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Books > Computing & IT > Applications of computing > Databases > Data capture & analysis
Big data consists of data sets that are too large and complex for
traditional data processing and data management applications.
Therefore, to obtain the valuable information within the data, one
must use a variety of innovative analytical methods, such as web
analytics, machine learning, and network analytics. As the study of
big data becomes more popular, there is an urgent demand for
studies on high-level computational intelligence and computing
services for analyzing this significant area of information
science. Big Data Analytics for Sustainable Computing is a
collection of innovative research that focuses on new computing and
system development issues in emerging sustainable applications.
Featuring coverage on a wide range of topics such as data
filtering, knowledge engineering, and cognitive analytics, this
publication is ideally designed for data scientists, IT
specialists, computer science practitioners, computer engineers,
academicians, professionals, and students seeking current research
on emerging analytical techniques and data processing software.
Use Microsoft SQL Server 2019 to implement, administer, and secure
a robust database solution that is disaster-proof and highly
available Key Features Explore new features of SQL Server 2019 to
set up, administer, and maintain your database solution
successfully Develop a dynamic SQL Server environment and
streamline big data pipelines Discover best practices for fixing
performance issues, database access management, replication, and
security Book DescriptionSQL Server is one of the most popular
relational database management systems developed by Microsoft. This
second edition of the SQL Server Administrator's Guide will not
only teach you how to administer an enterprise database, but also
help you become proficient at managing and keeping the database
available, secure, and stable. You'll start by learning how to set
up your SQL Server and configure new and existing environments for
optimal use. The book then takes you through designing aspects and
delves into performance tuning by showing you how to use indexes
effectively. You'll understand certain choices that need to be made
about backups, implement security policy, and discover how to keep
your environment healthy. Tools available for monitoring and
managing a SQL Server database, including automating health
reviews, performance checks, and much more, will also be discussed
in detail. As you advance, the book covers essential topics such as
migration, upgrading, and consolidation, along with the techniques
that will help you when things go wrong. Once you've got to grips
with integration with Azure and streamlining big data pipelines,
you'll learn best practices from industry experts for maintaining a
highly reliable database solution. Whether you are an administrator
or are looking to get started with database administration, this
SQL Server book will help you develop the skills you need to
successfully create, design, and deploy database solutions. What
you will learn Discover SQL Server 2019's new features and how to
implement them Fix performance issues by optimizing queries and
making use of indexes Design and use an optimal database management
strategy Combine SQL Server 2019 with Azure and manage your
solution using various automation techniques Implement efficient
backup and recovery techniques in line with security policies Get
to grips with migrating, upgrading, and consolidating with SQL
Server Set up an AlwaysOn-enabled stable and fast SQL Server 2019
environment Understand how to work with Big Data on SQL Server
environments Who this book is forThis book is for database
administrators, database developers, and anyone who wants to
administer large and multiple databases single-handedly using
Microsoft's SQL Server 2019. Basic awareness of database concepts
and experience with previous SQL Server versions is required.
Solve real-world business problems by learning how to create common
industry key performance indicators and other calculations using
DAX within Microsoft products such as Power BI, SQL Server, and
Excel. Key Features Learn to write sophisticated DAX queries to
solve business intelligence and data analytics challenges Handle
performance issues and optimization within the data model, DAX
calculations and more Solve business issues with Microsoft Excel,
Power BI, and SQL Server using DAX queries Book DescriptionDAX
provides an extra edge by extracting key information from the data
that is already present in your model. Filled with examples of
practical, real-world calculations geared toward business metrics
and key performance indicators, this cookbook features solutions
that you can apply for your own business analysis needs. You'll
learn to write various DAX expressions and functions to understand
how DAX queries work. The book also covers sections on dates, time,
and duration to help you deal with working days, time zones, and
shifts. You'll then discover how to manipulate text and numbers to
create dynamic titles and ranks, and deal with measure totals.
Later, you'll explore common business metrics for finance,
customers, employees, and projects. The book will also show you how
to implement common industry metrics such as days of supply, mean
time between failure, order cycle time and overall equipment
effectiveness. In the concluding chapters, you'll learn to apply
statistical formulas for covariance, kurtosis, and skewness.
Finally, you'll explore advanced DAX patterns for interpolation,
inverse aggregators, inverse slicers, and even forecasting with a
deseasonalized correlation coefficient. By the end of this book,
you'll have the skills you need to use DAX's functionality and
flexibility in business intelligence and data analytics. What you
will learn Understand how to create common calculations for dates,
time, and duration Create key performance indicators (KPIs) and
other business calculations Develop general DAX calculations that
deal with text and numbers Discover new ideas and time-saving
techniques for better calculations and models Perform advanced DAX
calculations for solving statistical measures and other
mathematical formulas Handle errors in DAX and learn how to debug
DAX calculations Understand how to optimize your data models Who
this book is forBusiness users, BI developers, data analysts, and
SQL users who are looking for solutions to the challenges faced
while solving analytical operations using DAX techniques and
patterns will find this book useful. Basic knowledge of the DAX
language and Microsoft services is mandatory.
Tragwerke, die besonderen Belastungen oder au13ergewohnlichen
Kraft- einwirkungen ausgesetzt sind, durch die Kiihnheit ihrer
Konstruktion bzw. eine nicht alltagliche Zweckbestimmung sich
auszeichnen, endlich solche, die aus neuartigem, noch nicht
geniigend durchforschtem Material bestehen, werden unmittelbar vor
Dbergabe an den Betrieb einer amtlichen Belastungsprobe unterzogen,
die in bestimmten Zeitabschnitten wiederholt wird (periodische
Erprobung). In Verbindung mit den Ergebnissen einer plangema13en
Dberpriifung stehen uns damit ausreichende Anhalte zur Abgabe eines
zutreffenden Urteiles iiber die Giite des in Frage stehenden
Bauwerkes zur Verfiigung. Die periodischen Deformationsmessungen an
bereits im Betriebe stehenden Tragwerken geben wieder Aufschlu13
iiber zwischenzeitlich etwa entstandene, optisch nur schwer
wahmehmbare Mangel an lebenswichtigen Traggliedern, mit deren
Bestehen immer dann zu rechnen ist, wenn im Vergleiche mit den
Ergebnissen voran- gegangener Proben unzulassig gro13e
Deformationen auftreten. Die Wichtigkeit und Notwendigkeit solcher
diagnostisch unentbehrlichen Messungen wurde schon friih erkannt.
Fehldiagnosen sind allerdings nicht ausgeschlossen, besonders wenn
die verschiedenen Faktoren, die das Me13ergebnis ma13geblich
beeinflussen, nicht richtig gedeutet und gegeneinander abgeschatzt
werden. Hier setzt die ebenso schwierige wie verantwortungsvolle
Tatigkeit des mit der Priifung und Erprobung betrauten Ingenieurs
ein, der daher nicht nur iiber griindliches Wissen und reiche
Erfahrung, sondern dariiber hinaus auch liber gewisse technisch-
diagnostische F iihigkeiten verfligen mli13te. Andernfalls besteht,
wie die Erfahrung lehrt, die nicht llnbegriindete Befiirchtung, daB
z. B. Rekonstruktionen angeordnet werden, die, abgesehen von den
Kosten, zumindest abwegig, wenn nicht gar system widrig sein
konnen.
Recent research reveals that socioeconomic factors of the
neighborhoods where road users live and where pedestrian-vehicle
crashes occur are important in determining the severity of the
crashes, with the former having a greater influence. Hence, road
safety countermeasures, especially those focusing on the road
users, should be targeted at these high risk neighborhoods. Big
Data Analytics in Traffic and Transportation Engineering: Emerging
Research and Opportunities is an essential reference source that
discusses access to transportation and examines vehicle-pedestrian
crashes, specifically in relation to socioeconomic factors that
influence them, main predictors, factors that contribute to crash
severity, and the enhancement of pedestrian safety measures.
Featuring research on topics such as public transport,
accessibility, and spatial distribution, this book is ideally
designed for policymakers, transportation engineers, road safety
designers, transport planners and managers, professionals,
academicians, researchers, and public administrators.
Recently, there has been a rapid increase in interest regarding
social network analysis in the data mining community. Cognitive
radios are expected to play a major role in meeting this exploding
traffic demand on social networks due to their ability to sense the
environment, analyze outdoor parameters, and then make decisions
for dynamic time, frequency, space, resource allocation, and
management to improve the utilization of mining the social data.
Cognitive Social Mining Applications in Data Analytics and
Forensics is an essential reference source that reviews cognitive
radio concepts and examines their applications to social mining
using a machine learning approach so that an adaptive and
intelligent mining is achieved. Featuring research on topics such
as data mining, real-time ubiquitous social mining services, and
cognitive computing, this book is ideally designed for social
network analysts, researchers, academicians, and industry
professionals.
Understand data analysis concepts to make accurate decisions based
on data using Python programming and Jupyter Notebook Key Features
Find out how to use Python code to extract insights from data using
real-world examples Work with structured data and free text sources
to answer questions and add value using data Perform data analysis
from scratch with the help of clear explanations for cleaning,
transforming, and visualizing data Book DescriptionData literacy is
the ability to read, analyze, work with, and argue using data. Data
analysis is the process of cleaning and modeling your data to
discover useful information. This book combines these two concepts
by sharing proven techniques and hands-on examples so that you can
learn how to communicate effectively using data. After introducing
you to the basics of data analysis using Jupyter Notebook and
Python, the book will take you through the fundamentals of data.
Packed with practical examples, this guide will teach you how to
clean, wrangle, analyze, and visualize data to gain useful
insights, and you'll discover how to answer questions using data
with easy-to-follow steps. Later chapters teach you about
storytelling with data using charts, such as histograms and scatter
plots. As you advance, you'll understand how to work with
unstructured data using natural language processing (NLP)
techniques to perform sentiment analysis. All the knowledge you
gain will help you discover key patterns and trends in data using
real-world examples. In addition to this, you will learn how to
handle data of varying complexity to perform efficient data
analysis using modern Python libraries. By the end of this book,
you'll have gained the practical skills you need to analyze data
with confidence. What you will learn Understand the importance of
data literacy and how to communicate effectively using data Find
out how to use Python packages such as NumPy, pandas, Matplotlib,
and the Natural Language Toolkit (NLTK) for data analysis Wrangle
data and create DataFrames using pandas Produce charts and data
visualizations using time-series datasets Discover relationships
and how to join data together using SQL Use NLP techniques to work
with unstructured data to create sentiment analysis models Discover
patterns in real-world datasets that provide accurate insights Who
this book is forThis book is for aspiring data analysts and data
scientists looking for hands-on tutorials and real-world examples
to understand data analysis concepts using SQL, Python, and Jupyter
Notebook. Anyone looking to evolve their skills to become
data-driven personally and professionally will also find this book
useful. No prior knowledge of data analysis or programming is
required to get started with this book.
A practical blockchain handbook designed to take you through
implementing and re-engineering banking and financial solutions and
workflows using eight step-by-step projects Key Features Implement
various end-to-end blockchain projects and learn to enhance
present-day financial solutions Use Ethereum, Hyperledger, and
Stellar to build public and private decentralized applications
Address complex challenges faced in the BFSI domain using different
blockchain platform services Book DescriptionBlockchain technology
will continue to play an integral role in the banking and finance
sector in the coming years. It will enable enterprises to build
transparent and secure business processes. Experts estimate annual
savings of up to 20 billion dollars from this technology. This book
will help you build financial apps using blockchain, guiding you
through enhancing popular products and services in the banking and
finance sector. The book starts by explaining the essential
concepts of blockchain, and the impact of blockchain technology on
the BFSI sector. Next, you'll delve into re-designing existing
banking processes and building new financial apps using blockchain.
To accomplish this, you'll work through eight blockchain projects.
By demonstrating the entire process, the book helps you understand
everything from setting up the environment and building frontend
portals to system integration and testing apps. You will gain
hands-on experience with the Ethereum, Hyperledger Fabric, and
Stellar to develop private and public decentralized apps. Finally,
you'll learn how to use ancillary platforms and frameworks such as
IPFS, Truffle OpenZeppelin, and MetaMask. By the end of this
blockchain book, you'll have an in-depth understanding of how to
leverage distributed ledgers and smart contracts for financial use
cases. What you will learn Design and implement blockchain
solutions in a BFSI organization Explore common architectures and
implementation models for enterprise blockchain Design blockchain
wallets for multi-purpose applications using Ethereum Build secure
and fast decentralized trading ecosystems with Blockchain Implement
smart contracts to build secure process workflows in Ethereum and
Hyperledger Fabric Use the Stellar platform to build KYC and
AML-compliant remittance workflows Map complex business workflows
and automate backend processes in a blockchain architecture Who
this book is forThis book is for blockchain and Dapps developers,
or anyone looking for a guide to building innovative and highly
secure solutions in the fintech domain using real-world use cases.
Developers working in financial enterprises and banks, and solution
architects looking to build brand new process flows using
blockchain technology will also find the book useful. Experience
with Solidity programming and prior knowledge of finance and trade
are required to get the most out of this book.
Implement business intelligence (BI), data modeling, and data
analytics within Microsoft products such as Power BI, SQL Server,
and Excel Key Features Understand the ins and outs of DAX
expressions and querying functions with the help of easy-to-follow
examples Manipulate data of varying complexity and optimize BI
workflows to extract key insights Create, monitor, and improve the
performance of models by writing clean and robust DAX queries Book
DescriptionData Analysis Expressions (DAX) is known for its ability
to increase efficiency by extracting new information from data that
is already present in your model. With this book, you'll learn to
use DAX's functionality and flexibility in the BI and data
analytics domains. You'll start by learning the basics of DAX,
along with understanding the importance of good data models, and
how to write efficient DAX formulas by using variables and
formatting styles. You'll then explore how DAX queries work with
the help of examples. The book will guide you through optimizing
the BI workflow by writing powerful DAX queries. Next, you'll learn
to manipulate and load data of varying complexity within Microsoft
products such as Power BI, SQL Server, and Excel Power Pivot.
You'll then discover how to build and extend your data models to
gain additional insights, before covering progressive DAX syntax
and functions to understand complex relationships in DAX. Later,
you'll focus on important DAX functions, specifically those related
to tables, date and time, filtering, and statistics. Finally,
you'll delve into advanced topics such as how the formula and
storage engines work to optimize queries. By the end of this book,
you'll have gained hands-on experience in employing DAX to enhance
your data models by extracting new information and gaining deeper
insights. What you will learn Understand DAX, from the basics
through to advanced topics, and learn to build effective data
models Write and use DAX functions and expressions with the help of
hands-on examples Discover how to handle errors in your DAX code,
and avoid unwanted results Load data into a data model using Power
BI, Excel Power Pivot, and SSAS Tabular Cover DAX functions such as
date, time, and time intelligence using code examples Gain insights
into data by using DAX to create new information Understand the DAX
VertiPaq engine and how it can help you optimize data models Who
this book is forThis book is for data analysts, business analysts,
BI developers, or SQL users who want to make the best use of DAX in
the BI and data analytics domain with the help of examples. Some
understanding of BI concepts is mandatory to fully understand the
concepts covered in the book.
Discover techniques to summarize the characteristics of your data
using PyPlot, NumPy, SciPy, and pandas Key Features Understand the
fundamental concepts of exploratory data analysis using Python Find
missing values in your data and identify the correlation between
different variables Practice graphical exploratory analysis
techniques using Matplotlib and the Seaborn Python package Book
DescriptionExploratory Data Analysis (EDA) is an approach to data
analysis that involves the application of diverse techniques to
gain insights into a dataset. This book will help you gain
practical knowledge of the main pillars of EDA - data cleaning,
data preparation, data exploration, and data visualization. You'll
start by performing EDA using open source datasets and perform
simple to advanced analyses to turn data into meaningful insights.
You'll then learn various descriptive statistical techniques to
describe the basic characteristics of data and progress to
performing EDA on time-series data. As you advance, you'll learn
how to implement EDA techniques for model development and
evaluation and build predictive models to visualize results. Using
Python for data analysis, you'll work with real-world datasets,
understand data, summarize its characteristics, and visualize it
for business intelligence. By the end of this EDA book, you'll have
developed the skills required to carry out a preliminary
investigation on any dataset, yield insights into data, present
your results with visual aids, and build a model that correctly
predicts future outcomes. What you will learn Import, clean, and
explore data to perform preliminary analysis using powerful Python
packages Identify and transform erroneous data using different data
wrangling techniques Explore the use of multiple regression to
describe non-linear relationships Discover hypothesis testing and
explore techniques of time-series analysis Understand and interpret
results obtained from graphical analysis Build, train, and optimize
predictive models to estimate results Perform complex EDA
techniques on open source datasets Who this book is forThis EDA
book is for anyone interested in data analysis, especially
students, statisticians, data analysts, and data scientists. The
practical concepts presented in this book can be applied in various
disciplines to enhance decision-making processes with data analysis
and synthesis. Fundamental knowledge of Python programming and
statistical concepts is all you need to get started with this book.
Get a firm grip on the core processes including browser automation,
web scraping, Word, Excel, and GUI automation with Python 3.8 and
higher Key Features Automate integral business processes such as
report generation, email marketing, and lead generation Explore
automated code testing and Python's growth in data science and AI
automation in three new chapters Understand techniques to extract
information and generate appealing graphs, and reports with
Matplotlib Book DescriptionIn this updated and extended version of
Python Automation Cookbook, each chapter now comprises the newest
recipes and is revised to align with Python 3.8 and higher. The
book includes three new chapters that focus on using Python for
test automation, machine learning projects, and for working with
messy data. This edition will enable you to develop a sharp
understanding of the fundamentals required to automate business
processes through real-world tasks, such as developing your first
web scraping application, analyzing information to generate
spreadsheet reports with graphs, and communicating with
automatically generated emails. Once you grasp the basics, you will
acquire the practical knowledge to create stunning graphs and
charts using Matplotlib, generate rich graphics with relevant
information, automate marketing campaigns, build machine learning
projects, and execute debugging techniques. By the end of this
book, you will be proficient in identifying monotonous tasks and
resolving process inefficiencies to produce superior and reliable
systems. What you will learn Learn data wrangling with Python and
Pandas for your data science and AI projects Automate tasks such as
text classification, email filtering, and web scraping with Python
Use Matplotlib to generate a variety of stunning graphs, charts,
and maps Automate a range of report generation tasks, from sending
SMS and email campaigns to creating templates, adding images in
Word, and even encrypting PDFs Master web scraping and web crawling
of popular file formats and directories with tools like Beautiful
Soup Build cool projects such as a Telegram bot for your marketing
campaign, a reader from a news RSS feed, and a machine learning
model to classify emails to the correct department based on their
content Create fire-and-forget automation tasks by writing cron
jobs, log files, and regexes with Python scripting Who this book is
forPython Automation Cookbook - Second Edition is for developers,
data enthusiasts or anyone who wants to automate monotonous manual
tasks related to business processes such as finance, sales, and HR,
among others. Working knowledge of Python is all you need to get
started with this book.
Learn how to analyze data using Python models with the help of
real-world use cases and guidance from industry experts Key
Features Get to grips with data analysis by studying use cases from
different fields Develop your critical thinking skills by following
tried-and-true data analysis Learn how to use conclusions from data
analyses to make better business decisions Book
DescriptionBusinesses today operate online and generate data almost
continuously. While not all data in its raw form may seem useful,
if processed and analyzed correctly, it can provide you with
valuable hidden insights. The Data Analysis Workshop will help you
learn how to discover these hidden patterns in your data, to
analyze them, and leverage the results to help transform your
business. The book begins by taking you through the use case of a
bike rental shop. You'll be shown how to correlate data, plot
histograms, and analyze temporal features. As you progress, you'll
learn how to plot data for a hydraulic system using the Seaborn and
Matplotlib libraries, and explore a variety of use cases that show
you how to join and merge databases, prepare data for analysis, and
handle imbalanced data. By the end of the book, you'll have learned
different data analysis techniques, including hypothesis testing,
correlation, and null-value imputation, and will have become a
confident data analyst. What you will learn Get to grips with the
fundamental concepts and conventions of data analysis Understand
how different algorithms help you to analyze the data effectively
Determine the variation between groups of data using hypothesis
testing Visualize your data correctly using appropriate plotting
points Use correlation techniques to uncover the relationship
between variables Find hidden patterns in data using advanced
techniques and strategies Who this book is forThe Data Analysis
Workshop is for programmers who already know how to code in Python
and want to use it to perform data analysis. If you are looking to
gain practical experience in data science with Python, this book is
for you.
Leverage machine learning to design and back-test automated trading
strategies for real-world markets using pandas, TA-Lib,
scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline,
backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle
book includes a free eBook in the PDF format. Key Features Design,
train, and evaluate machine learning algorithms that underpin
automated trading strategies Create a research and strategy
development process to apply predictive modeling to trading
decisions Leverage NLP and deep learning to extract tradeable
signals from market and alternative data Book DescriptionThe
explosive growth of digital data has boosted the demand for
expertise in trading strategies that use machine learning (ML).
This revised and expanded second edition enables you to build and
evaluate sophisticated supervised, unsupervised, and reinforcement
learning models. This book introduces end-to-end machine learning
for the trading workflow, from the idea and feature engineering to
model optimization, strategy design, and backtesting. It
illustrates this by using examples ranging from linear models and
tree-based ensembles to deep-learning techniques from cutting edge
research. This edition shows how to work with market, fundamental,
and alternative data, such as tick data, minute and daily bars, SEC
filings, earnings call transcripts, financial news, or satellite
images to generate tradeable signals. It illustrates how to
engineer financial features or alpha factors that enable an ML
model to predict returns from price data for US and international
stocks and ETFs. It also shows how to assess the signal content of
new features using Alphalens and SHAP values and includes a new
appendix with over one hundred alpha factor examples. By the end,
you will be proficient in translating ML model predictions into a
trading strategy that operates at daily or intraday horizons, and
in evaluating its performance. What you will learn Leverage market,
fundamental, and alternative text and image data Research and
evaluate alpha factors using statistics, Alphalens, and SHAP values
Implement machine learning techniques to solve investment and
trading problems Backtest and evaluate trading strategies based on
machine learning using Zipline and Backtrader Optimize portfolio
risk and performance analysis using pandas, NumPy, and pyfolio
Create a pairs trading strategy based on cointegration for US
equities and ETFs Train a gradient boosting model to predict
intraday returns using AlgoSeek's high-quality trades and quotes
data Who this book is forIf you are a data analyst, data scientist,
Python developer, investment analyst, or portfolio manager
interested in getting hands-on machine learning knowledge for
trading, this book is for you. This book is for you if you want to
learn how to extract value from a diverse set of data sources using
machine learning to design your own systematic trading strategies.
Some understanding of Python and machine learning techniques is
required.
Learn the fundamentals of clean, effective Python coding and build
the practical skills to tackle your own software development or
data science projects Key Features Build key Python skills with
engaging development tasks and challenging activities Implement
useful algorithms and write programs to solve real-world problems
Apply Python in realistic data science projects and create simple
machine learning models Book DescriptionHave you always wanted to
learn Python, but never quite known how to start? More applications
than we realize are being developed using Python because it is easy
to learn, read, and write. You can now start learning the language
quickly and effectively with the help of this interactive tutorial.
The Python Workshop starts by showing you how to correctly apply
Python syntax to write simple programs, and how to use appropriate
Python structures to store and retrieve data. You'll see how to
handle files, deal with errors, and use classes and methods to
write concise, reusable, and efficient code. As you advance, you'll
understand how to use the standard library, debug code to
troubleshoot problems, and write unit tests to validate application
behavior. You'll gain insights into using the pandas and NumPy
libraries for analyzing data, and the graphical libraries of
Matplotlib and Seaborn to create impactful data visualizations. By
focusing on entry-level data science, you'll build your practical
Python skills in a way that mirrors real-world development.
Finally, you'll discover the key steps in building and using simple
machine learning algorithms. By the end of this Python book, you'll
have the knowledge, skills and confidence to creatively tackle your
own ambitious projects with Python. What you will learn Write clean
and well-commented code that is easy to maintain Automate essential
day-to-day tasks with Python scripts Debug logical errors and
handle exceptions in your programs Explore data science
fundamentals and create engaging visualizations Get started with
predictive machine learning Keep your development process bug-free
with automated testing Who this book is forThis book is designed
for anyone who is new to the Python programming language. Whether
you're an aspiring software engineer or data scientist, or are just
curious about learning how to code with Python, this book is for
you. No prior programming experience is required.
Understand data science concepts and methodologies to manage and
deliver top-notch solutions for your organization Key Features
Learn the basics of data science and explore its possibilities and
limitations Manage data science projects and assemble teams
effectively even in the most challenging situations Understand
management principles and approaches for data science projects to
streamline the innovation process Book DescriptionData science and
machine learning can transform any organization and unlock new
opportunities. However, employing the right management strategies
is crucial to guide the solution from prototype to production.
Traditional approaches often fail as they don't entirely meet the
conditions and requirements necessary for current data science
projects. In this book, you'll explore the right approach to data
science project management, along with useful tips and best
practices to guide you along the way. After understanding the
practical applications of data science and artificial intelligence,
you'll see how to incorporate them into your solutions. Next, you
will go through the data science project life cycle, explore the
common pitfalls encountered at each step, and learn how to avoid
them. Any data science project requires a skilled team, and this
book will offer the right advice for hiring and growing a data
science team for your organization. Later, you'll be shown how to
efficiently manage and improve your data science projects through
the use of DevOps and ModelOps. By the end of this book, you will
be well versed with various data science solutions and have gained
practical insights into tackling the different challenges that
you'll encounter on a daily basis. What you will learn Understand
the underlying problems of building a strong data science pipeline
Explore the different tools for building and deploying data science
solutions Hire, grow, and sustain a data science team Manage data
science projects through all stages, from prototype to production
Learn how to use ModelOps to improve your data science pipelines
Get up to speed with the model testing techniques used in both
development and production stages Who this book is forThis book is
for data scientists, analysts, and program managers who want to use
data science for business productivity by incorporating data
science workflows efficiently. Some understanding of basic data
science concepts will be useful to get the most out of this book.
Understand the fundamentals of algorithmic trading to apply
algorithms to real market data and analyze the results of
real-world trading strategies Key Features Understand the power of
algorithmic trading in financial markets with real-world examples
Get up and running with the algorithms used to carry out
algorithmic trading Learn to build your own algorithmic trading
robots which require no human intervention Book DescriptionIt's now
harder than ever to get a significant edge over competitors in
terms of speed and efficiency when it comes to algorithmic trading.
Relying on sophisticated trading signals, predictive models and
strategies can make all the difference. This book will guide you
through these aspects, giving you insights into how modern
electronic trading markets and participants operate. You'll start
with an introduction to algorithmic trading, along with setting up
the environment required to perform the tasks in the book. You'll
explore the key components of an algorithmic trading business and
aspects you'll need to take into account before starting an
automated trading project. Next, you'll focus on designing,
building and operating the components required for developing a
practical and profitable algorithmic trading business. Later,
you'll learn how quantitative trading signals and strategies are
developed, and also implement and analyze sophisticated trading
strategies such as volatility strategies, economic release
strategies, and statistical arbitrage. Finally, you'll create a
trading bot from scratch using the algorithms built in the previous
sections. By the end of this book, you'll be well-versed with
electronic trading markets and have learned to implement, evaluate
and safely operate algorithmic trading strategies in live markets.
What you will learn Understand the components of modern algorithmic
trading systems and strategies Apply machine learning in
algorithmic trading signals and strategies using Python Build,
visualize and analyze trading strategies based on mean reversion,
trend, economic releases and more Quantify and build a risk
management system for Python trading strategies Build a backtester
to run simulated trading strategies for improving the performance
of your trading bot Deploy and incorporate trading strategies in
the live market to maintain and improve profitability Who this book
is forThis book is for software engineers, financial traders, data
analysts, and entrepreneurs. Anyone who wants to get started with
algorithmic trading and understand how it works; and learn the
components of a trading system, protocols and algorithms required
for black box and gray box trading, and techniques for building a
completely automated and profitable trading business will also find
this book useful.
Document the architecture of your software easily with this highly
practical, open-source template. Key Features Get to grips with
leveraging the features of arc42 to create insightful documents
Learn the concepts of software architecture documentation through
real-world examples Discover techniques to create compact, helpful,
and easy-to-read documentation Book DescriptionWhen developers
document the architecture of their systems, they often invent their
own specific ways of articulating structures, designs, concepts,
and decisions. What they need is a template that enables simple and
efficient software architecture documentation. arc42 by Example
shows how it's done through several real-world examples. Each
example in the book, whether it is a chess engine, a huge CRM
system, or a cool web system, starts with a brief description of
the problem domain and the quality requirements. Then, you'll
discover the system context with all the external interfaces.
You'll dive into an overview of the solution strategy to implement
the building blocks and runtime scenarios. The later chapters also
explain various cross-cutting concerns and how they affect other
aspects of a program. What you will learn Utilize arc42 to document
a system's physical infrastructure Learn how to identify a system's
scope and boundaries Break a system down into building blocks and
illustrate the relationships between them Discover how to describe
the runtime behavior of a system Know how to document design
decisions and their reasons Explore the risks and technical debt of
your system Who this book is forThis book is for software
developers and solutions architects who are looking for an easy,
open-source tool to document their systems. It is a useful
reference for those who are already using arc42. If you are new to
arc42, this book is a great learning resource. For those of you who
want to write better technical documentation will benefit from the
general concepts covered in this book.
Leverage the full potential of SAS to get unique, actionable
insights from your data Key Features Build enterprise-class data
solutions using SAS and become well-versed in SAS programming Work
with different data structures, and run SQL queries to manipulate
your data Explore essential concepts and techniques with practical
examples to confidently pass the SAS certification exam Book
DescriptionSAS is one of the leading enterprise tools in the world
today when it comes to data management and analysis. It enables the
fast and easy processing of data and helps you gain valuable
business insights for effective decision-making. This book will
serve as a comprehensive guide that will prepare you for the SAS
certification exam. After a quick overview of the SAS architecture
and components, the book will take you through the different
approaches to importing and reading data from different sources
using SAS. You will then cover SAS Base and 4GL, understanding data
management and analysis, along with exploring SAS functions for
data manipulation and transformation. Next, you'll discover SQL
procedures and get up to speed on creating and validating queries.
In the concluding chapters, you'll learn all about data
visualization, right from creating bar charts and sample geographic
maps through to assigning patterns and formats. In addition to
this, the book will focus on macro programming and its advanced
aspects. By the end of this book, you will be well versed in SAS
programming and have the skills you need to easily handle and
manage your data-related problems in SAS. What you will learn
Explore a variety of SAS modules and packages for efficient data
analysis Use SAS 4GL functions to manipulate, merge, sort, and
transform data Gain useful insights into advanced PROC SQL options
in SAS to interact with data Get to grips with SAS Macro and define
your own macros to share data Discover the different graphical
libraries to shape and visualize data with Apply the SAS Output
Delivery System to prepare detailed reports Who this book is
forBudding or experienced data professionals who want to get
started with SAS will benefit from this book. Those looking to
prepare for the SAS certification exam will also find this book to
be a useful resource. Some understanding of basic data management
concepts will help you get the most out of this book.
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