STATE OFDATASCIENCE
PAVING THE WAY FOR INNOVATIONEXECUTIVE SUMMARY
This year, we conducted our State of Data Science survey to gather
demographic information about our community, ascertain howthat community works, and collect insights into big questions andtrends that are top of mind within the community.
As the impacts of COVID continue to linger and assimilate into
our new normal, we decided to move away from covering COVID
themes in our report and instead focus on more actionable issues
within the data science, machine learning (ML), and artificial
intelligence (AI) industries, like open-source security, the talentdilemma, ethics and bias, and more.

In the spirit of democratizing data, we are making
the raw data from our 2022 State of Data Sciencesurvey available to the public via Anaconda Nucleus.

2022 STATE OF DATA SCIENCE REPORTTABLE OF CONTENTS
01 / METHODOLOGY
02 / THE FACE OF DATA SCIENCE
08 / DATA PROFESSIONALS AT WORK
21 / ENTERPRISE ADOPTION OF OPEN SOURCE
28 / POPULARITY OF PYTHON
31 / DATA JOBS AND THE FUTURE OF WORK
37 / BIG QUESTIONS AND TRENDS
43 / KEY TAKEAWAYS AND REFLECTIONS

2022 STATE OF DATA SCIENCE REPORTMETHODOLOGY
3,493 individuals from 133 countries and regions took part in our online
survey conducted from April 25, 2022, to May 14, 2022. Respondents camefrom the Anaconda email database, Anaconda.org, social media, and othersources. They had the opportunity to participate in a sweepstakes drawing
as an incentive for completing the survey, and five winners were selected
at random after the survey was complete. The respondents were divided
into three separate tracks: students, academics, and those working incommercial environments. Each of these different cohorts was asked
some universal questions, while some questions were unique to eachcohort’s experience. In the report, we indicate whether responses camefrom the entire set of respondents or from a subset.

Note: All percentages are rounded to the nearest whole percent. Due to rounding,some numbers may not equal 100.

2022 STATE OF DATA SCIENCE REPORT

01

THE FACE OFDATA SCIENCE
As with years past, we began our survey with a series of demographic
questions. Our respondents span a vast range of geographical locations,ages, and job functions, and capturing their demographic info each year
offers insight into how the data science community is evolving.

2022 STATE OF DATA SCIENCE REPORT

02

THE FACE OF DATA SCIENCE

3,493 individuals from 133 countries and regions took part in our 2022 survey.

North America
EU/Central Asia

15%
South Asia

40%

Latin America

7%3%Sub-Saharan Africa

12%East Asia/Pacific

8%Australia/New Zealand

12%

Middle East/North Africa

3%

n = 3,493

2022 STATE OF DATA SCIENCE REPORT

03

THE FACE OF DATA SCIENCE

Respondent Age
7%

58-67
Boomers

Respondent Gender

4%

68+
Boomers+

19%
18-25
Gen Z

2%

76%

23%

Non-binary

Man

42-57
Gen X

23%
Woman

47%

26-41
Millennials
n = 3,493

n = 3,493

Our respondent set skews toward younger generations. Of the 3,493
respondents, 66.54% are either Generation Z (19.47%) or Millennials (47.07%).
We saw an increase in Generation X respondents by about 5% compared to2021. Only 10.48% of respondents are 58 or older.

The gender data from our respondents supports what the community
continues to see across male-dominated STEM fields. The industry as awhole demonstrates room for improvement when it comes to increasing
gender diversity.

2022 STATE OF DATA SCIENCE REPORT

04

THE FACE OF DATA SCIENCE

Respondent Education Level
1%

6%

12%

35%

Respondent Company Type
33%

13%

57%

20%

11%

Commercial(for-proﬁt) entity

Educational
institution

Govern
agen

20%

11%

11%

The majority of our survey respondents are well-educated.
80.71% have at Educational
Commercial
least a college-level degree, and there was(for-proﬁt)
about a 12%
year-over-year (YoY) institution
entityincrease in the number of respondents who hold advanced degrees. 19.29%
of respondents do not hold any degree.

Government
agencyNot-for-proﬁtorganization

No
degreeHigh school
diploma or
equivalentSomecollege

57%

Bachelor’s
degreeMaster’s
degreeDoctorate
degreen = 3,493

2022 STATE OF DATA SCIENCE REPORTn = 2,924

05

THE FACE OF DATA SCIENCE

Respondent Primary Job Function
The current professional landscape includes a wide variety
of data-focused roles, and there is often overlap between
job functions—and in some cases, individual titles apply tomultiple tasks. With that in mind, we asked respondents toselect the role that best reflects their job function. 16.46%
of respondents are data scientists. More data scientists(+5%) and fewer students (-13%) responded this year
compared to 2021.

BUSINESSPROFESSIONAL

Business analyst
Product manager
Line-of-business manager

DATA SCIENCE

Data scientist
Data engineer
ML engineer

EDUCATION

Student
Professor/instructor/researcher

OPERATIONSOTHERSCIENTISTS

8%
4%
3%
16%
8%
2%
14%
9%

Cloud engineer

3%
1%

DevOps1%

Cloud security manager

1%

CloudOps

<1%

MLOps

<1%

Other

11%

Developer

8%

Research scientist

8%

Applied scientist

3%

System administrator

n = 3,493

2022 STATE OF DATA SCIENCE REPORT

06

THE FACE OF DATA SCIENCE

Respondent Current Job Level

17%

Most (33.93%) of survey respondents hold senior-level
positions, a 9% increase from 2021. The percentage of
respondents who hold entry-level positions has decreased
by 5%, and the percentage of respondents who hold a VPlevel or C-suite position has decreased by about 7%.

Associate

4%

C-Suite/Owner

7%

Director

2%
VP

10%

22%

Entry Level

Manager5%

Principal

34%
Seniorn = 1,966

2022 STATE OF DATA SCIENCE REPORT07

DATA PROFESSIONALSAT WORK
Most of our respondents work in commercial environments.
We took a deeper dive into their responses to ascertain wheredata professionals sit within their organizations, how they spend
their time, what tools they use, and their most significant challenges.

2022 STATE OF DATA SCIENCE REPORT08

DATA PROFESSIONALS AT WORK

Respondent Industry
Technology

11%

Defense3%

Finance

9%

Professional services

3%

Consulting

8%

Telecommunications

3%

Healthcare

5%

Insurance

3%

Automotive

5%

Media

2%

Government

5%Transportation and logistics

2%

Research and development (R&D) 5%

Agriculture2%

Electronics

5%

Food and beverage

2%

Manufacturing

5%

Other

1%

Engineering

5%

Nonproﬁt

1%Construction

4%

Pharmaceutical

1%

Energy4%Entertainment

1%

Education

3%

Hospitality

1%

Retail3%

Utilities

1%

Companies across awide variety of sectors—
from government totelecommunications
to nonprofit topharmaceutical—relyon data-driven roles.
The top five industries
represented in our survey
are technology, finance,
consulting, healthcare,
and automotive.

n = 1,966

2022 STATE OF DATA SCIENCE REPORT09

DATA PROFESSIONALS AT WORK

Company Size

29%
1-200

26%
201-1,000

15%
1,001-3,000

8%3,001-5,000

55.14%of respondents work forcompanies with 1,000
employees or less.

7%
5,001-10,000

15%
10,001+n = 1,966

2022 STATE OF DATA SCIENCE REPORT

10DATA PROFESSIONALS AT WORK

What department does your role fall under?

24%

23% 18% 15%

11%

5%

4%

Other

Finance

Center of
Excellence

Where do data roles fall within organizations? The short answer
is: everywhere. The longer answer is, it depends on the specificorganization. Sometimes there is an entire data-focused team;other times, data scientists work within other departments likefinance or even marketing.
Most of our commercial respondents (24.47%) work within adata science department, while 22.89% work in R&D and 18.01%
work in information technology (IT).

Data
science

R&D

IT

Operationsn = 1,966

2022 STATE OF DATA SCIENCE REPORT

11

DATA PROFESSIONALS AT WORKHow do data scientists spend their time?
Data professionals spend their time on a variety of tasks that require
diverse technical and non-technical skills. Respondents indicated they
spend about 37.75% of their time on data preparation and cleansing.
Beyond preparing and cleaning data, interpreting results remains
critical. Data visualization (12.99%) and demonstrating data’s value
through reporting and presentation (16.20%) are essential steps towardmaking data actionable and providing answers to critical questions.
Working with models through selection, training, and deployment takes
about 26.44% of respondents’ time (-8.56% YoY).

Data preparation

22%
16%

Reporting andpresentation

16%
13%

Model selection

Deploying models

Data cleansing

9%
9%
9%
7%

Data visualization

Model trainingOthern = 1,966
We asked our respondents how much time they spend on the above tasks, and for each item they entered
a number reflecting the percentage of time spent relative to the other options. This is the average of thereported percentages.

2022 STATE OF DATA SCIENCE REPORT

12DATA PROFESSIONALS AT WORK

Data Science and ML Measures and Tools
Which of the following tools are being used within your organization?
Given our survey sample, it’s no surprise that 46.83% of commercial respondents indicated their organizations currently use Anaconda. Other popular toolsthat organizations are currently using include GitHub (44.94%), RStudio* (33.33%), Stack Overflow (31.57%), and Tableau (30.65%).

15%

10%

11%

17%

12%

16%

20%

21%

9%

9%

15%

47%

6%

8%

10%
20%

18%

18%

23%

10%

10%

10%

19%

45%

22%

24%

24%

24%

24%

8%

17%

16%

15%

18%

17%

35%

33%

15%

19%

26%

27%
25%

21%

28%
25%

21%

17%

27%

24%

23%

22%

27%

22%

22%
24%

24%
18%

19%
22%

25%

24%

20%

16%

14%

17%

26%

26%

25%

21%

27%
22%

21%

6%

17%
11%

24%

26%

17%

23%

23%

25%

25%

17%

10%

18%

17%

21%

24%

24%

26%

26%

27%
19%

15%

7%
16%

22%

18%

11%
24%

17%

24%

28%

21%

16%

67%

19%
22%

26%

31%

16%

23%

17%

32%
19%

27%

28%

11%

15%

18%

19%
23%

23%

8%

20%

20%

23%

13%

25%

16%

16%

11%

18%

25%

24%

25%

26%

26%

22%

11%
24%

24%
14%

23%

14%

21%

7%
14%

12%

16%

16%

9%

22%

15%
2%

AWS
Alteryx Anaconda Azure ML Cloudera
Sagemaker
StudioCDSWCurrently using

Comet Databricks Dataiku DataRobot Domino Explorium GitHub Google AI Google Google
Platform BigQuery ColabPlan to useInterested inI’m not sure

H20 IBM Watson JFrog
KNIME
Studio Artifactory

Looker

MLFlow

Nexus
PowerBI
Sonotype

RStudio RapidMinerSAS

Snowﬂake

Stack
Tableau
Overﬂow

Weights& Biases

Other

Not interested in*Note that after our survey concluded, RStudio was rebranded as Posit.
n = 1,373

2022 STATE OF DATA SCIENCE REPORT

13

DATA PROFESSIONALS AT WORK

Data Science and ML Measures and Tools
What kinds of measures or tools do you or your institution use
to ensure fairness and mitigate bias in data sets and models?
In our 2021 State of Data Science report, about 40% of survey
respondents indicated that their organizations had implemented
or planned to implement steps to ensure fairness and mitigate
bias over the following 12 months.

31%

25%

24%

We evaluate data collection
methods according
to internally set standards

We manually assess data
sets for fairness and bias

We do not have standards
for fairness and bias
mitigation in data sets andmodels/None currently

19%

15%

15%

We perform a suite ofstatistical fairness tests

We have a
center of excellence

I’m not sureThis year, we wanted to dig into the specific steps organizations are
now taking to ensure fairness and mitigate bias. The most common
step is evaluating data collection methods according to internallyset standards (30.61%), with the second-most-common step being
manually assessing data sets for fairness and bias (24.84%). 23.64%
of respondents indicated that their organizations do not have standards
surrounding/have not implemented measures or tools to address
fairness and bias mitigation in data sets and models, and 14.89% aren’t
sure about their organizations’ efforts.

n = 1,578

2022 STATE OF DATA SCIENCE REPORT

14

DATA PROFESSIONALS AT WORK

Data Science and ML Measures and Tools
What kinds of measures or tools do you or your institution use
to address model explainability and interpretability?
In our 2021 State of Data Science report, about 41% of survey
respondents indicated that their organizations had implemented
or planned to implement steps to address model explainability overthe following 12 months.

35%

30%

28%

We perform a series ofcontrolled tests to assess
model interpretability

Model outcomes must be
applicable to all related groups
and treatments in test sample(i.e., no cherry picking data)

We only use
low-interpretability models
in low-risk scenarios

28%

24%

16%

We use statisticalinferential tests toassess variable ﬁdelity

We do not currently use
any measures or tools toensure model explainability
or interpretability

I’m not sureThis year, we wanted to dig into the specific steps organizations
are now taking to address model explainability. The most common
step is performing a series of controlled tests to assess modelinterpretability (35.36%), with the second-most-common step being
ensuring model outcomes are applicable to all related groups andtreatments in test samples (i.e., no cherry picking data) (30.23%).
23.76% of respondents indicated that their organizations do not use
any measures or tools to ensure model explainability or interpretability,and 16.41% aren’t sure about their organizations’ efforts.

n = 1,578

2022 STATE OF DATA SCIENCE REPORT

15

DATA PROFESSIONALS AT WORK

Getting to Production

Where do you deploy models into production?

Organizations that deploy ML models and other data science
outputs to power business functions and products enjoy acompetitive advantage that leans on the value data scientists
provide. While getting models to production can be one of
the most rewarding functions of a data professional, it can alsopresent unique challenges.

Only 23.70% of commercial respondents are not deploying models into
production, which means the majority (69.20%) are deploying models into
production (7.10% aren’t sure), typically via an on-premises local server(41.32%) or the cloud (27.88%).

24%

41%

I do not deploy
models into
production

On-premises
local server

7%

I'm not sure28%

In the cloudn = 1,578

2022 STATE OF DATA SCIENCE REPORT

16

DATA PROFESSIONALS AT WORK

Getting to Production
What roadblocks do you face when moving
models to a production environment?
Most (41.32% of) commercial respondents deploy models
into production via an on-premises local server. The top
roadblocks respondents face when moving models to
a production environment include meeting IT/InfoSec
standards (33.88%), securing data connectivity (28.45%),and re-coding models from Python/R to anotherlanguage (26.12%).

34%
Meeting IT/InfoSec standards

28%
Securing data connectivity

Re-coding models from Python/R to another language

26%
25%

Managing package dependencies and environments

24%
Access to compute resources

24%
Securing network connectivity

22%
Re-coding models from another language to Python/R

22%
A skills gap in my organization (data engineering, Docker, Kubernetes, etc.)

19%
Moving to the cloud19%
We do not deploy models in production

9%Model decay or data driftn = 1,455
2022 STATE OF DATA SCIENCE REPORT

17

DATA PROFESSIONALS AT WORK

Skills Gaps and Continued Learning
In your opinion, what are the most important skills/areas of expertise missing in the data science/ML area of your organization?

ANALYTICS

BUSINESS SKILLSCOMPUTING & ENGINEERING

DATA

MATH

n = 1,443
2022 STATE OF DATA SCIENCE REPORT

18

DATA PROFESSIONALS AT WORK

Skills Gaps and Continued Learning
In your opinion, what are the top five mostimportant skills or areas of expertise?
According to respondents, the top five most important skills/areas ofexpertise missing in the data science/ML areas of their organizations
are engineering skills (38.12%), probability and statistics (33.26%), businessknowledge (32.22%), communication skills (30.56%), and big data
management (29.24%). But are educational institutions teaching theseskills, and are students learning them? Let’s take a look:

50% 47%26%

32% 29%Businessknowledge

Communication
skills24%

 Educator respondents:
What topics, tools, or skills is yourinstitution teaching students of data
science and machine learning?

35%

29%

Engineeringskills

28% 29%Probability andstatistics

Big data
management

There seems to be a fairly close correlation between
the number of educational institutions teachingengineering skills, probability and statistics, businessknowledge, communication skills, and big data
management and the number of students learning
about these topics. Of the topics, probability andstatistics are covered and learned most frequently,
while business knowledge is covered and learned
least frequently. Overall, there is opportunity for bothteachers and students alike to place greater emphasison these skills in order to better prepare students toenter the workforce.

 Student respondents:
What topics, tools, or skills are covered
in your courses in preparation forentering the data science/ML ﬁeld?

n = 517 and n = 407

2022 STATE OF DATA SCIENCE REPORT19

DATA PROFESSIONALS AT WORK

What tools and resources do you feel are lacking for data
scientists who want to learn and develop their skills?

How do you typically learn about new tools
and topics relevant to your role?

Tailored learning paths (51.30%), hands-on projects (48.33%), and mentorship
opportunities (47.63%) are the top tools and resources respondents feel arelacking for data scientists who want to learn and develop their skills.

Most respondents typically learn about new tools and topics relevant to theirroles by reading technical books, blogs, newsletters, and papers (68.25%) or via
free video content (67.97%).

51%

48%

48%

37%

Tailoredlearning paths

Hands-onprojects

Mentorship
opportunities

Otherbooks,
Community
Reading technicalengagement andblogs, newsletters,
learning platforms
and papers

3%
68%

68%

48%

44%

Free video content(e.g. YouTube)

Paid online courses(e.g. Coursera, EdX)

Community platforms
and forums

48%

37%

3%
68%

68%

48%

44%

24%

3%

ntorshiportunities

Community
engagement andlearning platforms

Other books,
Reading technicalblogs, newsletters,
and papers

Free video content(e.g. YouTube)

Paid online courses(e.g. Coursera, EdX)

Community platforms
and forums

Paid university
coursesOthern = 2,1542022 STATE OF DATA SCIENCE REPORTn = 2,154

20

Pa

ENTERPRISE ADOPTION
OF OPEN SOURCE
Using and contributing to open-source software (OSS) sets the mostinnovative organizations apart and often saves them significant time andresources. As opposed to purchasing tools from a vendor or building themin-house, the crowdsourced OSS model leverages multiple minds at workto accelerate projects that could otherwise take years.

2022 STATE OF DATA SCIENCE REPORT21

ENTERPRISE ADOPTION OF OPEN SOURCE

Does your organization allow theuse of open-source software?

8%No

How does your employer empower you and your team
to contribute to open source?

5%
Not sure

16%

Not sure32%
No

In 2021, 65% of commercial respondents said their teams were encouraged to contributeto open-source projects, and the majority of those respondents (54%) said that theiremployers were empowering them to contribute to open source through an increase infunding related to open-source project development.
This year, just 51.99% of commercial respondents said their teams are encouraged tocontribute to open-source projects—about a 13% YoY decrease, perhaps due to security
concerns. The
majority of these respondents (54.04%) said that their employers are52%
empowering Yes
them to contribute to open source through additional time dedicated tocontributing to open-source projects.

87%
Yes

n = 1,966

Does your employer encourage you and yourteam to contribute to open-source projects?

16%

54%
Additional time dedicated to contributing to open-source projects

51%
Funding speciﬁc to open-source project development

Not sure

37%
Team members dedicated to contributing to open-source projects

32%
No

87%
Yes

52%
Yes

4%
Other
n = 890n = 1,966

2022 STATE OF DATA SCIENCE REPORT22

ENTERPRISE ADOPTION OF OPEN SOURCE

How much control do IT users
have over the open-source tools
and packages your company uses?

33%
IT controls most company open-source tools and packages

23%
IT controls all company open-source tools and packages

18%
IT controls about 50% of company open-source tools and packages

15%
IT controls minimal company open-source tools and packages

Within the subset of commercial respondents
whose organizations allow the use of open-source
software, 88.99% indicated that IT controls company
open-source tools and packages to some extent,with 56.49% indicating that IT controls most or allcompany open-source tools and packages.

11%
IT has no oversight of company open-source tools and packages
n = 1,526

2022 STATE OF DATA SCIENCE REPORT23

ENTERPRISE ADOPTION OF OPEN SOURCE

Securing the Software Supply Chain
and Open-Source Pipeline

How does your organization ensure its open-source supply
chains are secure and meet enterprise security standards?

Like any software, open source comes with inherent security challenges.
Multiple individuals are often involved in OSS creation, maintenance, andevolution, which can increase risk. On the bright side, it can also allow forvulnerabilities to be caught and patched more quickly.

40%

33%

27%

24%

It’s certainly possible to reap the benefits of open-source software while
being mindful of pipeline management and security. In this section, we’ll
look at practices and sentiments surrounding software supply chain andopen-source security.

Vulnerability andsecurity scanningsoftwareWe create and use
custom andproprietary software

Manual model andapplication audits

We only use
source-veriﬁedpackages

24%

15%

9%

I’m not sure

We have anduse SBOMs

None currently

When asked about how organizations that use OSS ensure their supply chainsare secure and meet enterprise security standards, 40.39% of respondents
say they use vulnerability and security scanning software, 32.76% create anduse custom and proprietary software, and 27.48% do manual model andapplication audits. Only 8.70% are not securing their open-source supply
chains, and 23.69% aren’t sure.

n = 1,874

2022 STATE OF DATA SCIENCE REPORT24

ENTERPRISE ADOPTION OF OPEN SOURCE

Securing the Software Supply Chain
and Open-Source Pipeline
Securing the Open-Source Pipeline
When asked about how organizations that use OSS ensure
their data science and ML packages are secure and meetenterprise security standards, 43.03% of respondents saythey use a managed repository, 35.76% use a vulnerability
scanner (+5.76% YoY), and 34.13% do manual checks against
a vulnerability database. 19.17% are not securing their opensource pipelines (-5.83% YoY), and 23.11% aren’t sure.

43%

36%

34%

We use a managed
repository

We use avulnerability scanner

Manual checks against
a vulnerability database

19%

23%

We are not securing ouropen-source pipeline

I don’t know if we’resecurely downloading
open-source packages

n = 1,471

2022 STATE OF DATA SCIENCE REPORT

25

ENTERPRISE ADOPTION OF OPEN SOURCE

Securing the Software Supply Chain
and Open-Source Pipeline
What roadblocks are preventing your organizationfrom leveraging open source?

54%
Fear of vulnerabilities, potential exposures, or risks

38%
Lack of knowledge or understanding

29%
Organization doesn’t have strong IT governance in place

28%
Open-source software is deemed insecure, so it’s not allowed

26%
Not wanting to disrupt current projects in place

According to our respondents, the majorityof organizations are using open-source
software. But of the 7.73% of respondents whoseorganizations are not, the biggest reason why
is fear of vulnerabilities, potential exposures,or risks (54.48%) (+13.48 YoY).
Earlier, we called out the approximate 13%
YoY decrease in the number of commercial
respondents who said their teams areencouraged to contribute to open-source
projects. It’s possible that the 13.48% increasein the fear of vulnerabilities, potential exposures,or risks is related to this change.

n = 1452022 STATE OF DATA SCIENCE REPORT26

ENTERPRISE ADOPTION OF OPEN SOURCE

Securing the Software Supply Chain
and Open-Source Pipeline
Has your organization scaled back the use of open-source
software in the past year due to concerns around security?

33%
No, we have not scaled back25%
Yes, we scaled back after the Log4j vulnerability

20%
I’m not sure

15%
Yes, we scaled back before the Log4j vulnerability

7%No, we have increased use

The Log4j incident in late 2021 was a disruptive
and far-reaching example of an open-source
security breach. 24.90% of commercial
respondents indicated their organizations
scaled back their open-source software usage
after the incident.
32.96% of respondents indicated theirorganizations have not scaled back theiropen-source software usage due to concerns
around security. This speaks to the value of
open source; organizations don’t want to missout on the affordability, flexibility, and limitlesstechnological advancement opportunities
affiliated with OSS.

n = 1,526

2022 STATE OF DATA SCIENCE REPORT27

POPULARITY
OF PYTHON
Python continues to be the preferred programming language among
data scientists, researchers, students, and professionals worldwide.

2022 STATE OF DATA SCIENCE REPORT28

POPULARITY OF PYTHON

How often do you use the following languages?
AlwaysFrequently

Sometimes

RarelyNever

Bash/Shell

9%

22%

24%

19%

26%

C/C++

8%

C#
Go

5%
3%
8%

Java

7%

JavaScript

6%

PHP

3%

2%

SQL
TypeScript

26%

13%

6%

19%

17%

45%

27%

22%

28%
14%

17%

14%

23%

6%

24%
56%

26%
13%

28%
51%

20%

16%

16%

33%

17%

19%

20%

21%

17%

12%

22%

21%

20%

7%

53%

31%

31%

R

42%

14%

18%

12%

30%

19%

18%

5%

22%

19%
18%

Python

Rust

23%

14%
12%

HTML/CSS

Julia

18%

29%
14%

16%

13%

50%

n = 2,274
2022 STATE OF DATA SCIENCE REPORT

29

POPULARITY OF PYTHON

How often do you use the following languages? (cont.)
The majority (58.26%) of survey respondents indicated they always or frequently use
Python, and only 5.72% of respondents never use Python, making it the most popular
choice out of all of the survey options—just like last year.
And why wouldn’t Python be the most popular programming language? It’s an accessible
language that makes a wide variety of programming-driven tasks possible. There are
hundreds of thousands of Python packages available, making Python applicable to manyuse cases. As such, Python is often used by non-programmers and students in addition toprogramming experts, and it makes a great teaching language for AI and ML. In fact, 61.32%
of our academic-track respondents indicated their institutions are teaching Python to
students of data science and machine learning, and 81.08% of student-track respondents
indicated Python is covered in their courses.
Anaconda pioneered the use of Python for data science, and in the past year we’vetaken big steps toward our goal of democratizing data science and advancing Python
accessibility. We developed PyScript, a framework that allows users to create rich Python
applications in the browser, and acquired PythonAnywhere, a cloud-based Python
development and hosting environment that simplifies the web development process andempowers teams to write programs from any modern web browser.

2022 STATE OF DATA SCIENCE REPORT

30

DATA JOBS AND THE
FUTURE OF WORK
According to the Bureau of Labor Statistics, “employment of computerand information research scientists is projected to grow 22 percent
from 2020 to 2030, much faster than the average for all occupations.”
Considering this increasing demand for data talent alongside the current
shortage of said talent, organizations must prioritize employee needs
to attract and retain employees and minimize churn.

2022 STATE OF DATA SCIENCE REPORT

31

DATA JOBS AND THE FUTURE OF WORKJob Satisfaction
How would you rate your job
satisfaction in your current role?

4%

16%

Not at all
satisﬁed

Job Satisfaction by Role
BUSINESSPROFESSIONAL

10%Extremely
satisﬁed

Product manager
Business analyst
Line-of-business manager

Slightlysatisﬁed

DATA SCIENCE

Data scientist
ML engineer
Data engineer

33%

Very satisﬁed

EDUCATION

Professor/instructor/researcher
Student

36%

Moderately
satisﬁed

OPERATIONSCloud engineer
System administrator
CloudOps
Cloud security manager

n = 2,893

DevOpsThe majority (69.27%) of survey
respondents are moderately or verysatisfied with their jobs. Only 4.42%
are not at all satisfied, and only
10.30% are extremely satisfied.

MLOps

OTHER

Other
Developer

SCIENTISTS

Applied scientist
Research scientist

n = 2,893
2022 STATE OF DATA SCIENCE REPORT32

DATA JOBS AND THE FUTURE OF WORKJob Satisfaction
What would cause you to leave yourcurrent employer for a new job?
Respondents were asked to select the top option besides pay/benefits.

32%
More responsibility/opportunity for career advancement

25%
More access to professional training/development

20%
N/A - I would not leave my current position

15%
More ﬂexibility with my work hours

9%
Other
n = 2,581

31.64% of respondents indicated that they would be most motivated to leave theircurrent employers for more responsibility/opportunity for career advancement.

2022 STATE OF DATA SCIENCE REPORT33

DATA JOBS AND THE FUTURE OF WORKThe Talent Shortage
Attracting and retaining employees has challenged thetechnology industry more than ever before. How concerned isyour organization about the potential impact of a talent shortage?

5%

What are you most concerned about with regard
to the current workforce talent shortage?

64%
Ability to recruit/retain technical talent

Extremely
concerned

63%
Recruiting and talent acquisition

32%

25%

Moderately
concerned

Veryconcerned

48%
Our ability to meet business demands

47%
Employee turnover

35%
Diversity and inclusion hiring objectives

31%
Customer service10%

Not at allconcerned

n = 1,438

10%

27%

Slightly
concerned

Most (62.51% of) commercial respondents indicated that their organizations are at least
moderately concerned about the potential impact of a talent shortage. Only 10.43%
indicated that their organizations are not at all concerned.

2022 STATE OF DATA SCIENCE REPORTWe are not concerned with the workforce shortagen = 1,241

Commercial respondents indicated that their top concerns surrounding the current
workforce talent shortage are the ability to recruit/retain technical talent (63.66%) and moregeneral recruiting and talent acquisition (62.77%). Respondents seem to be least concerned
about diversity and inclusion hiring objectives (34.65%) and customer service (30.94%).

34

DATA JOBS AND THE FUTURE OF WORKThe Talent Shortage

44%

Which of the following steps is your organization taking
to address the current workforce talent shortage?

Training current employees

Training current employees is the most prevalent step (44.05%)
that organizations are taking to address the current workforce
talent shortage, with permitting or expanding remote work
options close behind (42.27%).

Permitting or expanding remote work options

42%
38%
Partnering with schools/universities to encourage talent to apply

31%
Increasing salaries

31%
Improving beneﬁts

21%
Increasing automation18%
We are not taking any action to address the current workforce talent shortage

10%
Decreasing job requirements

10%
I’m not suren = 1,403

2022 STATE OF DATA SCIENCE REPORT

35

DATA JOBS AND THE FUTURE OF WORKThe Future Workforce
Where Students Hope to WorkA nonproﬁtorganization
A government
position

Our student respondents shared their biggest obstacles to obtaining
experience required for a career in data science or related fields.

Other
3%
5%

7%

Lack of resources
due to COVID-19

established startup
27% Anin growthmode

Saturated ﬁeld makes
it diﬃcult to stand out

An early-stage
startup company

14%

Learning new
languages

Cost
An academic or
research center

22%

well-established
23% Aindustry
giant

n = 40726.78% of student respondents are hoping to work for an establishedstartup in growth mode upon completing their programs. Tied for second
choice are well-established industry giants (22.60%) and academic or
research centers (22.11%).

Other
3%
4%

9%

27%

Findingan internship

9%

12%

Lack of social capitalor mentor relationships

15%of clarity surrounding
20% Lack
required experiencen = 40726.54% of student respondents cited finding an internship as the biggest
obstacle to obtaining required experience, with the second-biggest obstacle
being a lack of clarity surrounding required experience (19.90%).

The smallest percentages of respondents (6.88% and 4.91%,
respectively) hope to work for a government organization or nonprofit.
2022 STATE OF DATA SCIENCE REPORT

36

BIG QUESTIONSAND TRENDS
Data professionals are grappling with big questions that will likely impact
how the industry evolves. With one eye on the future, we seek to unpack
respondent sentiments surrounding trends, opportunities, and perceived
blockers to growth.

2022 STATE OF DATA SCIENCE REPORT37

BIG QUESTIONS AND TRENDS

AutoML

Enable non-experts to train
machine learning models (2.57)

Last year, 55% of survey respondents indicated that they hoped tosee more automation and AutoML in data science. This year, wewanted to dig into what respondents think an AutoML tool should
do for data scientists.

Quickly and eﬃciently tune very
many hyperparameters (2.75)

On average, enabling non-experts to train machine learning models
is the #1 task non-student respondents think an AutoML tool should
do for data scientists.

Help choose the best model types
to solve speciﬁc problems (2.78)

Speed up the ML pipeline by automating
certain workﬂows (data cleaning, etc.) (3.06)

Tune the model once performance (suchas accuracy, etc.) starts to degrade (3.99)

Other (5.85)

We asked respondents to drag and rank the options from mostto least important, with the first being most important.
n = 2,042

2022 STATE OF DATA SCIENCE REPORT38

BIG QUESTIONS AND TRENDS

Government Involvement in Tech
Do you believe the government should play a largerrole in strengthening technological innovation andmanufacturing in your country?

Which of the following governmental incentives do you support?

70%
More funding for STEM/tech schooling

10%

46%

I’m not sure

More funding for technology companies

35%

15%

More regulation of big tech companies

8%No

N/A - nonen = 1,337

A vast majority (74.98%) of respondents think the government shouldplay a larger role in strengthening technological innovation andmanufacturing. More funding for STEM/tech schooling is the incentivethat the most respondents (69.78% of the respondents who comprise theaforementioned majority) support.

75%
Yes

n = 1,443

2022 STATE OF DATA SCIENCE REPORT

39

BIG QUESTIONS AND TRENDS

Challenges to Innovation
Have supply chain disruption problems such as the ongoing chipshortage impacted your access to computing resources?

What do you think is the biggest problem
in the data science/AI/ML space today?

32%

21%

Social impacts from bias in data and models

I’m not sure18%
Impacts to individual privacy

16%
Advanced information warfare

51%

14%
Lack of diversity and inclusion in the profession

14%

No

A reduction in job opportunities caused by automation

6%
Other

29%
Yes

n = 2,133

Fortunately, most (50.59% of) respondents indicated that supply chain
disruption problems such as the ongoing chip shortage have notimpacted their access to computing resources.
2022 STATE OF DATA SCIENCE REPORTn = 2,154

31.94% of respondents feel that social impacts from bias in data and models
is the biggest problem in the data science/AI/ML space today.
This finding is particularly interesting when examined alongside the factthat when it comes to the aforementioned talent shortage, only 34.65% ofcommercial respondents view diversity and inclusion hiring objectives as a top
concern. Diversity and inclusion staffing practices—or the lack thereof—canhave a direct impact on fairness and bias in data and models.

40

BIG QUESTIONS AND TRENDS

Challenges to Innovation
What do you think are the biggest barriers to thesuccessful enterprise adoption of data science?

What do you believe is the biggest challenge
in the open-source community today?

65%

56%

31%

26%

20%

Insuﬃcient investment in dataengineering and tooling to enable
the production of good models

Insuﬃcient talent or
headcount in the data
science area

Security vulnerabilities

Public trust

Talent shortage

19%

3%

1%

Undermanagement

Other

None

43%
Unrealistic expectations
for results

5%
Other

n = 2,133
n = 1,428

Most (65.34% of) commercial respondents think one of the biggest barriers to
the successful enterprise adoption of data science is insufficient investment indata engineering and tooling to enable the production of good models.
2022 STATE OF DATA SCIENCE REPORT

Most respondents (31.08%) think the biggest challenge in the open-source
community today comes down to security vulnerabilities, which may very
well be linked to the issue of public trust (which 25.74% think is the biggest
challenge).
41

BIG QUESTIONS AND TRENDS

What do you value most about
open-source technology?

21%

21%

18%

13%

14%

12%

1%

Most
economical
optionSpeed ofinnovation

Most usefultools formy needs

Avoid vendor
lock-in

Product
qualityOﬀers
securityOthern = 2,154

In spite of any challenges affiliated with open source, survey respondents
recognize an array of OSS benefits, with affordability (20.84%) and speedof innovation (20.54%) just about tying for most valued.
2022 STATE OF DATA SCIENCE REPORT42

KEY TAKEAWAYS
AND REFLECTIONS
Past, Present, and Future2022 STATE OF DATA SCIENCE REPORT43

1

KEY TAKEAWAYS AND REFLECTIONSIN PROFESSIONALENVIRONMENTS, OPEN-SOURCESECURITY IS TOP OF MIND.

40.23% of commercial-track respondents indicated that theirorganizations scaled back their open-source software usage
in the past year due to concerns around security, and most
respondents (31.08%) selected “security vulnerabilities” as
the biggest challenge in the open-source community today.
As noted, organizations are leveraging a variety of measuresand tools in their efforts to ensure their open-source supply
chains and packages are secure and meet enterprise security
standards.
It’s no surprise that open-source security is so top ofmind, given some of the incidents that have troubled theindustry over the last year. There was the aforementioned
Log4j incident, followed by the rise of protestware, spurredby economic sanctions the U.S. imposed on Russia in
response to the conflict in Ukraine. In March, President
Biden made a statement urging organizations to “strengthen
the cybersecurity and resilience of the critical services andtechnologies on which Americans rely.” Shortly thereafter, we
at Anaconda informed our community of the efforts we’ve
made—and continue to make—to protect commercial users
from cybersecurity threats.
2022 STATE OF DATA SCIENCE REPORTThe issue of open-source security is likely to remain
at the forefront of the data science, AI, and ML industries
for the foreseeable future. While 34.80% of academic-track
respondents indicated that topics related to open-source
security are taught frequently (in multiple classes andlectures) or often (but only during a specific class), 25.75%
indicated that such topics are taught only sometimes (whenthere is a specific lecture), and 23.34% indicated open-source
security hasn’t come up in any discussions or lectures.
Of our student-track respondents, 25.55% indicated that
topics related to open-source security are taught frequently(in multiple classes and lectures) or often (but only during
a specific class), and 32.92% indicated open-source security
hasn’t come up in any discussions or lectures. This data
points to a discrepancy between the weight of open-source
security in the commercial sector and the weight the issue
is given in academic environments.

40%of commercial-track
respondents indicatedthat their organizations
scaled back their opensource software usage
in the past year due toconcerns around security,and most respondents(31.08%) selected“security vulnerabilities”
as the biggest challenge
in the open-source
community today.

44

2

KEY TAKEAWAYS AND REFLECTIONSORGANIZATIONS WILLCONTINUE TO GRAPPLEWITH THE TALENT DILEMMA.

Over the last couple of years, organizations attempting toscale their data science efforts and accelerate technology
advancements and AI/ML adoption have increasingly
noticed, if not suffered from, a lack of data science talent.
As stated, 62.51% of commercial-track survey respondents
indicated that their organizations are at least moderately
concerned about the potential impact of a talent shortage,
with the biggest concerns surrounding the ability to recruit/retain technical talent and more general recruiting and talentacquisition. 56.09% of commercial-track respondents feelthat insufficient talent or headcount in the data science area
is one of the biggest barriers to the successful enterpriseadoption of data science.

2022 STATE OF DATA SCIENCE REPORTAs the data science, AI, and ML presence continues to growin businesses across industries, organizations need to getcreative when it comes to attracting and retaining talent andoptimizing their workforces. Training existing employees andpermitting or expanding remote work options are the mostpopular steps that organizations are taking. Organizations will
want to monitor both skills gaps and the tools and resources
available for continued learning as they continue to upskill
their workforces, and academic institutions and students will
want to make a note of these gaps and opportunities as they
prepare and become jobseekers.

63%of commercial-track
survey respondents
indicated that theirorganizations areat least moderately
concerned about thepotential impact ofa talent shortage.

45

3

KEY TAKEAWAYS AND REFLECTIONSETHICS, BIAS, AND
REGULATION NEED
MORE ATTENTION.

In our 2021 report, we proposed that “preventing bias
and developing ethical data science [was] critical,” and thisyear’s data upholds that truth. As stated above, 31.94% of
survey respondents feel that social impacts from bias in data
and models is (still) the biggest problem in the data science/
AI/ML space today.
To reiterate: While many commercial-track respondents
indicated that their organizations are taking measures orusing tools to ensure fairness and mitigate bias in data sets
and models (most commonly, evaluating data collection
methods according to internally-set standards and manuallyassessing data sets for fairness and bias), 23.64% indicatedthat their organizations do not have standards surrounding/have not implemented measures or tools to address fairness
and bias, and 14.89% aren’t sure.
What’s more, just 18.76% of academic-track respondents
indicated that their institutions are teaching ethics indata science/ML to students, and just 20.15% of student
respondents indicated that ethics in data science/ML iscovered in their courses in preparation for entering the field.

2022 STATE OF DATA SCIENCE REPORT

Plus, only 23.14% of academic respondents and 21.38%
of students said that bias in AI/ML/data science is taught
frequently in classes or lectures, and 39.44% of academic
respondents and 35.62% (-9.38% YoY) of students responded
that it’s rarely or never taught. While this latter percentage
and the YoY decrease it reflects is a step in the right direction,
there is certainly room for a greater emphasis on ethics andbias in the educational sphere.
In a 2022 predictions blog post, we touched on regulationand governance around ethical data and AI throughgovernment interventions and wide-scale standards.
Here we are several months later, and essentially three outof four (74.98%) commercial-track survey respondents feelthe government should play a larger role in strengthening
technological innovation and manufacturing, with more
funding for STEM tech schooling being the incentive thatthe most respondents (69.78% of the 74.98%) support.
Perhaps said schooling should incorporate more topics
surrounding ethics and bias in AI/ML/data science.

32%

of survey respondents
feel that social impacts
from bias in data
and models is (still)
the biggest problem inthe data science/AI/ML
space today.

46

4

KEY TAKEAWAYS AND REFLECTIONSTHE DATA SCIENCE, AI, AND
ML COMMUNITIES ARE READY
FOR MORE INNOVATION.

The #1 advancement that survey respondents are hoping to see from the data science
industry in the coming year is further innovation in the open-source data science
community (32.40%). New, optimized models that allow for more complex computations
to be conducted faster (24.98%) and more resources given to data science teams withinorganizations (23.21%) come in second and third, respectively.
To help support the successful open-source innovation that the data science community
wants to see, organizations will need to address the issues and challenges listed above,
including open-source security, the talent dilemma, and ethics and bias. Educational
institutions and students would do well to monitor these issues and adjust learning paths
to reflect and prepare for the commercial open-source landscape. Organizations will alsoneed to encourage teams to contribute to open-source projects; again, only 51.99% ofcommercial respondents said their teams are encouraged to do so—about a 13% decrease
compared to 2021. Surely continued efforts to mitigate the aforementioned issues will
make organizations progressively more comfortable with such encouragement.
Anaconda remains committed to identifying and tempering challenges that inhibit opensource innovation and providing products, features, and resources to power open-source
advancements, streamline workflows, and instill confidence in OSS among all who rely on
it. We continue to proudly distribute and contribute to a variety of open-source projects,
and we continue to direct a portion of our revenue to the open-source community
through the Anaconda Dividend Program.

2022 STATE OF DATA SCIENCE REPORT

What are you most hoping to see fromthe data science industry this year?
32%
Further innovation in the open-source data science community

25%
New, optimized models that allow for more complex
computations to be conducted faster

23%
More resources given to data science teams within organizations

10%
More specialized data science hardware

6%
Further innovation in vendor-speciﬁc data science products

3%
Other

1%
None
n = 2,15447

2022 STATE OF DATASCIENCE REPORT
If you enjoyed this report, we encourage you to:• Keep an eye on our blog for more news and thought leadership content.
• Tune in to Numerically Speaking: The Anaconda Podcast on your favorite

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• Create a free Anaconda Nucleus account to access educational resources

and connect with other members of the data science community.
• Engage with us at popular industry conferences and events.
• Follow us on social media!

2022 STATE OF DATA SCIENCE REPORT48

ABOUT ANACONDA
With more than 30 million users, Anaconda is the world’s most popular
data science platform and the foundation of modern machine learning.
We pioneered the use of Python for data science, champion its vibrantcommunity, and continue to steward open-source projects that maketomorrow’s innovations possible. Our enterprise-grade solutions
enable corporate, research, and academic institutions around theworld to harness the power of open-source for competitive advantage,
groundbreaking research, and a better world.
Visit https://www.anaconda.com to learn more.

© 2022 ANACONDA INC. ALL RIGHTS RESERVED.