaker.
You'll understand how to use various modules of SageMaker as a
single toolset to solve the challenges faced in ML. As you
progress, you'll cover features such as AutoML, built-in algorithms
and frameworks, and the option for writing your own code and
algorithms to build ML models. Later, the book will show you how to
integrate Amazon SageMaker with popular deep learning libraries
such as TensorFlow and PyTorch to increase the capabilities of
existing models. You'll also learn to get the models to production
faster with minimum effort and at a lower cost. Finally, you'll
explore how to use Amazon SageMaker Debugger to analyze, detect,
and highlight problems to understand the current model state and
improve model accuracy. By the end of this Amazon book, you'll be
able to use Amazon SageMaker on the full spectrum of ML workflows,
from experimentation, training, and monitoring to scaling,
deployment, and automation. What you will learn Create and automate
end-to-end machine learning workflows on Amazon Web Services (AWS)
Become well-versed with data annotation and preparation techniques
Use AutoML features to build and train machine learning models with
AutoPilot Create models using built-in algorithms and frameworks
and your own code Train computer vision and NLP models using
real-world examples Cover training techniques for scaling, model
optimization, model debugging, and cost optimization Automate
deployment tasks in a variety of configurations using SDK and
several automation tools Who this book is forThis book is for
software engineers, machine learning developers, data scientists,
and AWS users who are new to using Amazon SageMaker and want to
build high-quality machine learning models without worrying about
infrastructure. Knowledge of AWS basics is required to grasp the
concepts covered in this book more effectively. Some understanding
of machine learning concepts and the Python programming language
will also be beneficial.
Discover how to describe your data in detail, identify data issues,
and find out how to solve them using commonly used techniques and
tips and tricks Key Features Get well-versed with various data
cleaning techniques to reveal key insights Manipulate data of
different complexities to shape them into the right form as per
your business needs Clean, monitor, and validate large data volumes
to diagnose problems before moving on to data analysis Book
DescriptionGetting clean data to reveal insights is essential, as
directly jumping into data analysis without proper data cleaning
may lead to incorrect results. This book shows you tools and
techniques that you can apply to clean and handle data with Python.
You'll begin by getting familiar with the shape of data by using
practices that can be deployed routinely with most data sources.
Then, the book teaches you how to manipulate data to get it into a
useful form. You'll also learn how to filter and summarize data to
gain insights and better understand what makes sense and what does
not, along with discovering how to operate on data to address the
issues you've identified. Moving on, you'll perform key tasks, such
as handling missing values, validating errors, removing duplicate
data, monitoring high volumes of data, and handling outliers and
invalid dates. Next, you'll cover recipes on using supervised
learning and Naive Bayes analysis to identify unexpected values and
classification errors, and generate visualizations for exploratory
data analysis (EDA) to visualize unexpected values. Finally, you'll
build functions and classes that you can reuse without modification
when you have new data. By the end of this Python book, you'll be
equipped with all the key skills that you need to clean data and
diagnose problems within it. What you will learn Find out how to
read and analyze data from a variety of sources Produce summaries
of the attributes of data frames, columns, and rows Filter data and
select columns of interest that satisfy given criteria Address
messy data issues, including working with dates and missing values
Improve your productivity in Python pandas by using method chaining
Use visualizations to gain additional insights and identify
potential data issues Enhance your ability to learn what is going
on in your data Build user-defined functions and classes to
automate data cleaning Who this book is forThis book is for anyone
looking for ways to handle messy, duplicate, and poor data using
different Python tools and techniques. The book takes a
recipe-based approach to help you to learn how to clean and manage
data. Working knowledge of Python programming is all you need to
get the most out of the book.
Die Absicht, ein Buch iiber Programmieren von Ziffernrechenanlagen
zu schreiben, entstand auf Grund einer Vorlesung gleichen Titels,
die ich seit nunmehr sieben Jahren an der Technischen Hochschule
Wien halte. Ich hatte dabei bemerkt, daB das Interesse fiir die
Programmierung von Ziffernrechnern immer weitere Kreise zieht und
daB es moglich ist, dieses Interesse aus einem einheitlichen
Gesichtswinkel zu befriedigen. Der Zugang zur Kenntnis des
Programmierens erfolgt heute iiblicher- weise mit Hille der
Mathematischen Verfahrenstechnik oder von seiten der
Administrativen Automation, oder schlieBlich iiber die mit tech-
nischen Einzelheiten vermengte Beschreibung spezieller Maschinen.
Ich bin nun der Meinung, daB man ein Buch iiber Programmieren
schreiben kann, ohne auf Einzelheiten der Mathematischen
Verfahrenstechnik und der Biiroautomation oder auf technische
Eigenschaften spezieller Ma- schinen eingehen zu miissen, und ohne
damit jewells einem Tell der Leser das Verstandnis zu erschweren.
Was nach Fortlassung der ge- nannten Gebiete bleibt, ist nicht ein
trockener, unverstandlicher Rest, sondern der Inbegriff aller fiir
das Programmieren wesentlichen Prin- zipien. Sowohl der
Naturwissenschaftler als auch der Verwaltungsfach- mann, der diese
Prinzipien erfaBt hat, wird jederzeit in der Lage sein, sie seinen
besonderen Aufgaben dienstbar zu machen. Kapitel A solI zeigen,
welchen Platz der Rechenautomat unter den technischen
Errungenschaften einnimmt und wie er dorthin gelangt ist.
Besonderes Anliegen ist mir hier der geschichtliche Uberblick, well
einer- seits die deutschsprachigen Biicher auf diesem Gebiet kaum
historische Angaben enthalten und andererseits die
anglo-amerikanische Literatur die kontinentaleuropaische
Entwicklung iibergeht. - Kapitel B enthalt die Beschreibung einer
gedachten Maschine TElCO in allen Einzelheiten.
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