Index
[SYMBOL][A][B][C][D][E][F][G][H][I][J][K][L][M][N][O][P][Q][R][S][T][U][V][W][Y][Z]
SYMBOL
A
abstracts, of talks
academia, conferences and
“Advice for new and junior data scientists” (Chang)
“Advice on applying to data science jobs” (Goodman)
alerting tools
algorithms
Allen, Jeff
Amazon Web Services in Action, 2nd ed. (Wittig and Wittig)
analyses
adding comments and explanations to
characteristics of good analysis
cleaning data
consistency of
data exploration
data modeling
delivery of
final presentation
finishing projects
first-time
format of
importing data
interview with Hilary Parker
plans for, 2nd
reports vs.
requests for
revisiting
sharing
styling of
analysts
APIs (application programming interfaces)
building
changes to
consistency of
creating, book about
deploying
to Docker container
to virtual machine
designing, book about
documentation for
maintaining
monitoring systems
retraining models
serving models with
applicant-tracking systems, 2nd, 3rd
applying for jobs
cover letters
referrals
résumés
generating
structure of
“art of slide design, The,” (Seckington)
Atwood, Jeff
Au, Randy, 2nd
authentication, consistency in
automatic retraining pipeline
AutoML (Automated Machine Learning)
autoscaling
averages
awards, on résumés
AWS (Amazon Web Services), 2nd
AWS SageMaker
Azure, 2nd
Azure in Action (Hay and Prince)
B
Barrows, Sam
Bartha, Emily
Bassa, Angela, 2nd
Beatty, Joy
Beautiful Evidence (Tufte)
behavioral questions, during job interviews, 2nd
data surprises
difficult problems
disagreements with teammates
previous job reflections
project that had most impact
senior person making mistake based on data
Betty, Joy
bias in sampled data
Bird by Bird (Lamott)
blameless postmortem
blogdown package
blogs, 2nd, 3rd, 4th
building readership for
getting invited to speak because of
interview with David Robinson
length of posts on
logistics of
of data scientists who could make referrals
of prospective employers
platforms for
potential topics for
turning documentation into posts for
Bolles, Richard N.
books, learning data science via
boosting
Bootcamp rankings (blog post)
bootcamps
choosing programs
cost of
networking in
project-based
skills acquired in
Boykis, Vicki
brain teasers, during job interviews, 2nd
combinatorics
estimation
breadth–depth trade-off
Bryan, Jennifer
bullet points, on résumés, 2nd
bureaucracy
at late-stage, successful tech startups
comparison between types of companies
Burnett, Bill
burnout, 2nd
business analysts
business domain expertise, questions about during interviews
business stakeholders
Butler, Allan
C
Cabrera, Beth
careers
advancement in
choosing path for
independent consulting
interview with Angela Bassa
management
principal data scientist track
analytics
business intelligence analyst
choosing, 2nd
data engineer
decision science
machine learning
related jobs
research scientist
Cartes, Rodrigo Fuentealba
Casari, Amanda
case studies, 2nd
Causey, Trey
CD (Continuous Deployment)
CFPs (calls for proposals)
Chang, Robert, 2nd
charts, on résumés
checkpoints
Chen, Daniel
CI (Continuous Integration)
CI/CD tool
Clarke, Arthur C.
cleaning data
“Climbing the relationship ladder to get a data science job” (Meloon)
clothing
at conferences
at interviews
cloud services
coding questions, during job interviews
FizzBuzz
frequently used library
frequently used package
Jupyter Notebooks
manipulating data in R/Python
prime numbers
reusing code
RMarkdown
technology decisions
working with Git
Coelho, Gustavo
Coleman, Mike
collaboration
between engineers and data scientists
problems with
collecting data
combinatorics, 2nd
comments
adding to analyses
adding to code
communicating with stakeholders, 2nd
communication
book about
with stakeholders
communities
contributions to programming
format of
mission statement of
purpose of, defining
competing offers, negotiations using
competition in job market
conferences
academia and
attire at
cost of, 2nd
diversity at
reviews of
size of
confidentiality agreements
confusion matrix
contact information on résumés
containers
Conway, Drew
Conway’s Venn diagram
corporate leadership
counteroffers
cover letters
structure of
tailoring
when to work on
Cracking the Coding Interview (McDowell)
critical path, of boss
critical thinking
current job, negotiating using
D
dashboards
consistency of
data analysts and
data
access to
cleaning, 2nd
collecting, 2nd
exploring
checking in with stakeholders
creating summary tables
maintaining focus
one-button run
plots for exploration vs. plots for sharing
simple methods for
for projects
importing
loading into SQL
manipulating in R/Python
modeling
checking in with stakeholders
creating models
creating summary tables
maintaining focus
one-button run
simple methods for
reviewing for new job
sampled, bias in
summarizing
training vs. test data
transforming
types of
visualizing
data infrastructure, state of
Data Moves Me, LLC
data science
business understanding of
hierarchy of needs
interview with Robert Chang
overview of
databases
mathematics
programming
statistics
“Data science foundations” (Au)
data science managers, tasks of
data science methods, on résumés
data storage
databases
data types
questions about during interviews, 2nd
skills for working with
types of joins
data-entry work
datahelpers.org
datasets, in the news
dbplyr package
deadlines
decision scientists
making analyses
production for
degrees
applying for academic programs
choosing school
on résumés
requirements for.
See also graduate programs.
Demystifying Public Speaking (Hogan)
Design of Everyday Things, The (Norman)
Design of Web APIs, The (Lauret)
Designing Your Life (Burnett and Evans)
DevOps (development operations) team
Dice
Difficult Conversations (Stone, et al.)
“Do you have time for a quick chat?” (Causey)
Docker containers
dockerfiles
documentation
about harassment
for APIs
for open source, contributing to
for stakeholders
making before leaving company
of failed projects
turning into blog posts
Doing Data Science (O’Neil and Schutt)
E
early-stage startups
pros and cons of
team culture
tech stack
e-commerce companies
failed project example
questions asked by during interviews
education, on résumés
email
about harassment
addresses of, on résumés
for communities
receiving details of job offers via
with stakeholders
E-Myth Revisited, The (Gerber)
engineering stakeholders
enterprise servers
Entry-Level positions
Equal Employment Opportunity Commission
ESOPs (employee stock purchase plans)
estimating
Evans, Dave
Excel
for sharing analyses
on résumé
expectations
from supervisors, unrealistic
setting
when you’re first data scientist at a company
experience, on résumés
experimental design
experts, asking questions to
exploring data
checking in with stakeholders
creating summary tables
maintaining focus
one-button run
plots for exploration vs plots for sharing
simple methods for
summarizing data
transforming data
visualizing data
F
Facebook’s facial-recognition model
failed projects
communicating with stakeholders
documentation of
examples of, 2nd
interview with Keim, Michelle
managing risk
negative emotions caused by
pivoting
reasons for
terminating
Farnell, Elin
features, selecting
feedback
from peers
whether part of company culture
final leadership interviews
first months on job
as first data scientist
at large organizations
at small companies
becoming productive
asking questions
building relationships
interview with Jarvis Miller
reviewing data
setting expectations
when job is not as promised
deciding to leave
toxic work environments
work is terrible
Fisher, Roger
FiveThirtyEight website
FizzBuzz task
Flask package
focus groups
Fournier, Camille
freedom/lack of freedom
at early-stage startups
at established retailers
at giant government contractors
at late-stage, successful tech startups
comparison between types of companies
freelancing
Fundamentals of Data Visualization (Wilke)
G
Galarnyk, Michael
GCP (Google Cloud Platform), 2nd
Geewax, JJ
Gerber, Michael E.
Getting to Yes (Fisher, Ury and Patton)
Getting What You Came For (Peters)
Ghor, Imran
Git, 2nd
GitHub, 2nd, 3rd
GitHub repositories
contributing to code in
of data scientists who could make referrals
organization of
showing work via
goals, for career
Godset, Brian
Goodman, Jason
Google Analytics, on résumé
Google Cloud Platform in Action (Geewax)
Google Cloud team
government contractors
learning
promotions
pros and cons of
raises
security
SQL Server databases
team culture
tech stack
government open data
graduate programs
admission requirements for
advisor for thesis/dissertation
after working in data science
connections with business in area
disadvantages of
funding for
jobs resulting from
location of
online
prestige of school
quantify of project work in
topics covered by
GRE (Graduate Record Examination) scores
Grove, Andrew S.
Guo, Philip
H
hackathons
Hallgrímsson, Hlynur
harassment
Hay, Chris
headers, on résumés
health
hierarchy of data science needs
High Output Management (Grove)
Hogan, Lara
honors, on résumés
“How to ask for a promotion” (Knight)
“How to Build a Data Science Portfolio” (Galarnyk)
“How to quantify your resume bullets” (Zhang)
“How to work with stakeholders as a data scientist” (Barrows)
“How to write a cover letter” (Muse Editor)
“How women can get what they want in a negotiation” (de Janasz and Cabrera)
HTTP protocol
human element
hype cycle
I
I Will Teach You to be Rich, 2nd ed. (Sethi)
icons, on résumés
IELTS (International English Language Testing System)
images, Docker
impostor syndrome, 2nd, 3rd, 4th, 5th
independent consulting
benefits of
drawbacks of
how to do
industry
differences in work depending on
transition from academia to
input, consistency in
interest-based negotiation
internal bargaining
interns
interpersonal skills
interviews
answering why you left a job
asking questions of interviewers
attire at
Au, Randy
Bassa, Angela
Casari, Amanda
Chang, Robert
following up after
Kehrer, Kristen
Keim, Michelle
location of
Madubuonwu, Brooke Watson
managing nerves during
Miller, Jarvis
Mostipak, Jesse
Nolis, Heather
Parker, Hilary
preparation for, importance of
reality of job vs.
Robinson, David
scheduling
Silge, Julia
Snowden-Akintunde, Sade
Teate, Renee
Williams, Ryan
Introduction to Empirical Bayes (Robinson)
J
Janasz, Suzanne de
job boards, applying on
job interviews, 2nd, 3rd
behavioral questions during, 2nd
data surprises
difficult problems
disagreements with teammates
previous job reflections
project that had most impact
senior person making mistake based on data
brain teasers during
combinatorics
estimation
case studies during
coding questions during
FizzBuzz
frequently used library
frequently used package
Jupyter Notebooks
manipulating data in R/Python
prime numbers
reusing code
RMarkdown
technology decisions
working with Git
database questions during
data types
types of joins
final interviews
identifying what companies want
initial phone screen interviews
interview with Ryan Williams
machine learning questions during
boosting
confusion matrix
deploying new models
experimental design
favorite algorithm
feature selection
flaws in experimental design
model behavior
training vs. test data
on-site interviews
process of
receiving job offers
SQL questions during
example SQL queries
loading data into SQL
statistics questions during
bias in sampled data
explain p-value
interpreting regression models
statistics terms
technical interviews
job market
competition in
whether bubble in
job offers
choosing between
interview with Madubuonwu, Brooke Watson
negotiating
by women
if unemployed
limits of
parameters for
tactics for
process of
receiving, 2nd
time frame for accepting
job security
at early-stage startups
comparison between types of companies
job titles
jobs
applying for, 2nd
cover letters
referrals
résumés
finding new employment while employed
interviewing for
past, on résumés
searching for, 2nd
attending meetups
decoding descriptions
different after first job
pitfalls
setting expectations
social media for
joins, types of
junior data scientist, defined
Junior positions
Jupyter Notebooks, 2nd
K
Kaggle, 2nd
Kehrer, Kristen, 2nd
Keim, Michelle
keywords, in résumés
Knight, Rebecca
KPIs (Key Performance Indicators)
L
Lamott, Anne
Lander, Jared
large organizations, 2nd
lasso regression
late-stage startups
pros and cons of
team culture
tech used by
Lauret, Arnaud
leaders in data science organization, 2nd
leadership interviews
learning
at conferences
at early-stage startups
at giant government contractors
at late-stage, successful tech startups
by self-teaching
comparison between types of companies
from bad jobs
from mentors
necessity of
to manage
via books
whether part of company culture.
See also skills.
letters of recommendation, for graduate program
libraries
creating in open source
frequently used
linear regressions, 2nd
LinkedIn, 2nd
bootcamp graduates on
referrals via
setting to show openess to recruiters
load testing
Locke, Steph
logging
logistics, questions asked about during interviews
logos, for communities
long-form analyses
long-term projects
loyalty program analysis example
M
machine learning
interview with Nolis, Heather
questions during job interviews, 2nd
boosting
confusion matrix
deploying new models
favorite algorithm
feature selection
model behavior
training vs. test data
taught at bootcamps
machine learning engineers
experience of
making analyses
production for
Madubuonwu, Brooke Watson
management role
benefits of
drawbacks of
how to obtain
earning promotions within company
growing new teams
with new company
Manager’s Path, The (Fournier)
managers
as stakeholders
resolving issues with
master résumés
master’s degree programs
mathematics
McDowell, Gayle Laakmann
median
meetings
listening during
to define expectations
to discuss priorities
with people you’ve never talked to
with stakeholders, 2nd
meetups
R-Ladies
speaking at
Meloon, Mark
mentorship, 2nd
Microsoft Azure.
See Azure.
Microsoft Excel.
See Excel.
Miller, Jarvis
Minto, Barbara
Mock, Thomas
mode
models
behavior of
building
checking in with stakeholders
creating
creating summary tables
creating, person responsible for
deploying, 2nd
API maintenance
creating production systems
interview with Nolis, Heather
load testing
overview of
highest-performing
maintaining focus
one-button run
putting into production
regression models
retraining
serving with APIs
simple methods for
summarizing data
transforming data
visualizing data
monitoring systems
MOOCs (massive open online courses)
Moore, Nathan
Mostipak, Jesse
Mothers of Data Science (Kehrer)
Mount, John
moving allowances
MTCs (Massive Tech Companies)
bureaucracy
job security
layoffs
machine learning
résumés and
tech stack
multiple virtual machines
MVP (minimum viable product)
Mwiti, Derrick
N
Neff, Kristin
negotiating job offers
by women
if unemployed
limits of
parameters for
tactics for
Netflix Prize
networking
at conferences
growing network through community
in bootcamps
interview with Mostipak, Jesse
interview with Teate, Renee
neural networks, 2nd
New Grad positions
Node.js, 2nd
Nolis, Heather, 2nd, 3rd, 4th
Norman, Don
not-for-profit organizations
conferences and
negotiating with
questions asked by during interviews
NumFOCUS
O
O’Neil, Cathy
offers.
See job offers.
OKRs (Objective Key Results)
one-button analysis
one-more-thing changes
one-year cliff
online bootcamps
online courses, 2nd
online graduate programs
on-site interviews, 2nd
on-the-job education
open source
contributing to existing work
creating libraries
creating packages
documentation for, contributing to
OpenAPI documents
output, consistency in
“Overcoming social anxiety to attend user groups” (Locke)
overscoping
P
packages
creating in open source
frequently used
Pandas for Everyone (Chen)
Parker, Hilary
Patton, Bruce
PCA (Principal Components Analysis)
performance goals
performance reviews
performance, how evaluated
permutations
personas
Peters, Robert L.
PhDs
phone calls, with stakeholders
phone screening
pivoting
plots
for exploration
for sharing
plumber package, 2nd
POCIT
portfolios
blogging and
logistics of
potential topics for
building
creating projects
finding data for
GitHub README files
example projects
interview with Robinson, David
training neural networks
PowerPoint, 2nd
Practical Data Science with R (Zumel and Mount)
presentations
before leaving company
book about
Prince, Brian H.
principal data scientist
benefits of
drawbacks of
how to become
priorities, 2nd
product analysts
production
creating systems for
building APIs
building models
collecting data
deploying APIs
documentation for APIs
serving models with APIs
testing
deploying models to
API maintenance
interview with Nolis, Heather
load testing
overview of
productivity
asking questions
building relationships
professors, letters of recommendation from
programming
community contributions to
flexibility of
questions about during interviews
reproducibility of
skills in, on résumés
taught at bootcamps
programming languages
projects, 2nd
failure of
communicating with stakeholders
documentation of
interview with Michelle Keim
managing risk
negative emotions caused by
pivoting
reasons for
finding data for
finishing
GitHub README files
long-term
terminating
promotions
asking for
at established retailers
at giant government contractors
to management
proofreading, of résumés
public speaking
opportunities for
preparing.
See also talks.
publications on résumés
p-value
Pyramid Principle, The (Minto)
Python programming language, 2nd
as job requirement
building API using
contributing to
questions about during interviews
Python Software Foundation
Q
quality control
quantitative analysts
quants
queries in SQL
questions
about stakeholders’ goals
about why you left previous job
answered in analysis
asking of interviewers
asking to stakeholders
criticisms veiled as
receiving before analyses
Qureshi, Haseeb
R
R for Everyone (Lander)
R Foundation
“R in Production” (Nolis and Nolis)
R Markdown, 2nd
R Packages, 2nd ed (Bryan and Hadley)
R programming language, 2nd
as job requirement
book about
building API using
conference for
contributing to
example project using
questions about during interviews
R/Python
R&D (research and development) projects
raises, at giant government contractors
random forests
randomness
README files
reasonableness
recommendations, person responsable for giving
recruiting, at conferences
referrals, blogs of data scientists who could make
regression models
regressions
rejection, when job-searching
relationships.
See also networking.
reports
data analysts and
written by other employees, learning from
repos.
See GitHub repositories.
reproducibility of programming
requests, turning into dialogues
research analysts
research scientists
resigning
Resilient Management (Hogan)
REST APIs (web services)
résumés
course on writing
editing
generating
interview with Kehrer, Kristen
management skills on
structure of
contact information
data science projects
education
experience
publications
skills
tailoring
uploading to job boards
when to work on
retraining models
reusing code
risk, managing
R-Ladies
Robinson, David, 2nd, 3rd
Rogati, Monica
Rossum, Guido van
rstudio::conf
RSUs (restricted stock units)
rtweet for R
S
salary
at early-stage startups
at established retailers
comparison between types of companies
discussing during interviews
negotiating
performance reviews and
sampled data, bias in
Schutt, Rachel
screening, by phone
Seckington, Melinda
selection bias
self identity
Self-Compassion (Neff)
self-contained analysis
self-deprecation
self-taught skills
self-teaching
senior data scientist, defined
senior roles, promotion to
server, defined
Sethi, Ramit
Shaikh, Reshama
sharing
analysis
work via Twitter
signal, lacking in data
signing bonus
Silge, Julia, 2nd, 3rd, 4th
skills
acquiring
education route
in bootcamps
interview with Silge, Julia
on-the-job
self-taught
through community
with data science degree
interviews and
on résumés
soft skills, on résumés.
See also learning.
small companies, 2nd
snapshots
Snowden-Akintunde, Sade
social anxiety
social media, 2nd
soft skills, on résumés
Software Requirements (Weigers and Betty), 2nd
Spahn, Emily
speaking up
sponsorship
sprints, open source
SQL questions, during job interviews, 2nd
example SQL queries
loading data into SQL
Stack Overflow
stakeholders
asking questions about during interviews
checking in with, 2nd
communication with
importance of
defined
documentation for
interview with Snowden-Akintunde, Sade
meeting
motivations of
presenting analyses to
prioritizing tasks from
types of
business stakeholders
corporate leadership
engineering stakeholders
managers
understanding goals of
when project doesn’t provide value to
working with
being consistent
communicating constantly
writing for
Stamm, Rob
STAR (situation, task, approach, result) approach
startups
early-stage
pros and cons of
team culture
tech stack
late-stage
pros and cons of
team culture
tech stack
statistics
bias in sampled data
interpreting regression models
p-value
questions during job interviews, 2nd
taught at bootcamps
terms
stock options, 2nd, 3rd
Stone, Douglas
success, defining
summarizing data
summary tables
Survey Monkey
surveys
survivorship bias
Switchup
systems, monitoring
T
tables
talks
inviting others to
recorded
reusing
time frame for
teaching
by managing
via blogs
teams
being introduced to
collaboration problems
Teate, Renee, 2nd
tech companies
pros and cons of
team culture
tech used by
Tech Ladies
technical degree
technical interviews
technical screening, not passing
tedious work
telemetry
“Ten rules for negotiating a job offer” (Qureshi)
terminating
employment
giving notice
interview with Amanda Casari
when to leave
failing projects
test data
testing
in user experience research
load testing
machine learning models
Text Mining with R (Silge and Robinson), 2nd
text, adding to shared analyses
Think Like a Data Scientist (Godset)
“Thinking of Blogging about Data Science?” (Mwiti)
tidytext package in R
TidyTuesday program
titles
TOEFL (Test of English as a Foreign Language)
tours of workplace
toxic work environments
training data
transforming data
treasure hunter metaphor
trends impacting data science
Tufte, Eduard
tutorials, 2nd
Twitter
benefits of
getting help on
learning via
referrals and
U
undergraduate degree
unicorns
“Up-level your résumé” (Kehrer)
Ury, William L.
user experience research
user stories
V
Van Rossum, Guido
Venn diagram, Conway’s
version control
virtual machines
visualizations, 2nd
voluntary response bias
W
web scraping
websites
applying on
creating for blogs
data collection from
for communities
personal
Weigers, Karl
weird data, 2nd
What Color Is Your Parachute? (Bolles)
“What You Need to Know before Considering a PhD” (Thomas)
“What’s the Difference between Data Science, Machine Learning, and Artificial Intelligence?” (Robinson)
whitespace, on résumés
“Whose critical path are you on?” (Guo)
Wickham, Hadley, 2nd
Wiegers, Karl
Wilke, Claus O.
Williams, Ryan, 2nd
Wittig, Andreas
Wittig, Michael
women, negotiating as, 2nd
Word
work spaces
work–life balance, in academia vs. industry
writing, book about