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APA formatted Question-1: The impact of the global digital divide. According to the authors, countries that lag behind the rest of the worlds ICT ca

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The impact of the global digital divide. According to the authors, countries that lag behind the rest of the worlds ICT capabilities encounter difficulties at various levels. Discuss specific areas, both within and outside, eGovernance, in which citizens living in a country that lags behind the rest of the world in ICT capacity are lacking. Include in your discussion quality of life, sustainability, safety, affluence, and any other areas that you find of interest. Use at least 8-10 sentences to discuss this topic.

Both within and outside, eGovernance, in which citizens living in a country that lags behind the rest of the world in ICT capacity are lacking. Include in your discussion quality of life, sustainability, safety, affluence, and any other areas that you find of interest. Use at least 8-10 sentences to discuss this topic.
In order to receive full credit for the initial discussion post, you must include at least two citations (APA) from academic resources

Question 2: —– 500 words
APA Edition 7 has been just released. Please do an independent research to assess the readiness of APA Edition 7.0 for deployment.
-Do you think APA version 7.0 is mature for adoption?Please provide a definite answer

Question 3: — 600 words — please find the attached document below.

Please refer to the article Data scientist and software development. Please read the entire paper. Summarize your understanding and present it, with focus on a) working styles and b) development life cycle.

Minimum 600 words
Use APA formatting (100%)
Minimum two additional references

The Emerging Role of Data Scientists
on Software Development Teams

Miryung Kim

Los Angeles, CA, USA


Thomas Zimmermann Robert DeLine Andrew Begel
Microsoft Research
Redmond, WA, USA

{tzimmer, rdeline, andrew.begel}

Creating and running software produces large amounts of raw data

about the development process and the customer usage, which can

be turned into actionable insight with the help of skilled data scien-

tists. Unfortunately, data scientists with the analytical and software

engineering skills to analyze these large data sets have been hard to

come by; only recently have software companies started to develop

competencies in software-oriented data analytics. To understand

this emerging role, we interviewed data scientists across several

product groups at Microsoft. In this paper, we describe their educa-

tion and training background, their missions in software engineer-

ing contexts, and the type of problems on which they work. We

identify five distinct working styles of data scientists: (1) Insight

Providers, who work with engineers to collect the data needed to

inform decisions that managers make; (2) Modeling Specialists,

who use their machine learning expertise to build predictive mod-

els; (3) Platform Builders, who create data platforms, balancing

both engineering and data analysis concerns; (4) Polymaths, who

do all data science activities themselves; and (5) Team Leaders,

who run teams of data scientists and spread best practices. We fur-

ther describe a set of strategies that they employ to increase the im-

pact and actionability of their work.

Categories and Subject Descriptors:

D.2.9 [Management]

General Terms:
Management, Measurement, Human Factors.

Software teams are increasingly using data analysis to inform their

engineering and business decisions [1] and to build data solutions

that utilize data in software products [2]. The people who do col-

lection and analysis are called data scientists, a term coined by DJ

Patil and Jeff Hammerbacher in 2008 to define their jobs at

LinkedIn and Facebook [3]. The mission of a data scientist is to

transform data into insight, providing guidance for leaders to take

action [4]. One example is the use of user telemetry data to redesign

Windows Explorer (a tool for file management) for Windows 8.

Data scientists on the Windows team discovered that the top ten

most frequent commands accounted for 81.2% of all of invoked

commands, but only two of these were easily accessible from the

command bar in the user interface 8 [5]. Based on this insight, the

team redesigned the user experience to make these hidden com-

mands more prominent.

Until recently, data scientists were found mostly on software teams

whose products were data-intensive, like internet search and adver-

tising. Today, we have reached an inflection point where many soft-

ware teams are starting to adopt data-driven decision making. The

role of data scientist is becoming standard on development teams,

alongside existing roles like developers, testers, and program man-

agers. Online service-oriented businesses such as Bing or Azure of-

ten require that software quality to be assessed in the field (testing

in production); as a result, Microsoft changed the test discipline and

hires data scientists to help with analyzing the large amount of us-

age data. With more rapid and continuous releases of software [6],

software development teams also need effective ways to operation-

alize data analytics by iteratively updating the software to gather

new data and automatically produce new analysis results.

So far, there have been only a few studies about data scientists,

which focused on the limitations of big data cloud computing tools

and the pain points that data scientists face, based on the experi-

ences of participants from several types of businesses [7, 8]. How-

ever, these studies have not investigated the emerging roles that

data scientists play within software development teams.

To investigate this emerging role, we interviewed 16 data scientists

from eight different product organizations within Microsoft. Dur-

ing the period of our interviews, Microsoft was in the process of

defining an official career path for employees in the role of data

scientist, that is, defining the knowledge and skills expected of the

role at different career stages. This process made Microsoft a par-

ticularly fruitful location to conduct our research, and several of our

participants took part in this process. We investigated the following

research questions:

Q1 Why are data scientists needed in software development
teams and what competencies are important?

Q2 What are the educational and training backgrounds of data

scientists in software development teams?

Q3 What kinds of problems and activities do data scientists work
on in software development teams?

Q4 What are the working styles of data scientists in software de-

velopment teams?

This paper makes the following contributions:

We characterize the roles of data scientists in a large software
company. (Section 4)

We explore various working styles of data scientists. (Section 5)

The paper concludes with a discussion of implications (Section 6).

Permission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies are

not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page. Copyrights for com-

ponents of this work owned by others than the author(s) must be honored.

Abstracting with credit is permitted. To copy otherwise, or republish, to
post on servers or to redistribute to lists, requires prior specific permission

and/or a fee. Request permissions from [emailprotected]

ICSE16, May 1422, 2016, Austin, TX, USA.

Copyright is held by the owner/author(s). Publication rights licensed to
ACM. ACM 978-1-4503-3900-1/16/05 $15.00.


2016 IEEE/ACM 38th IEEE International Conference on Software Engineering


The work related to this paper falls into general data science and

software analytics.

Data Science has become popular over the past few years as com-

panies have recognized the value of data, for example, as data prod-

ucts, to optimize operations, and to support decision making. Not

only did Davenport and Patil [9] proclaim that data scientist would

be the sexiest job of the 21st century, many authors have pub-

lished data science books based on their own experiences, e.g.,

ONeill and Schutt [10], Foreman [11], or May [12]. Patil summa-

rized strategies to hire and build effective data science teams based

on his experience in building the data science team at LinkedIn [3].

We found a small number of studies which systematically focused

on how data scientists work inside a company. Fisher et al. inter-

viewed sixteen Microsoft data analysts working with large datasets,

with the goal of identifying pain points from a tooling perspective

[7]. They uncovered tooling challenges in big data computing plat-

forms such as data integration, cloud computing cost estimation,

difficulties shaping data to the computing platform, and the need

for fast iteration on the analysis results. However, they did not de-

scribe the roles that data scientists play within software develop-

ment teams.

In a survey, Harris et al. asked 250+ data science practitioners how

they viewed their skills, careers, and experiences with prospective

employers [13]. Then, they clustered the survey respondents into

four roles: Data Businesspeople, Data Creatives, Data Developers,

and Data Researchers. They also observed evidence for so-called

T-shaped data scientists, who have a wide breadth of skills with

depth in a single skill area. Harris et al. focus on general business

intelligence analysts rather than data scientists in a software devel-

opment organization. Due to the nature of a survey research

method, Harris et al. also do not provide contextual, deeper findings

on what types of problems that data scientists work on, and the

strategies that they use to increase the impact of their work.

Kandel et al. conducted interviews with 35 enterprise analysts in

healthcare, retail, marketing, and finance [8]. Companies of all

kinds have long employed business intelligence analysts to improve

sales and marketing strategies. However, the data scientists we

study are different in that they are an integral part of the software

engineering team and focus their attention on software-oriented

data and applications. Unlike our work, the Kandel et al. study does

not investigate how data scientists contribute to software debug-

ging, defect prediction, and software usage data (telemetry) collec-

tion in software development contexts.

Software Analytics is a subfield of analytics with the focus on soft-

ware data. Software data can take many forms such as source code,

changes, bug reports, code reviews, execution data, user feedback,

and telemetry information. Davenport, Harris, and Morison [4] de-

fine analytics as the use of analysis, data, and systematic reasoning

to make decisions. According to an Accenture survey of 254 US

managers in industry, however, up to 40 percent of major decisions

are based on gut feel rather than facts [14]. Due to the recent boom

in big data, several research groups have pushed for greater use of

data for decision making [15, 16, 17] and have shared their experi-

ences collaborating with industry on analytics projects [18, 16, 19].

Analysis of software data has a long tradition in the research com-

munities of empirical software engineering, software reliability,

and mining software repositories [1]. Software analytics has been

the dedicated topic of tutorials and panels at the ICSE conference

[20, 21], as well as special issues of IEEE Software (July 2013 and

September 2013). Zhang et al. [22] emphasized the trinity of soft-

ware analytics in the form of three research topics (development

process, system, users) as well as three technology pillars (infor-

mation visualization, analysis algorithms, large-scale computing).

Buse and Zimmermann argued for a dedicated data science role in

software projects [17] and presented an empirical survey with soft-

ware professionals on guidelines for analytics in software develop-

ment [23]. They identified typical scenarios and ranked popular in-

dicators among software professionals. Begel and Zimmermann

collected 145 questions that software engineers would like to ask

data scientists to investigate [24]. None of this work has focused on

the characterization of data scientists on software teams, which is

one of the contributions of this paper.

Many software companies such as LinkedIn, Twitter, and Facebook

employ data scientists to analyze user behavior and user-provided

content data [25, 26, 27]. However, the authors of these published

reports concentrate mainly on their big data pipeline architectures

and implementations, and ignore the organizational architecture

and work activities of the data scientists themselves. According to

our study, data scientists in software teams have a unique focus in

analyzing their own software teams engineering processes to im-

prove software correctness and developer productivity.

It is common to expect that action and insight should drive the col-

lection of data. Goal-oriented approaches use goals, objectives,

strategies, and other mechanisms to guide the choice of data to be

collected and analyzed. For example, the Goal/Question/Metric

(GQM) paradigm [28] proposes a top-down approach to define

measurement; goals lead to questions, which are then answered

with metrics. Other well-known approaches are GQM+ (which

adds business alignment to GQM) [29], Balanced Scorecard (BSC)

[30], and Practical Software Measurement [31].

Basili et al. [32] proposed the Experience Factory, which is an in-

dependent organization to support a software development organi-

zation in collecting experiences from their projects. The Experience

Factory packages these experiences (for example, in models) and

validates and reuses experiences in future projects. Some of the

team structures that we observed in the interviews were similar to

an Experience Factory in spirit; however, many data scientists were

also directly embedded in the development organizations. While

some experiences can be reused across different products, not all

insight is valid and actionable in different contexts.

We interviewed people who acted in the role of data scientists, then

formed a theory of the roles that data scientists play in software

development organizations.

Protocol. We conducted one-hour, semi-structured interviews, giv-

ing us the advantage of allowing unanticipated information to be

mentioned [33]. All interviews were conducted by two people.

Each was led by the first author, who was accompanied by one of

the other three authors (as schedules permitted) who took notes and

asked additional questions. Interviews were audio-taped and later

transcribed for analysis. The interview format started with an intro-

duction, a short explanation of the research being conducted, and

demographic questions. Participants were then asked about the role

they played on their team, their data science-related background,

their current project(s), and their interactions with other employees.

We also asked for stories about successes, pitfalls, and the changes

that data is having on their teams practices. Our interview guide is

in Appendix A.


Participants. In total, we interviewed 16 participants (5 women,

11 men) from eight different organizations at Microsoft: Advanced

Technology Lab (1 participant), Advertisement and Monetization

(1), Azure (2), Bing (1), Engineering Excellence (1), Exchange (1),

Office (1), Skype (2), Windows (4), and Xbox (2).

We selected participants by snowball sampling [34]:

First, we identified presenters at data-driven engineering meet-
ups and technical community meetings, since these have been
responsible internally for sharing best practices.

Next, we selected additional data scientists by word-of-mouth,
asking each participant to introduce us to other data scientists or

other key stakeholders whom they knew.

At the time of this study in Summer 2014, there was no easy way

to identify those who do data science work at Microsoft. In fact,

Microsoft was in the process of creating a new job discipline called

data and applied science. Therefore, we used snowball sampling

because it helped us locate hidden populations of data science prac-

titioners, such as those employees working on data science tasks

who do not have data or data science in their job title (see Table

1). As mentioned by P15, a lot of people kind of moonlighted as

data scientists besides their normal day job. Our sampling method

may have caused us to miss some data scientists, however, to miti-

gate this threat, we seeded our sample with data science thought

leaders from various product teams identified through company-

wide engineering meetups and technical community talks.

Our findings reached saturation after interviewing 16 people. There

was enough diversity in the participants responses to enable us to

find meaningful themes and draw useful interpretations. Stopping

after saturation is standard procedure in qualitative studies.

Data Analysis. The authors individually used the Atlas.TI qualita-

tive coding tool ( to code emerging themes from

the transcripts; together, we discussed the taxonomies derived from

the interview transcripts. In concert, we employed affinity diagram-

ming [35] and card sorting [36] to make sense of our data. Figure 1

shows a screen snapshot of Atlas.TI with an interview transcript

excerpt and corresponding code describing emerging themes. In or-

der to further help with traceability and to provide the details of our

data analysis process, our technical report lists the codes we de-

rived, along with supporting quotes [37].

To infer the working styles of data scientists (Q4), we performed

two card sorts based on the roles data scientists played. One was

done by the first author, another by the second and third authors.

When participants shared experiences from multiple roles, we put

each role on a separate card. This happened when participants

shared experiences from previous jobs on different teams (P2 and

P12) or had multiple responsibilities (P15 manages one team of en-

gineers building a machine learning platform and another team of

data scientists using the platform to create models). Both card sorts

led to similar groupings of the participants, which are discussed

later in the paper.


Title Education

P1 Data Scientist II BS in CS / Statistics, MS in SE, currently pursuing PhD in Informatics

P2 Director, App Statistics Engineer MS in Physics

P3 Principal Data Scientist MBA, BS in Physics / CS, currently pursuing PhD in Statistics

P4 Principal Quality Manager BS in CS

P5 Partner Data Science Architect PhD in Applied Mathematics

P6 Principal Data Scientist PhD in Physics

P7 Research Software Design Engineer II MS in Computer Science, MS in Statistics

P8 Program Manager BS in Cognitive Science

P9 Senior Program Manager BSE in CS and BAS in Economics/Finance

P10 Director of Test BS in CS

P11 Principal Dev Manager MS in CS

P12 Data Scientist PhD in CS / Machine Learning

P13 Applied Scientist PhD in CS / Machine Learning and Database

P14 Principal Group Program Manager BS in business

P15 Director of Data Science PhD in CS / Machine Learning

P16 Senior Data Scientist PhD in CS / Machine Learning

Figure 1. An interview transcript excerpt in Atlas.TI. Using this tool, we added codes describing emerging themes.


We categorized our results in terms of how and why data scientists

are employed in a large software company (Section 4), the working

styles that data scientists use with software development teams and

their strategies to increase the impact of their work (Section 5).

Limitations. This is a qualitative study based on 16 interviews. In

this paper, we share the observations and themes that emerged in

the interviews. Because experts in qualitative studies specifically

warn the danger of quantifying inherently qualitative data [38, 39],

we do not make any quantitative statements about how frequently

the themes occur in broader populations. We follow this guideline

to focus on providing insights that contextualize our empirical find-

ings, especially when they can serve as the basis for further studies,

such as surveys.

Although this study was conducted only in one company, the par-

ticipants came from eight different organizations, each working on

different kinds of products. Several of our participants spoke of data

science experiences they had prior to joining Microsoft. We believe

that the nature of data science work in this context is meaningful,

given that very few companies of a similar scale exist in the soft-

ware industry. In addition, the software engineering research com-

munity also uses large-scale analysis of various types of software


Data science is not a new field, but the prevalence of interest in it

at Microsoft has grown rapidly in the last few years. In 2015, one

year after this study, over six hundred people are now in the new

data and applied science discipline and an additional 1600+ em-

ployees are interested in data science work and signed up to mailing

lists related to data science topics.

We observed an evolution of data science in the company, both in

terms of technology and people. Product leaders across the com-

pany are eager to be empowered to make data-driven engineering

decisions, rather than relying on gut feel. Some study participants

who initially started as vendors or individual contributors have

moved into management roles. Participants also reported that infra-

structure that was initially developed to support a single data-driven

task, for example, the Windows Error Reporting tool used for col-

lecting crash data from in-field software deployments [40], was

later extended to support multiple tasks and multiple stakeholders.

Separate organizations within the company merged their data engi-

neering efforts to build common infrastructure. The term, data sci-

entist in this paper is a logical role, rather than the title of a posi-

tion. Our goal is to understand and define this data scientist role by

studying the participants in terms of their training and education

background, what they work on, and how they fit in their organiza-

tion, rather than restricting our study to those with the title data


4.1 Why are Data Scientists Needed in
Software Development Teams?
Data-driven decision making has increased the demand for data sci-

entists with statistical knowledge and skills. Specifically, partici-

pants described the increasing need for knowledge about experi-

mental design, statistical reasoning, and data collection.

Demand for Experimentation. As the test-in-production para-

digm for on-line services has taken off, our participants recognized

the opportunity and need for designing experiments with real user

data [41]. Real customer usage data is easier to obtain and more

authentic than the simulated data test engineers create for antici-

pated usage scenarios.

Instead of having an army of testers to go off and generate a
bunch of data, that data’s already here. It’s more authentic be-

cause its real customers on real machines, real networks. You

no longer have to simulate and anticipate what the customers

going to do. [P10]

Participants mentioned an increase in the demand for experiment-

ing with alternative software implementations, in order to assess

the requirements and utility of new software features. Over the last

decade, randomized two-variant experiments (called A/B testing)

have been used to assess the utility of software prototypes and fea-

tures, particularly for online services like web search. Because there

are endless possibilities for alternative software designs, data sci-

entists and engineering teams build software systems with an inher-

ent capability to inject changes, called flighting.

You create an environment where, for example, in search, where
I can actually experiment based on a mockup, if you will, of the

idea. I can actually come up with a set of ideas, broad ideas

about my work, and I can actually deploy them in some easy

way. [P5]

Do I change the size? Do I change the font? There are so many
things you could do Were trying to flight things. It has capa-
bility to inject changes. [P11]

Several participants took it upon themselves both to design incen-

tive systems that get users to adopt a product feature and to create

user telemetry and surveys that measure whether the systems


So we create a game that gets people to repetitively use the fea-
ture. And then we watch what happens when we take the game
away. Did it stick or did it not stick? [P13]

Demand for Statistical Rigor. In the analysis of data, participants

told us that there is an increasing demand for statistical rigor. Data

scientists and their teams conduct formal hypothesis testing, report

confidence intervals, and determine baselines through normaliza-


For example, when participant P2 (who worked on estimating fu-

ture failures) reported her estimate to her manager, the manager

asked how confident she was. She gave him a hard number, sur-

prising him because whenever he had asked the question to previ-

ous employees, he had just been told highly confident or not very


He was like So, you are giving me some predictions. How con-
fident are you that this is what we get? And Im looking and go,

What do you mean? Its 95 percent! Its implied in all the test-

ing. This is how we define this whole stuff. And he goes, Wow,

this is the first time Im getting this answer. [P2]

There has been a similar increase in the demand for conducting for-

mal hypothesis testing. For example, instead of reasoning about av-

erages or means, engineering teams want to see how different their

observation is from random chance:

When I do my analyses, I always have a null hypothesis and an
alternative hypothesis. [P3]

Data scientists also have to determine a baseline of usual behavior

so they can normalize incoming data about system behavior and


telemetry data that are collected from a large set of machines under

many different conditions.

Ive got all of these different clients out in the wild running on
all these different servers. I want to get a general sense of what
things feel like on a normal Monday. [P8]

Demand for Data Collection Rigor. When it comes to collecting

data, data scientists discussed how much data quality matters and

how many data cleaning issues they have to manage. Many partic-

ipants mentioned that a large portion of their work required the

cleaning and shaping of data just to enable data analysis. This aligns

with a recent article on New York Times that said that 80% of data

science work requires janitor work [42].

We need to cleanse the data, because there are all sorts of data
quality issues [often, due to] imperfect instrumentation. [P11]

Furthermore, data collection itself requires a sophisticated engi-

neering system that tries to satisfy many engineering, organiza-

tional, and legal requirements.

What about storage, what about speed? What about legal, what
about privacy? There is an entire gamut of things that you need

to jump through hoops to collect the instrumentation. [P1]

4.2 Background of Data Scientists
One column in Table 1 shows the educational background of the

study participants. Data scientists often do not have a typical four-

year degree in Computer Science [12]. In our study, 11 of 16 par-

ticipants have degrees in Computer Science; however, many also

have joint degrees from other fields such as statistics, physics,

math, bio-informatics, applied math, business, economics, and fi-

nance. Their interdisciplinary backgrounds contribute their strong

numerical reasoning skills for data analysis. 11 participants have

higher education degrees (PhD or MS), and many have prior job

experiences with dealing with big data.

Several non-CS participants expressed a strong passion for data.

I love data, looking and making sense of the data. [P2]

Ive always been a data kind of guy. I love playing with data.
Im very focused on how you can organize and make sense of

data and being able to find patterns. I love patterns. [P14]

When data scientists hire other data scientists, they sometimes look

for skill sets that mirror how they were themselves trained. When

one team manager with a PhD in machine learning spoke about hir-

ing new employees for his data science tools team, he said that he

looks for machine learning hackers.

So, the typical guys on my team have some PhD in a quantitative
field with machine learning background and the ability to code.

They have to manipulate data. The other very essential skill is

[that] we want programming. It’s almost like … a hacker-type

skill set. [P15]

Another data science team manager with strong statistics back-

ground demanded the same from everyone on his team:

My people have to know statistics. They need to be able to an-
swer sample size questions, design experiment questions, know
standard deviations, p-value, confidence intervals, etc. [P2]

Our participants background in higher education also contributes

to how they view the work of data science. Usually, the problems

and questions are not given in advance. A large portion of their re-

sponsibility is to identify important questions that could lead to im-

pact. Then they iteratively refine questions and approaches to the

analyses. Participants from a variety of product teams discussed

how their training in a PhD program contributed to the working

style they use to identify important questions and iteratively refine

questions and approaches.

It has never been, in my four years, that somebody came and
said, Can you answer this question? I mostly sit around think-

ing, How can I be helpful? Probably that part of your PhD is
you are figuring out what is the most important questions. [P13]

I have a PhD in experimental physics, so pretty much, I am used
to designing experiments. [P6]

Doing data science is kind of like doing research. It looks like a
good problem and looks like a good idea. You think you may

have an approach, but then maybe you end up with a dead end.


4.3 Problems that Data Scientists Work on
Our participants said they worked on many kinds of problems rang-

ing from performance and quality regression, user engagement and

feature assessment, debugging and root cause analysis, bug repro-

duction, server log anomaly detection, failure rate estimation and

failure planning. They also worked on business-specific problems,

such as detecting fraud in e-commerce, identifying a mode of trans-

portation for mobile users, and assessing advertisement ranking and

news recommendations. Here are just a few of the example tasks

that participants told us they worked on.

Performance Regression. Are we getting better in terms of crashes

or worse? [P3] How long did it take to detect when a new fea-
ture has blown up your system? [P1]

Requirements Identification. If you see the repetitive pattern
where people dont recognize, the feature is there. [P3]

Fault Localization and Root Cause Analysis. What areas of the

product are failing and why? [P3] How many failures are there
per day? [P11]

Bug Prioritization. Oh, cool. Now we know which bugs we should
fix first. Then how can we reproduce this error? [P5]

Server Anomaly Detection. We are interested in anomaly detection

on real time servers in general. [P7] Is this application log ab-
normal w.r.t. the rest of the data? [P12]

Failure Rate Estimation. Is the beta ready to ship? [P8]

Customer Understanding. How long do our users use the app?

[P1] What are the most popular features? [P4] Is my feature
used in a way that improves the cus


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