Bid data paper ( 1500 word minimum, APA format, no need for cover page, at least 10 references) - Science
Read the Kelleher Textbook: Chapters 1 and all the postings on Topic 1 - Epistemology I
Your topic is: Big Data
What is big data? Give the history.
How is big data used today in decision science?
Give big data examples.
What are the big data types?
What is big data technology.
Describe big data analytics.
How big is big data?
Integrate your faith, the textbook, your values, your future career plans, and your past employment history as applicable.
DATA SCIENCE
The MIT Press Essential Knowledge Series
Auctions, Timothy P. Hubbard and Harry J. Paarsch
The Book, Amaranth Borsuk
Cloud Computing, Nayan Ruparelia
Computing: A Concise History, Paul E. Ceruzzi
The Conscious Mind, Zoltan L. Torey
Crowdsourcing, Daren C. Brabham
Data Science, John D. Kelleher and Brendan Tierney
Free Will, Mark Balaguer
The Future, Nick Montfort
Information and Society, Michael Buckland
Information and the Modern Corporation, James W. Cortada
Intellectual Property Strategy, John Palfrey
The Internet of Things, Samuel Greengard
Machine Learning: The New AI, Ethem Alpaydin
Machine Translation, Thierry Poibeau
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MOOCs, Jonathan Haber
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Self-Tracking, Gina Neff and Dawn Nafus
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Understanding Beliefs, Nils J. Nilsson
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The MIT Press | Cambridge, Massachusetts | London, England
DATA SCIENCE
J O H N D . K E L L E H E R
A N D B R E N D A N T I E R N E Y
© 2018 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by
any electronic or mechanical means (including photocopying, recording, or
information storage and retrieval) without permission in writing from the
publisher.
This book was set in Chaparral Pro by Toppan Best-set Premedia Limited.
Printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Data
Names: Kelleher, John D., 1974- author. | Tierney, Brendan, 1970- author.
Title: Data science / John D. Kelleher and Brendan Tierney.
Description: Cambridge, MA : The MIT Press, [2018] | Series: The MIT Press
essential knowledge series | Includes bibliographical references and index.
Identifiers: LCCN 2017043665 | ISBN 9780262535434 (pbk. : alk. paper)
Subjects: LCSH: Big data. | Machine learning. | Data mining. | Quantitative
research.
Classification: LCC QA76.9.B45 K45 2018 | DDC 005.7--dc23 LC record
available at https://lccn.loc.gov/2017043665
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../../../../../[email protected]/2017043665
Series Foreword vii
Preface ix
Acknowledgments xiii
1 What Is Data Science? 1
2 What Are Data, and What Is a Data Set? 39
3 A Data Science Ecosystem 69
4 Machine Learning 101 97
5 Standard Data Science Tasks 151
6 Privacy and Ethics 181
7 Future Trends and Principles of Success 219
Glossary 239
Notes 247
Further Readings 251
References 253
Index 261
C O N T E N T S
S E R I E S FO R E W O R D
The MIT Press Essential Knowledge series offers acces-
sible, concise, beautifully produced pocket-size books on
topics of current interest. Written by leading thinkers, the
books in this series deliver expert overviews of subjects
that range from the cultural and the historical to the sci-
entific and the technical.
In today’s era of instant information gratification, we
have ready access to opinions, rationalizations, and super-
ficial descriptions. Much harder to come by is the founda-
tional knowledge that informs a principled understanding
of the world. Essential Knowledge books fill that need.
Synthesizing specialized subject matter for nonspecialists
and engaging critical topics through fundamentals, each
of these compact volumes offers readers a point of access
to complex ideas.
Bruce Tidor
Professor of Biological Engineering and Computer Science
Massachusetts Institute of Technology
P R E FA C E
The goal of data science is to improve decision making by
basing decisions on insights extracted from large data sets.
As a field of activity, data science encompasses a set of
principles, problem definitions, algorithms, and processes
for extracting nonobvious and useful patterns from large
data sets. It is closely related to the fields of data mining
and machine learning, but it is broader in scope. Today,
data science drives decision making in nearly all parts of
modern societies. Some of the ways that data science may
affect your daily life include determining which advertise-
ments are presented to you online; which movies, books,
and friend connections are recommended to you; which
emails are filtered into your spam folder; what offers you
receive when you renew your cell phone service; the cost of
your health insurance premium; the sequencing and tim-
ing of traffic lights in your area; how the drugs you may
need were designed; and which locations in your city the
police are targeting.
The growth in use of data science across our societies
is driven by the emergence of big data and social media,
the speedup in computing power, the massive reduction
in the cost of computer memory, and the development of
more powerful methods for data analysis and modeling,
such as deep learning. Together these factors mean that
x P r e fa c e
it has never been easier for organizations to gather, store,
and process data. At the same time, these technical inno-
vations and the broader application of data science means
that the ethical challenges related to the use of data and
individual privacy have never been more pressing. The aim
of this book is to provide an introduction to data science
that covers the essential elements of the field at a depth
that provides a principled understanding of the field.
Chapter 1 introduces the field of data science and pro-
vides a brief history of how it has developed and evolved.
It also examines why data science is important today and
some of the factors that are driving its adoption. The
chapter finishes by reviewing and debunking some of the
myths associated with data science. Chapter 2 introduces
fundamental concepts relating to data. It also describes
the standard stages in a data science project: business un-
derstanding, data understanding, data preparation, mod-
eling, evaluation, and deployment. Chapter 3 focuses on
data infrastructure and the challenges posed by big data
and the integration of data from multiple sources. One
aspect of a typical data infrastructure that can be chal-
lenging is that data in databases and data warehouses of-
ten reside on servers different from the servers used for
data analysis. As a consequence, when large data sets are
handled, a surprisingly large amount of time can be spent
moving data between the servers a database or data ware-
house are living on and the servers used for data analysis
P r e fa c e x i
and machine learning. Chapter 3 begins by describing a
typical data science infrastructure for an organization and
some of the emerging solutions to the challenge of mov-
ing large data sets within a data infrastructure, which in-
clude the use of in-database machine learning, the use of
Hadoop for data storage and processing, and the develop-
ment of hybrid database systems that seamlessly combine
traditional database software and Hadoop-like solutions.
The chapter concludes by highlighting some of the chal-
lenges in integrating data from across an organization into
a unified representation that is suitable for machine learn-
ing. Chapter 4 introduces the field of machine learning
and explains some of the most popular machine-learning
algorithms and models, including neural networks, deep
learning, and decision-tree models. Chapter 5 focuses on
linking machine-learning expertise with real-world prob-
lems by reviewing a range of standard business problems
and describing how they can be solved by machine-learning
solutions. Chapter 6 reviews the ethical implications of
data science, recent developments in data regulation,
and some of the new computational approaches to pre-
serving the privacy of individuals within the data science
process. Finally, chapter 7 describes some of the areas
where data science will have a significant impact in the
near future and sets out some of the principles that are
important in determining whether a data science project
will succeed.
A C K N O W L E D G M E N T S
John and Brendan thank Paul McElroy and Brian Leahy for
reading and commenting on early drafts. They also thank
the two anonymous reviewers who provided detailed and
helpful feedback on the manuscript and the staff at the
MIT Press for their support and guidance.
John thanks his family and friends for their sup-
port and encouragement during the preparation of this
book and dedicates this book to his father, John Bernard
Kelleher, in recognition of his love and friendship.
Brendan thanks Grace, Daniel, and Eleanor for their
constant support while he was writing yet another book
(his fourth), juggling the day jobs, and traveling.
1
W H AT I S D ATA S C I E N C E ?
Data science encompasses a set of principles, problem
definitions, algorithms, and processes for extracting non-
obvious and useful patterns from large data sets. Many
of the elements of data science have been developed in
related fields such as machine learning and data mining.
In fact, the terms data science, machine learning, and data
mining are often used interchangeably. The commonality
across these disciplines is a focus on improving decision
making through the analysis of data. However, although
data science borrows from these other fields, it is broader
in scope. Machine learning (ML) focuses on the design
and evaluation of algorithms for extracting patterns from
data. Data mining generally deals with the analysis of
structured data and often implies an emphasis on com-
mercial applications. Data science takes all of these consid-
erations into account but also takes up other challenges,
2 C h a p t e r 1
such as the capturing, cleaning, and transforming of
unstructured social media and web data; the use of big-
data technologies to store and process big, unstructured
data sets; and questions related to data ethics and
regulation.
Using data science, we can extract different types of
patterns. For example, we might want to extract patterns
that help us to identify groups of customers exhibiting
similar behavior and tastes. In business jargon, this task
is known as customer segmentation, and in data science
terminology it is called clustering. Alternatively, we might
want to extract a pattern that identifies products that are
frequently bought together, a process called association-
rule mining. Or we might want to extract patterns that
identify strange or abnormal events, such as fraudulent
insurance claims, a process known as anomaly or outlier
detection. Finally, we might want to identify patterns that
help us to classify things. For example, the following rule
illustrates what a classification pattern extracted from
an email data set might look like: If an email contains the
phrase “Make money easily,” it is likely to be a spam email.
Identifying these types of classification rules is known as
prediction. The word prediction might seem an odd choice
because the rule doesn’t predict what will happen in the
future: the email already is or isn’t a spam email. So it
is best to think of prediction patterns as predicting the
missing value of an attribute rather than as predicting
If a human expert can
easily create a pattern
in his or her own
mind, it is generally
not worth the time and
effort of using data
science to “discover” it.
4 C h a p t e r 1
the future. In this example, we are predicting whether the
email classification attribute should have the value “spam”
or not.
Although we can use data science to extract differ-
ent types of patterns, we always want the patterns to be
both nonobvious and useful. The example email classifica-
tion rule given in the previous paragraph is so simple and
obvious that if it were the only rule extracted by a data
science process, we would be disappointed. For example,
this email classification rule checks only one attribute of
an email: Does the email contain the phrase “make money
easily”? If a human expert can easily create a pattern in
his or her own mind, it is generally not worth the time
and effort of using data science to “discover” it. In general,
data science becomes useful when we have a large number
of data examples and when the patterns are too complex
for humans to discover and extract manually. As a lower
bound, we can take a large number of data examples to
be defined as more than a human expert can check easily.
With regard to the complexity of the patterns, again, we
can define it relative to human abilities. We humans are
reasonably good at defining rules that check one, two, or
even three attributes (also commonly referred to as fea-
tures or variables), but when we go higher than three attri-
butes, we can start to struggle to handle the interactions
between them. By contrast, data science is often applied in
contexts where we want to look for patterns among tens,
W h at I s D ata s C I e n C e ? 5
hundreds, thousands, and, in extreme cases, millions of
attributes.
The patterns that we extract using data science are
useful only if they give us insight into the problem that
enables us to do something to help solve the problem. The
phrase actionable insight is sometimes used in this context
to describe what we want the extracted patterns to give us.
The term insight highlights that the pattern should give
us relevant information about the problem that isn’t ob-
vious. The term actionable highlights that the insight we
get should also be something that we have the capacity to
use in some way. For example, imagine we are working for
a cell phone company that is trying to solve a customer
churn problem—that is, too many customers are switching
to other companies. One way data science might be used to
address this problem is to extract patterns from the data
about previous customers that allow us to identify current
customers who are churn risks and then contact these cus-
tomers and try to persuade them to stay with us. A pattern
that enables us to identify likely churn customers is useful
to us only if (a) the patterns identify the customers early
enough that we have enough time to contact them before
they churn and (b) our company is able to put a team in
place to contact them. Both of these things are required in
order for the company to be able to act on the insight the
patterns give us.
6 C h a p t e r 1
A Brief History of Data Science
The term data science has a specific history dating back
to the 1990s. However, the fields that it draws upon have
a much longer history. One thread in this longer history
is the history of data collection; another is the history of
data analysis. In this section, we review the main develop-
ments in these threads and describe how and why they
converged into the field of data science. Of necessity, this
review introduces new terminology as we describe and
name the important technical innovations as they arose.
For each new term, we provide a brief explanation of its
meaning; we return to many of these terms later in the
book and provide a more detailed explanation of them.
We begin with a history of data collection, then give a his-
tory of data analysis, and, finally, cover the development
of data science.
A History of Data Gathering
The earliest methods for recording data may have been
notches on sticks to mark the passing of the days or poles
stuck in the ground to mark sunrise on the solstices. With
the development of writing, however, our ability to re-
cord our experiences and the events in our world vastly
increased the amount of data we collected. The earliest
form of writing developed in Mesopotamia around 3200
BC and was used for commercial record keeping. This type
W h at I s D ata s C I e n C e ? 7
of record keeping captures what is known as transactional
data. Transactional data include event information such
as the sale of an item, the issuing of an invoice, the deliv-
ery of goods, credit card payment, insurance claims, and
so on. Nontransactional data, such as demographic data,
also have a long history. The earliest-known censuses took
place in pharaonic Egypt around 3000 BC. The reason that
early states put so much effort and resources into large
data-collection operations was that these states needed to
raise taxes and armies, thus proving Benjamin Franklin’s
claim that there are only two things certain in life: death
and taxes.
In the past 150 years, the development of the elec-
tronic sensor, the digitization of data, and the invention
of the computer have contributed to a massive increase
in the amount of data that are collected and stored. A
milestone in data collection and storage occurred in 1970
when Edgar F. Codd published a paper explaining the re-
lational data model, which was revolutionary in terms of
setting out how data were (at the time) stored, indexed,
and retrieved from databases. The relational data model
enabled users to extract data from a database using simple
queries that defined what data the user wanted without
requiring the user to worry about the underlying structure
of the data or where they were physically stored. Codd’s
paper provided the foundation for modern databases and
the development of structured query language (SQL), an
8 C h a p t e r 1
international standard for defining database queries. Re-
lational databases store data in tables with a structure of
one row per instance and one column per attribute. This
structure is ideal for storing data because it can be decom-
posed into natural attributes.
Databases are the natural technology to use for storing
and retrieving structured transactional or operational data
(i.e., the type of data generated by a company’s day-to-day
operations). However, as companies have become larger
and more automated, the amount and variety of data
generated by different parts of these companies have dra-
matically increased. In the 1990s, companies realized that
although they were accumulating tremendous amounts
of data, they were repeatedly running into difficulties in
analyzing those data. Part of the problem was that the
data were often stored in numerous separate databases
within the one organization. Another difficulty was that
databases were optimized for storage and retrieval of data,
activities characterized by high volumes of simple opera-
tions, such as SELECT, INSERT, UPDATE, and DELETE. In
order to analyze their data, these companies needed tech-
nology that was able to bring together and reconcile the
data from disparate databases and that facilitated more
complex analytical data operations. This business chal-
lenge led to the development of data warehouses. In a data
warehouse, data are taken from across the organization
W h at I s D ata s C I e n C e ? 9
and integrated, thereby providing a more comprehensive
data set for analysis.
Over the past couple of decades, our devices have be-
come mobile and networked, and many of us now spend
many hours online every day using social technologies,
computer games, media platforms, and web search en-
gines. These changes in technology and how we live have
had a dramatic impact on the amount of data collected.
It is estimated that the amount of data collected over the
five millennia since the invention of writing up to 2003
is about 5 exabytes. Since 2013, humans generate and
store this same amount of data every day. However, it is
not only the amount of data collected that has grown dra-
matically but also the variety of data. Just consider the
following list of online data sources: emails, blogs, pho-
tos, tweets, likes, shares, web searches, video uploads,
online purchases, podcasts. And if we consider the meta-
data (data describing the structure and properties of the
raw data) of these events, we can begin to understand the
meaning of the term big data. Big data are often defined in
terms of the three Vs: the extreme volume of data, the va-
riety of the data types, and the velocity at which the data
must be processed.
The advent of big data has driven the development
of a range of new database technologies. This new gen-
eration of databases is often referred to as “NoSQL da-
tabases.” They typically have a simpler data model than
1 0 C h a p t e r 1
traditional relational databases. A NoSQL database stores
data as objects with attributes, using an object notation
language such as the JavaScript Object Notation (JSON).
The advantage of using an object representation of data
(in contrast to a relational table-based model) is that the
set of attributes for each object is encapsulated within the
object, which results in a flexible representation. For ex-
ample, it may be that one of the objects in the database,
compared to other objects, has only a subset of attributes.
By contrast, in the standard tabular data structure used
by a relational database, all the data points should have
the same set of attributes (i.e., columns). This flexibility in
object representation is important in contexts where the
data cannot (due to variety or type) naturally be decom-
posed into a set of structured attributes. For example, it
can be difficult to define the set of attributes that should
be used to represent free text (such as tweets) or images.
However, although this representational flexibility allows
us to capture and store data in a variety of formats, these
data still have to be extracted into a structured format be-
fore any analysis can be performed on them.
The existence of big data has also led to the develop-
ment of new data-processing frameworks. When you are
dealing with large volumes of data at high speeds, it can
be useful from a computational and speed perspective to
distribute the data across multiple servers, process que-
ries by calculating partial results of a query on each server,
W h at I s D ata s C I e n C e ? 1 1
and then merge these results to generate the response to
the query. This is the approach taken by the MapReduce
framework on Hadoop. In the MapReduce framework, the
data and queries are mapped onto (or distributed across)
multiple servers, and the partial results calculated on each
server are then reduced (merged) together.
A History of Data Analysis
Statistics is the branch of science that deals with the col-
lection and analysis of data. The term statistics originally
referred to the collection and analysis of data about the
state, such as demographics data or economic data. How-
ever, over time the type of data that statistical analysis
was applied to broadened so that today statistics is used
to analyze all types of data. The simplest form of statisti-
cal analysis of data is the summarization of a data set in
terms of summary (descriptive) statistics (including mea-
sures of a central tendency, such as the arithmetic mean, or
measures of variation, such as the range). However, in the
seventeenth and eighteenth centuries the work of people
such as Gerolamo Cardano, Blaise Pascal, Jakob Bernoulli,
Abraham de Moivre, Thomas Bayes, and Richard Price
laid the foundations of probability theory, and through
the nineteenth century many statisticians began to use
probability distributions as part of their analytic tool kit.
These new developments in mathematics enabled statis-
ticians to move beyond descriptive statistics and to start
1 2 C h a p t e r 1
doing statistical learning. Pierre Simon de Laplace and Carl
Friedrich Gauss are two of the most important and famous
nineteenth-century mathematicians, and both made im-
portant contributions to statistical learning and modern
data science. Laplace took the intuitions of Thomas Bayes
and Richard Price and developed them into the first ver-
sion of what we now call Bayes’ Rule. Gauss, in his search
for the missing dwarf planet Ceres, developed the method
of least squares, which enables us to find the best model
that fits a data set such that the error in the fit minimizes
the sum of squared differences between the data points in
the data set and the model. The method of least squares
provided the foundation for statistical learning methods
such as linear regression and logistic regression as well as the
development of artificial neural network models in artifi-
cial intelligence (we will return to least squares, regression
analysis, and neural networks in chapter 4).
Between 1780 and 1820, around the same time that
Laplace and Gauss were making their contributions to
statistical learning, a Scottish engineer named William
Playfair was inventing statistical graphics and laying the
foundations for modern data visualization and exploratory
data analysis. Playfair invented the line chart and area chart
for time-series data, the bar chart to illustrate compari-
sons between quantities of different categories, and the
pie chart to illustrate proportions within a set. The advan-
tage of visualizing quantitative data is that it allows us to
W h at I s D ata s C I e n C e ? 1 3
use our powerful visual abilities to summarize, compare,
and interpret data. Admittedly, it is difficult to visualize
large (many data points) or complex (many attributes)
data sets, but data visualization is still an important part
of data science. In particular, it is useful in helping data sci-
entists explore and understand the data they are working
with. Visualizations can also be useful to communicate the
results of a data science project. Since Playfair’s time, the
variety of data-visualization graphics has steadily grown,
and today there is research ongoing into the development
of novel approaches to visualize large, multidimensional
data sets. A recent development is the t-distributed stochas-
tic neighbor embedding (t-SNE) algorithm, which is a use-
ful technique for reducing high-dimensional data down to
two or three dimensions, thereby facilitating the visualiza-
tion of those data.
The developments in probability theory and statis-
tics continued into the twentieth century. Karl Pearson
developed modern hypothesis testing, and R. A. Fisher
developed statistical methods for multivariate analysis
and introduced the idea of maximum likelihood estimate
into statistical inference as a method to draw conclusions
based on the relative probability of events. The work of
Alan Turing in the Second World War led to the inven-
tion of the electronic computer, which had a dramatic
impact on statistics because it enabled much more com-
plex statistical calculations. Throughout the 1940s and
1 4 C h a p t e r 1
subsequent decades, a number of important computa-
tional models were developed that are still widely used in
data science. In 1943, Warren McCulloch and Walter Pitts
proposed the first mathematical model of a neural net-
work. In 1948, Claude Shannon published “A Mathemati-
cal Theory of Communication” and by doing so founded
information theory. In 1951, Evelyn Fix and Joseph Hodges
proposed a model for discriminatory analysis (what would
now be called a classification or pattern-recognition prob-
lem) that became the basis for modern nearest-neighbor
models. These postwar developments culminated in 1956
in the establishment of the field of artificial intelligence
at a workshop in Dartmouth College. Even at this early
stage in the development of artificial intelligence, the
term machine learning was beginning to be used to de-
scribe programs that gave a computer the ability to learn
from data. In the mid-1960s, three important contribu-
tions to ML were made. In 1965, Nils Nilsson’s book titled
Learning Machines showed how neural networks could be
used to learn linear models for classification. The follow-
ing year, 1966, Earl B. Hunt, Janet Marin, and Philip J.
Stone developed the concept-learning system frame-
work, which was the progenitor of an important family
of ML algorithms that induced decision-tree models from
data in a top-down fashion. Around the same time, a
number of independent researchers developed and pub-
lished early versions of the k-means clustering algorithm,
W h at I s D ata s C I e n C e ? 1 5
now the standard algorithm used for data (customer)
segmentation.
The field of ML is at the core of modern data science
because it provides algorithms that are able to automati-
cally analyze large data sets to extract potentially interest-
ing and useful patterns. Machine learning has continued
to develop and innovate right up to the present day. Some
of the most important developments include ensemble
models, where predictions are made using a set (or com-
mittee) of models, with each model voting on each query,
and deep-learning neural networks, which have multiple
(i.e., more than three) layers of neurons. These deeper lay-
ers in the network are able to discover and learn complex
attribute representations (composed of multiple, interact-
ing input attributes that have been processed by earlier
layers), which in turn enable the network to learn patterns
that generalize across the input data. Because of their abil-
ity to learn complex attributes, deep-learning networks
are particularly suitable to high-dimensional data and so
have revolutionized a number of fields, including machine
vision and natural-language processing.
As we discussed in our review of database history, the
early 1970s marked the beginning of …
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*** In-Text Citations and References using Harvard style.
*** In Task section I’ve chose (Economic issues in overseas contracting)"
Electromagnetism
w or quality improvement; it was just all part of good nursing care. The goal for quality improvement is to monitor patient outcomes using statistics for comparison to standards of care for different diseases
e a 1 to 2 slide Microsoft PowerPoint presentation on the different models of case management. Include speaker notes... .....Describe three different models of case management.
visual representations of information. They can include numbers
SSAY
ame workbook for all 3 milestones. You do not need to download a new copy for Milestones 2 or 3. When you submit Milestone 3
pages):
Provide a description of an existing intervention in Canada
making the appropriate buying decisions in an ethical and professional manner.
Topic: Purchasing and Technology
You read about blockchain ledger technology. Now do some additional research out on the Internet and share your URL with the rest of the class
be aware of which features their competitors are opting to include so the product development teams can design similar or enhanced features to attract more of the market. The more unique
low (The Top Health Industry Trends to Watch in 2015) to assist you with this discussion.
https://youtu.be/fRym_jyuBc0
Next year the $2.8 trillion U.S. healthcare industry will finally begin to look and feel more like the rest of the business wo
evidence-based primary care curriculum. Throughout your nurse practitioner program
Vignette
Understanding Gender Fluidity
Providing Inclusive Quality Care
Affirming Clinical Encounters
Conclusion
References
Nurse Practitioner Knowledge
Mechanics
and word limit is unit as a guide only.
The assessment may be re-attempted on two further occasions (maximum three attempts in total). All assessments must be resubmitted 3 days within receiving your unsatisfactory grade. You must clearly indicate “Re-su
Trigonometry
Article writing
Other
5. June 29
After the components sending to the manufacturing house
1. In 1972 the Furman v. Georgia case resulted in a decision that would put action into motion. Furman was originally sentenced to death because of a murder he committed in Georgia but the court debated whether or not this was a violation of his 8th amend
One of the first conflicts that would need to be investigated would be whether the human service professional followed the responsibility to client ethical standard. While developing a relationship with client it is important to clarify that if danger or
Ethical behavior is a critical topic in the workplace because the impact of it can make or break a business
No matter which type of health care organization
With a direct sale
During the pandemic
Computers are being used to monitor the spread of outbreaks in different areas of the world and with this record
3. Furman v. Georgia is a U.S Supreme Court case that resolves around the Eighth Amendments ban on cruel and unsual punishment in death penalty cases. The Furman v. Georgia case was based on Furman being convicted of murder in Georgia. Furman was caught i
One major ethical conflict that may arise in my investigation is the Responsibility to Client in both Standard 3 and Standard 4 of the Ethical Standards for Human Service Professionals (2015). Making sure we do not disclose information without consent ev
4. Identify two examples of real world problems that you have observed in your personal
Summary & Evaluation: Reference & 188. Academic Search Ultimate
Ethics
We can mention at least one example of how the violation of ethical standards can be prevented. Many organizations promote ethical self-regulation by creating moral codes to help direct their business activities
*DDB is used for the first three years
For example
The inbound logistics for William Instrument refer to purchase components from various electronic firms. During the purchase process William need to consider the quality and price of the components. In this case
4. A U.S. Supreme Court case known as Furman v. Georgia (1972) is a landmark case that involved Eighth Amendment’s ban of unusual and cruel punishment in death penalty cases (Furman v. Georgia (1972)
With covid coming into place
In my opinion
with
Not necessarily all home buyers are the same! When you choose to work with we buy ugly houses Baltimore & nationwide USA
The ability to view ourselves from an unbiased perspective allows us to critically assess our personal strengths and weaknesses. This is an important step in the process of finding the right resources for our personal learning style. Ego and pride can be
· By Day 1 of this week
While you must form your answers to the questions below from our assigned reading material
CliftonLarsonAllen LLP (2013)
5 The family dynamic is awkward at first since the most outgoing and straight forward person in the family in Linda
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The most important benefit of my statistical analysis would be the accuracy with which I interpret the data. The greatest obstacle
From a similar but larger point of view
4 In order to get the entire family to come back for another session I would suggest coming in on a day the restaurant is not open
When seeking to identify a patient’s health condition
After viewing the you tube videos on prayer
Your paper must be at least two pages in length (not counting the title and reference pages)
The word assimilate is negative to me. I believe everyone should learn about a country that they are going to live in. It doesnt mean that they have to believe that everything in America is better than where they came from. It means that they care enough
Data collection
Single Subject Chris is a social worker in a geriatric case management program located in a midsize Northeastern town. She has an MSW and is part of a team of case managers that likes to continuously improve on its practice. The team is currently using an
I would start off with Linda on repeating her options for the child and going over what she is feeling with each option. I would want to find out what she is afraid of. I would avoid asking her any “why” questions because I want her to be in the here an
Summarize the advantages and disadvantages of using an Internet site as means of collecting data for psychological research (Comp 2.1) 25.0\% Summarization of the advantages and disadvantages of using an Internet site as means of collecting data for psych
Identify the type of research used in a chosen study
Compose a 1
Optics
effect relationship becomes more difficult—as the researcher cannot enact total control of another person even in an experimental environment. Social workers serve clients in highly complex real-world environments. Clients often implement recommended inte
I think knowing more about you will allow you to be able to choose the right resources
Be 4 pages in length
soft MB-920 dumps review and documentation and high-quality listing pdf MB-920 braindumps also recommended and approved by Microsoft experts. The practical test
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One thing you will need to do in college is learn how to find and use references. References support your ideas. College-level work must be supported by research. You are expected to do that for this paper. You will research
Elaborate on any potential confounds or ethical concerns while participating in the psychological study 20.0\% Elaboration on any potential confounds or ethical concerns while participating in the psychological study is missing. Elaboration on any potenti
3 The first thing I would do in the family’s first session is develop a genogram of the family to get an idea of all the individuals who play a major role in Linda’s life. After establishing where each member is in relation to the family
A Health in All Policies approach
Note: The requirements outlined below correspond to the grading criteria in the scoring guide. At a minimum
Chen
Read Connecting Communities and Complexity: A Case Study in Creating the Conditions for Transformational Change
Read Reflections on Cultural Humility
Read A Basic Guide to ABCD Community Organizing
Use the bolded black section and sub-section titles below to organize your paper. For each section
Losinski forwarded the article on a priority basis to Mary Scott
Losinksi wanted details on use of the ED at CGH. He asked the administrative resident