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Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
Dieses Buch ist als Einfuhrung in die Statistik gedacht. Die
dargelegten Methoden und Gedankengange sind aus den Statistik-
vorlesungen fur Volks- und Betriebswirtschafter hervorgegangen, die
ich seit rund einem Jahrzehnt an der Universitat in
Freiburg/Schweiz gehalten habe. Das Buch richtet sich deshalb vor
allem an Volks- und Betriebs- wirtschafter. Mit Rucksicht auf
diesen Leserkreis wurden die notwendigen mathematischen Ableitungen
moeglichst luckenlos durchgefuhrt, damit auch Leser, die in der
Mathematik weniger bewan ert sind, den Darlegungen folgen und
mathematisch anspruchsvollere Lehrbucher der Statistik mit mehr
Gewinn lesen koennen. Meine Erfahrung hat gezeigt, dass diese
Ableitungen fur das bessere Verstandnis der einzelnen Methoden und
deren Grenzen unbedingt erforderlich sind. Der mathematisch
geschulte Leser moege deshalb diese mathematische Weitschweifigkeit
verzeihen. Das vorliegende Buch durfte deshalb zwischen den
elementaren Statistik- buchern und den sehr anspruchsvollen
Lehrbuchern der mathematischen Statistik seinen Platz haben. Im
Anschluss an dieses Buch sind weitere Darstellungen -uber die
Stichprobentheorie, die Versuchsplanung, die statistischen
Testverfahren und die Zeitreihenanalyse geplant. Des weiteren wird
auch das Verhaltnis zwischen Statistik, Operations Research,
OEkonoemetrie und Datenverarbei- tung behandelt werden. Dem Verlag
sei an dieser Stelle fur sein Verstandnis und seine her- vorragende
Arbeit gedankt. Sollte diesem Buch Erfolg beschieden sein, so ist
er weitgehend auf die sorgfaltige Arbeit des Verlags zuruck-
zufuhren. Freiburg/Schweiz, Februar 1970 Ernst P. Billeter
Inhaltsverzeichnis 1. Geschichte, Wesen und Begriff der Statistik
1. 1. Geschichte der Statistik . 1 1. 2. Wesen der Statistik . 5 7
1. 3. Begriff der Statistik 2. Grundlagen der Statistik 2. 1.
Wahrscheinlichkeitsrechnung . . . . . . .
Discover how to build and backtest algorithmic trading strategies
with Zipline Key Features Get to grips with market data and stock
analysis and visualize data to gain quality insights Find out how
to systematically approach quantitative research and strategy
generation/backtesting in algorithmic trading Learn how to navigate
the different features in Python's data analysis libraries Book
DescriptionAlgorithmic trading helps you stay ahead of the markets
by devising strategies in quantitative analysis to gain profits and
cut losses. The book starts by introducing you to algorithmic
trading and explaining why Python is the best platform for
developing trading strategies. You'll then cover quantitative
analysis using Python, and learn how to build algorithmic trading
strategies with Zipline using various market data sources. Using
Zipline as the backtesting library allows access to complimentary
US historical daily market data until 2018. As you advance, you
will gain an in-depth understanding of Python libraries such as
NumPy and pandas for analyzing financial datasets, and explore
Matplotlib, statsmodels, and scikit-learn libraries for advanced
analytics. You'll also focus on time series forecasting, covering
pmdarima and Facebook Prophet. By the end of this trading book, you
will be able to build predictive trading signals, adopt basic and
advanced algorithmic trading strategies, and perform portfolio
optimization. What you will learn Discover how quantitative
analysis works by covering financial statistics and ARIMA Use core
Python libraries to perform quantitative research and strategy
development using real datasets Understand how to access financial
and economic data in Python Implement effective data visualization
with Matplotlib Apply scientific computing and data visualization
with popular Python libraries Build and deploy backtesting
algorithmic trading strategies Who this book is forThis book is for
data analysts and financial traders who want to explore how to
design algorithmic trading strategies using Python's core
libraries. If you are looking for a practical guide to backtesting
algorithmic trading strategies and building your own strategies,
then this book is for you. Beginner-level working knowledge of
Python programming and statistics will be helpful.
Build a strong foundation in SAS data warehousing by understanding
data transformation code and policy, data stewardship and
management, interconnectivity between SAS and other warehousing
products, and print and web reporting Key Features Understand how
to use SAS macros for standardizing extract, transform, and load
(ETL) protocols Develop and use data curation files for effective
warehouse management Learn how to develop and manage ETL, policies,
and print and web reports that meet user needs Book DescriptionSAS
is used for various functions in the development and maintenance of
data warehouses, thanks to its reputation of being able to handle
'big data'. This book will help you learn the pros and cons of
storing data in SAS. As you progress, you'll understand how to
document and design extract-transform-load (ETL) protocols for SAS
processes. Later, you'll focus on how the use of SAS arrays and
macros can help standardize ETL. The book will also help you
examine approaches for serving up data using SAS and explore how
connecting SAS to other systems can enhance the data warehouse
user's experience. By the end of this data management book, you
will have a fundamental understanding of the roles SAS can play in
a warehouse environment, and be able to choose wisely when
designing your data warehousing processes involving SAS. What you
will learn Develop efficient ways to manage data input/output (I/O)
in SAS Create and manage extract, transform, and load (ETL) code in
SAS Standardize ETL through macro variables, macros, and arrays
Identify data warehouse users and ensure their needs are met Design
crosswalk and other variables to serve analyst needs Maintain data
curation files to improve communication and management Use the
output delivery system (ODS) for print and web reporting Connect
other products to SAS to optimize storage and reporting Who this
book is forThis book is for data architects, managers leading data
projects, and programmers or developers using SAS who want to
effectively maintain a data lake, data mart, or data warehouse.
The text is for instructors who want to use MATLAB to teach
introductory programming concepts. Since many students struggle
with applying the concepts that underlie good programming practice,
" Learning to Program with MATLAB: Building GUI Tools" was designed
upon the observation that student learning is enhanced if the
students themselves build the GUI (graphical user interface) tool,
construct the computational model, implement the visualization of
results, and design the GUI. This text teaches the core concepts of
computer programming--arrays, loops, functions, and basic data
structures--using MATLAB. The chapter sequence covers text-based
programs, then programs that produce graphics, building up to an
emphasis on GUI tools. This progression unleashes the real power of
MATLAB--creating visual expressions of the underlying mathematics
of a problem or design.
This engaging and clearly written textbook/reference provides a
must-have introduction to the rapidly emerging interdisciplinary
field of data science. It focuses on the principles fundamental to
becoming a good data scientist and the key skills needed to build
systems for collecting, analyzing, and interpreting data. The Data
Science Design Manual is a source of practical insights that
highlights what really matters in analyzing data, and provides an
intuitive understanding of how these core concepts can be used. The
book does not emphasize any particular programming language or
suite of data-analysis tools, focusing instead on high-level
discussion of important design principles. This easy-to-read text
ideally serves the needs of undergraduate and early graduate
students embarking on an "Introduction to Data Science" course. It
reveals how this discipline sits at the intersection of statistics,
computer science, and machine learning, with a distinct heft and
character of its own. Practitioners in these and related fields
will find this book perfect for self-study as well. Additional
learning tools: Contains "War Stories," offering perspectives on
how data science applies in the real world Includes "Homework
Problems," providing a wide range of exercises and projects for
self-study Provides a complete set of lecture slides and online
video lectures at www.data-manual.com Provides "Take-Home Lessons,"
emphasizing the big-picture concepts to learn from each chapter
Recommends exciting "Kaggle Challenges" from the online platform
Kaggle Highlights "False Starts," revealing the subtle reasons why
certain approaches fail Offers examples taken from the data science
television show "The Quant Shop" (www.quant-shop.com)
This accessible and classroom-tested textbook/reference presents an
introduction to the fundamentals of the emerging and
interdisciplinary field of data science. The coverage spans key
concepts adopted from statistics and machine learning, useful
techniques for graph analysis and parallel programming, and the
practical application of data science for such tasks as building
recommender systems or performing sentiment analysis. Topics and
features: provides numerous practical case studies using real-world
data throughout the book; supports understanding through hands-on
experience of solving data science problems using Python; describes
techniques and tools for statistical analysis, machine learning,
graph analysis, and parallel programming; reviews a range of
applications of data science, including recommender systems and
sentiment analysis of text data; provides supplementary code
resources and data at an associated website.
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