STATE OFDATASCIENCEON THE PATH TO IMPACTEXECUTIVE SUMMARY
We conducted the 2021 State of Data Science survey focusing
on how data science as a field is growing, the overall trends in
adoption from both commercial environments and academic
institutions, and what students can do to prepare for the future.
Given that 2020 and 2021 were affected by the COVID-19
pandemic, we took this opportunity to ask questions aroundhow the pandemic impacted work and how organizations
invested in the field.
This report dives into the day-to-day of a data professional,
the tools and languages they use to be successful, how open
source is adopted, the future of work and business impact,
and big questions around automation, bias, explainability,
and interpretability.

2021 STATE OF DATA SCIENCE REPORTTABLE OF CONTENTS
01 / METHODOLOGY
02 / THE FACE OF DATA SCIENCE
07 / HOW HAS COVID-19 AND THE PANDEMIC IMPACTED DATA SCIENCE?
10 / DATA PROFESSIONALS AT WORK
18 / ENTERPRISE ADOPTION OF OPEN SOURCE
22 / POPULARITY OF PYTHON
25 / DATA LITERACY AND BUSINESS IMPACT
30 / DATA JOBS AND THE FUTURE OF WORK
34 / BIG QUESTIONS
40 / LOOKING AHEAD

2021 STATE OF DATA SCIENCE REPORTMETHODOLOGY
4,299 individuals from more than 140 countries took partin our online survey conducted from April 14, 2021 - May 5,
2021. Respondents came from social media, the Anaconda
email database, and Anaconda.org. They had the opportunity
to participate in a sweepstakes drawing as an incentive forcompleting the survey, and two winners were selected at
random after the survey was complete. The respondents weredivided into three separate tracks: students, academics, andthose working in commercial environments. Each of thesedifferent cohorts was asked some universal questions,while some questions were unique to each cohort’s experience.
In the report, we indicate if responses came from either theentire set of respondents or a subset.
Note: All percentages are rounded to the nearest whole percent. Due to rounding, somenumbers may not equal 100.

2021 STATE OF DATA SCIENCE REPORT

01

THE FACE OFDATA SCIENCE
We started this year’s survey with a series of questions designed toprovide an overview of data professionals. From geographical regions
to generational differences, our respondents’ demographics give asnapshot of how the data science community is evolving, from job
functions, organization size, level of education, and more.

2021 STATE OF DATA SCIENCE REPORT

02

THE FACE OF DATA SCIENCE

More than 4,200 individuals from 140+ countries participated in our online survey.

NUMBER OFRESPONDENTS
 55+
 27-54
 16-26
 7-15
 4-6
 2-3
1

n = 4,229

2021 STATE OF DATA SCIENCE REPORT

03

THE FACE OF DATA SCIENCE

Respondent Age
24%

50%

Respondent Gender and How They Identify
18%

5%

3%

5%

72%

Non-binary

Man

23%
Woman

18-24

25-40

41-56

57-66

67+

n = 4,229

n = 4,229

Our respondent set skews heavily toward younger generations. Of the 4,229 respondents,
74% are Generation Z (24%) or Millennials (50%). Compared to 2020, this year, we saw a 15%
increase in respondents from Generation Z (ages 18-24).

It comes as no surprise that this data supports what we’ve seen across other maledominated STEM-related fields. Nevertheless, there is an opportunity for the industry to workon increasing gender diversity.

2021 STATE OF DATA SCIENCE REPORT

04

THE FACE OF DATA SCIENCE

Respondent Education Level
The majority of respondents are also well-educated. 68% have a college-level degree.

2%

13%

17%

34%

24%

10%

32%

of respondents do not holdany college degree — with theincreasing availability of
online courses and ways to build
technical skills and experience,
having a degree isn’t a prerequisite
for getting started in data science.

No
degreeHigh school
degree or
equivalentSomecollege

Bachelor’s
degreeMaster’s
degreeDoctorate
degreen = 4,229

2021 STATE OF DATA SCIENCE REPORT

05

THE FACE OF DATA SCIENCE

Respondent Primary Job Function
Business analyst
Cloud engineer
Cloud security manager
CloudOps
Data engineer
Data scientist
DevOpsDeveloper
Line-of-business manager
ML engineer
MLOps
Product manager
Professor/instructor/researcher
Researcher (non-academic)
Student
System administrator

Respondent Current Job Level
11%
4%
4%
2%

4%
15%

Other

Owner/Executive/
C-Level5%
VP

Entry Level

7%
11%
1%
6%
4%
2%
1%
4%

8%

10%
Director

25%
Senior9%
5%

26%Manager

8%

27%
2%

Principal

We asked our respondents to choose one role that best represents their job function. However, because
there are various data-focused job roles, and on some teams many individual titles are responsible formultiple tasks, there is often overlap between job functions (for example, a Machine Learning Engineer
may do product integration, which traditionally may be considered more of an MLOps job).

Entry-level positions made up the third-largest group (15%), mirroring
the generational demographics of our Millennial and Generation Z
respondents who work in a commercial environment.

n = 4,229

n = 2,664

2021 STATE OF DATA SCIENCE REPORT

06

HOW HAS COVID-19
AND THE PANDEMIC
IMPACTEDDATA SCIENCE?
COVID-19 played an important role in how we worked,
lived, socialized, and focused our resources over the past year.
The pandemic likely influenced answers throughout the survey,
especially in allocating resources, job functions, and more.

2021 STATE OF DATA SCIENCE REPORT07

HOW HAS COVID-19 AND THE PANDEMIC IMPACTED DATA SCIENCE?

Did the COVID-19 pandemic impact yourorganization’s investment in data science?
Of respondents who said the pandemic impacted theirorganization’s investment in data science, 50% said theinvestment stayed the same or increased, meaning dataroles remained significant throughout the pandemic.
COVID-19 had a trickle-down effect that impacted
virtually every industry – from healthcare to government,
financial institutions, and more; they all needed to findways to act quickly on data and find solutions to new
problems. Additionally, when asked how involved theirrole is in business decisions, 14% of respondents said“all” decisions rely on insights interpreted by them ortheir team, and 39% said “many” business decisions
rely on them. While there is still work needed to ensure
we bring data scientists into the fold, it’s encouraging
to see their value is recognized in organizations andmight explain why the field avoided a sharp decrease ininvestment. We dive into this more in the report’s
data literacy and business impact section and identify
further opportunities for improvement.
n = 2,229
In total, 50% said their data science investment stayed the same or increased during the pandemic,
while 37% saw a decrease.

2021 STATE OF DATA SCIENCE REPORT08

HOW HAS COVID-19 AND THE PANDEMIC IMPACTED DATA SCIENCE?

We took a deeper look at why individuals said there was an increase or decrease in investment.
We asked respondents to select all that apply.

Increased Investment

Decreased Investment

Those who said their investment increased saw it mostly allocatedtoward hiring, followed by increasing budgets and additional or
expedited projects.

For those who saw a decrease in investment, the trends weretoward no team growth, followed by decreased budgets.

data science?

data science?

In what ways
In has
what
your
ways
organization
has your organization In what ways
In has
what
your
ways
organization
has your organization
increased its
increased
investment
its
investmentin
data
science?
indata
science?
decreasedits
decreased
investment
its
investmentin
data
science?
in
data
scien
In what ways has your organization increased its investment in
In what ways has your organization decreased its investment in
51%
Increased budget

Increased budget

Decreased budget

55%
Active hiring

51%

No team growth

54%
28%

Team layoﬀs

Other

39%

39%

35%

35%

Project timelines on hold
Project
indeﬁnitely
timelines
or onfor hold
an extended
indeﬁnitely
periodor for an extended period

2%
Other

47%

Team layoﬀs

28%

More tools at disposal More tools at disposal

47%
No team growth54%

Additional projects or expedited
Additionalprojectprojectstimelines
or expedited project timelines

45%

Decreased budget

55%

Active hiring

45%

2%

17%

17%

0.5%

0.5%

Fewer tools at disposalFewer tools at disposal

n = 569

Other

Other

n = 8182021 STATE OF DATA SCIENCE REPORT09

DATA PROFESSIONALSAT WORKThe majority of our respondents work in commercial environments.
We took a closer look at these responses to get more detail on wheredata professionals sit in the organization, how they spend their time,
what tools they use, and the most significant challenges in their roles.

2021 STATE OF DATA SCIENCE REPORT

10DATA PROFESSIONALS AT WORK

Respondent Industry
Technology

10%

Manufacturing

3%

Academic

7%

Aerospace2%

Consulting

7%

Chemicals

2%

Automotive

6%

Defense2%

Banking

6%Energy2%Engineering

5%

Retail2%

Finance5%Telecommunications

2%

Apparel4%Entertainment

1%

Education

4%

Environmental

1%

Communications

4%

Food & Beverage

1%

Agriculture

3%

Insurance

1%

Biotechnology

3%

Media

1%

Construction

3%

Not For Proﬁt

1%

Electronics

3%

Pharmaceutical

1%

Government

3%

Transportation

1%

Healthcare

3%

Utilities

1%

We saw representationfrom virtually every
industry in ourcommercial respondent
set. From higher
education to
not-for-profit and retail,companies are prioritizing
data-driven roles.

n = 2,644
Of the individuals surveyed, the top nine industries represented were: Technology (10%), Academic (7%), Consulting(7%), Automotive (6%), Banking (6%), Engineering (5%), Finance (5%), Apparel (4%) and Education (4%).

2021 STATE OF DATA SCIENCE REPORT

11

DATA PROFESSIONALS AT WORKTeam Size
To better understand data professionals at work and how they fit into the corporate structure, we asked individuals
about the size of their organization and if they worked on a team or by themselves.

81%
Team

10%

20+ people

Do you work ona team, or are youa solo practitioner?

17%

What is the sizeof the team you sit on?

44%

n = 1,902

11-20 peoplen = 2,644

6-10 people

29%

1-5 people

While 81% of our
respondents work ona team, most teams
tend to be small.
For example, 73% of
respondents indicatedthey were part of ateam with ten people
or fewer. While hiring
data science talent isdifficult, new challenges
will emerge as leaders
decide how to structure
these teams. The best
approach will dependon the stage of maturityof an organization’s data
science or AI program.

19%

Solo practitioner

2021 STATE OF DATA SCIENCE REPORT

12DATA PROFESSIONALS AT WORK

What department does your role fall under?
Where do data roles fall within the organization? The short answer
is: everywhere. Organizations structure their departments differently.
Sometimes there is an entire data-focused team; other times, data scientists
are seated within different departments. Most of our respondents’ roles fallunder IT (23%) and Research & Development (16%). In large organizations
with 10,001+ employees, we found that individuals in data-focused roles
primarily fall under R&D (18%), IT (18%), and finance departments (16%).
In larger organizations with more mature data science programs, there are
more resources to ensure data roles are integrated throughout the businessand in cross-functional teams.
For data scientists specifically, they are most often working under the IT
function of the organization (22%), followed by R&D (21%) and theData Science Center of Excellence (20%). We found that 43% oforganizations with a Data Science Center of Excellence have data scienceteams made up of 1-5 people, compared to IT teams with data scientists,
which most of our respondents said have 6-10 people. This is likely because
IT teams also include additional roles such as system analysts, developers,
and programmers.

n = 2,644

2021 STATE OF DATA SCIENCE REPORT

IT

23%

Research and Development

16%

Data Science Center of Excellence

8%

Operations

8%

Finance6%None of these6%

Administration

5%

Customer Service/Support

5%Marketing5%

Human Resources

4%

Production

4%

Distribution

3%

Sales

3%

Accounting

2%

Purchasing

2%
13

DATA PROFESSIONALS AT WORKHow do data scientists spend their time?

11%

Data scientists spend their day focused on various tasks that require adiverse set of technical and non-technical skills. When asked how muchtime they spend on tasks, respondents stated they spent about 39% of theirtime on data prep and data cleansing, which is more than the time spent onmodel training, model selection, and deploying models combined.

Model
training

While data preparation and data cleansing are time consuming andpotentially tedious, automation is not the solution. Instead, having a humanin the mix ensures data quality, more accurate results, and provides context
for the data.

Datavisualization

12%
12%

Beyond preparing and cleaning data, interpreting results is also critical.
Visualization and demonstrating the data’s value through reportingand presentation are essential parts of making that data actionable andultimately providing answers to critical questions.

Modelselection

15%
17%

Data
cleansing

DeployingmodelsReporting andpresentation

17%
22%

Data
preparationn = 2,030
We asked our respondents how much time they spend on each of the above tasks, and for each item, enter anumber representing the percentage of time spent on each task relative to the other tasks on this list. This is
the average of the reported percentages.
2021 STATE OF DATA SCIENCE REPORT

14

DATA PROFESSIONALS AT WORK

Getting to Production
Data can provide immense value to an enterprise, which goes beyond
gathering business insights, refreshing dashboards, and monitoring KPIs.
Organizations that deploy machine learning models and other data science
outputs to power business functions and products have a competitive
advantage and rely on the value data scientists bring to the table.
However, while getting models to production is one of the most rewarding
jobs of a data professional, these individuals are often faced with challenges
outside of their control.
From the survey responses, 28% said they do not deploy models in
production. However, the respondents in commercial environments who domove models to a production environment cited these top four roadblocks:

27%

Meeting IT security standards

24%

Re-coding models fromPython/R to another language

23%

Managing dependencies
and environments

23%

Re-coding models fromanother language to Python/R

2021 STATE OF DATA SCIENCE REPORT

Barriers to production
differ across job rolesand departments.
Alignment on a project’s
purpose, scope, budget,and goals across theorganization andamongst technical andbusiness stakeholders is
critical to getting models
deployed. Organizations
have an opportunity
to address theseroadblocks to see anincreased impact fromtheir data science andmachine learning efforts.

15

DATA PROFESSIONALS AT WORK

What roadblocks do you face when moving your models to a production environment?
We asked respondents to select all that apply.

When we break the roadblocks down by job function, there is a clear difference between the priorities based on role in the business. For example, data scientists see their biggest roadblock as askills gap, which makes sense given the gaps we see between what enterprises need and what educational institutions teach.

27%
29%
27%
31%
21%
13%
12%
15%

15%

31%
16%
30%
19%
23%
26%
17%
19%
22%

29%

20%
16%
23%
20%

in production

CloudOps

Data engineer

Data scientist

language to Python/R

DevOps Access to compute resources Securing data connectivity

41%

44%

34%

33%

35%

38%

23%
25%
20%
25%
13%

16%
16%

Meeting IT security standards is the top
blocker for DevOps, Data Engineers,
Product Managers, and System Admins.
2021 STATE OF DATA SCIENCE REPORTML engineer

MLOps

 Managing dependencies
 Moving to the cloud A skills gap in my organization(Data engineering, Docker,
Kubernetes, etc.)0%

N = 2,294

Line-of-business
manager Securing network connectivity
and environments

6%

9%

14%
24%
11%
15%
15%
16%
20%
18%

17%
21%
24%
17%
23%
20%
21%
26%

20%

14%

18%

12%

19%
19%
15%
12%

22%
18%

27%

29%
34%
31%

28%

31%

35%

36%

29%

25%
16%
19%
19%
16%
16%

24%
16%

16%
22%
22%
18%
20%

26%
29%

30%

28%
33%

Developer Re-coding models from Python/R Re-coding models from another46%

Cloud security
manager Meeting IT security standards
to another language

1%

4%

5%
7%

8%
12%

17%

19%

10%
15%
15%
20%

12%

15%
18%
19%
18%

18%
23%
22%

27%
26%
28%
27%

33%

33%
32%

29%

26%

28%

20%

23%
11%
17%
18%
23%
16%
21%
20%
24%

Cloud engineer

38%

44%

44%
42%

38%
36%

43%

Business analyst

 We do not deploy models

Product manager

Re-coding models was the biggest
roadblock to production for those
involved in infrastructure.

Researcher(non-academic)

Systemadministrator

For Machine Learning Engineers, thebiggest roadblock to getting models to
production is access to compute resources.
16

DATA PROFESSIONALS AT WORKWhen purchasing a system for your data science workflows,which of these factors most influence your decision?
We asked respondents to choose their top four factors.

Performance (CPU/GPU)

60%

Memory46%

Approved by IT department

45%

OS (e.g., Linux, Windows, other)

42%

Company reputation for help/support

40%

Quality/warranty

37%

Brand (e.g., HP, Dell, Lenovo, Apple)

32%

Form factor (laptop vs. desktop)

29%

Networking

26%

Recommended by others

26%

Style and design

16%

Supported peripherals

9%

Additionally, when asked about purchasing a system fordata science workflows, 60% said performance (CPU/GPU)is a top factor when deciding between hardware options.
Machine Learning Engineers view compute resources
as the most significant roadblock to deploying models
to production. This is something enterprises should be
mindful of when choosing hardware and cloud resources.

n = 3,1042021 STATE OF DATA SCIENCE REPORT

17

ENTERPRISE ADOPTION
OF OPEN SOURCE
Using and contributing to open-source software (Python / R libraries
such as pandas, NumPy, etc.) is a key differentiator of the mostinnovative organizations. By using open-source software, organizations
can save tremendous amounts of time and resources. Rather thanpurchasing from a single vendor or building everything in-house,
this crowdsourced model leverages multiple minds at work toaccelerate projects that would typically take years.

2021 STATE OF DATA SCIENCE REPORT

18

7%
No

ENTERPRISE ADOPTION OF OPEN SOURCE

Does your organization allow the use ofopen-source software?

We asked respondents to select all that apply.

6%

7%

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

14%

Not sure46%

Not sure

No

Additional time dedicated to contributing to open-source projects

21%
No
87%
Yes

54%
Funding speciﬁc to open-source project development

41%

65%
Yes

Team members dedicated to contributing to open-source projects

n = 2,289

Does your employer encourage you and yourteam to contribute to open-source projects?
6%
Not sure

Not sureNo

2021 STATE OF DATA SCIENCE REPORT

3%n = 1,366

21%

n = 2,107

Support decreased due to COVID-19 or other factors

Other

14%

87%
Yes

18%

65%
Yes

Of the 65% of individuals who said their teams were encouraged to contribute to opensource projects, the majority of respondents (54%) said that their employers are empowering
them to contribute to open source through an increase in funding related to open-source
project development. This was followed closely by respondents saying they receive
additional time dedicated to contributing (46%), and then team members dedicated tocontributing to open-source projects (41%). Even as the past year has presented its shareof challenges, only 18% of survey respondents said that employer support for open source
decreased due to COVID-19 or other factors.
19

ENTERPRISE ADOPTION OF OPEN SOURCE

Securing the Open-Source Pipeline
How organizations ensure open-source packages used for data science and machine learning are secure and meet enterprise security standards.
We asked respondents to select all that apply.

45%

33%

30%

25%

20%

2%

We use amanaged repository

Manual checks against
a vulnerability database

We use avulnerability scanner

We are not securing
our open-source pipeline

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

Othern = 1,972

When asked about how those organizations that use open-source software ensure security, 45% of respondents say they use a managed repository, 30% use a vulnerability scanner, and 33% domanual checks against a vulnerability database. However, 25% are not securing their open-source pipeline, and 20% did not report any knowledge about open-source package security.
Like any proprietary software, open source also comes with inherent security management challenges. However, open source’s nature of being “open” means various individuals are involvedin the process, making it vulnerable to risk. The good thing is that it also allows contributors and maintainers to catch and patch any vulnerabilities quickly. Organizations must understand thatthey can reap the value and innovation of open-source software while being mindful of pipeline management and security.

2021 STATE OF DATA SCIENCE REPORT20

ENTERPRISE ADOPTION OF OPEN SOURCE

Securing the Open-Source Pipeline
For the organizations that aren’t using open-source software, what is thebarrier to entry? There are various reasons, but similar to the roadblocks
for deploying models to a production environment, security concerns
reign supreme.

What roadblocks are preventing your organization’s use of open source?
We asked respondents to select all that apply.

41%
Fear of CVEs, potential exposures, or risks

31%
Not wanting to disrupt current projects in place30%
Lack of knowledge or understanding

26%
Open-source software is deemed insecure, so it’s not allowed

Based on the responses, a majority of organizations
are using open-source software. But, of the 7% of
respondents who are not, the biggest reason why isfear of CVEs (Common Vulnerabilities and Exposures),potential exposures, or risks. These fears also reflect
the 25% of respondents who aren’t securing theiropen-source pipeline and 30% of respondents wholack knowledge or understanding of open source.
These concerns are particularly prevalent among
mid-to-large-sized organizations, perhaps reflecting
the more stringent regulatory environment in which
many of these companies operate.

25%
Organization doesn’t have strong IT governance in place

4%
Other
n = 1422021 STATE OF DATA SCIENCE REPORT21

POPULARITY
OF PYTHON
For data scientists, researchers, students, and professionals
worldwide, Python is becoming an increasingly popular
programming language.

2021 STATE OF DATA SCIENCE REPORT

22

POPULARITY OF PYTHON

How often do you use the following languages?
AlwaysFrequently

Sometimes

Rarely

Never

34%

29%

22%

11%

4%

Python
SQL

15%

R

20%

10%

27%

17%

16%

25%

JavaScript

8%

16%

HTML/CSS

7%

17%

Java

6%

Bash/Shell

6%

13%

22%

C/C++

5%

13%

23%

C#

4%

10%

TypeScript

4%

10%

PHP

3%

11%

Rust

3%

8%

14%

Julia

3%

8%

16%

Go

2%

9%

14%

18%

30%

19%

24%
28%

15%

22%

33%
21%

22%

27%

19%

38%

19%

40%
25%

19%

34%

18%

17%

49%

12%

18%

57%
16%

12%
13%
13%

52%
63%
61%
62%

n = 3,1042021 STATE OF DATA SCIENCE REPORT23

POPULARITY OF PYTHON

How often do you use the following languages?
Python appears poised to continue its dominance in the field. 63% of
respondents said they always or frequently use Python, making it the mostpopular language included in this year’s survey. In addition, 71% of educators
are teaching Python, and 88% of students reported being taught Python in
preparation to enter the data science/ML field. Even in our own Anaconda
usage data, we’ve seen impressive growth in Python. Between March
2020 to February 2021, the pandemic economic period, we saw 4.6 billion
package downloads, a 48% increase from the previous year. We believe
some of this increase could be related to workers transitioning to work fromhome and more individuals having free time during the pandemic to learn,
improve their skills, and pursue their interest in Python.
Beyond being a top language used in commercial environments and taught at universities,
Python’s popularity can also be demonstrated by various other factors, such as its easeof use, libraries, and community. 20% of students said the biggest obstacle to obtaining
the experience required for a career in data science is learning a new language. With
most educators teaching Python and Python’s continued popularity in the data science
community, there is an opportunity for the Python language to become an industry
standard. Standardization could help solve re-coding pain points associated with deploying
models into production.

2021 STATE OF DATA SCIENCE REPORT

When asked whichdata science andmachine learning toolsorganizations use, it’sno surprise given our
survey sample; 51%of our commercial
respondents said theyuse Anaconda. Other
popular tools include
GitHub (35%), Azure ML
Studio (23%), Power BI(21%), and Tableau (20%).

24

DATA LITERACY AND
BUSINESS IMPACT
Organizations rely heavily on data to help make decisions,but interpreting and communicating that information is what
ensures it’s understood and appropriate action happens.
From our results, we learned business leaders have an opportunity
to become more data literate, and data scientists can improve theirbusiness skills to impact how decisions are made.

2021 STATE OF DATA SCIENCE REPORT

25

DATA LITERACY AND BUSINESS IMPACT

A lack of business knowledge is impacting a data professional’s role in business decisions.
Data practitioners are often brought intobusiness decisions, but there is a gap in
how their insights are ultimately used.
For example, 39% said many decisions
are based on insights interpreted byme and my team. While this shows thegrowing maturity of the field and might
explain why many organizations avoided
cuts to these departments during
COVID-19, there’s still room for growth.
Data science teams bring valuable knowledge
to the table, but they don’t always have thebusiness background or experience to provide
context. In addition, the gap in data literacy
amongst leaders makes it difficult for them toenact change based on their insights. Thereneeds to be a greater connection between theC-suite and its data practitioners.

How involved our respondents are in business decisions

12%

I am not sure how business decisions
are made based on the insights
interpreted by me and my team

35%

Some business decisions
are based on the insights
interpreted by me and my team

14%

All business decisions
rely on the insights interpretedby me and my team

39%

Many business decisions
are based on the insights
interpreted by me and my teamn = 2,177

2021 STATE OF DATA SCIENCE REPORT

26

DATA LITERACY AND BUSINESS IMPACT

Data literacy is critical to making impactful data-driven business decisions. Yet, while most leaders have a baseline of data knowledge, there is still a gap.
Based on our responses, only 36% of people said their organization’s decision-makers are very data literate and understand the stories told by visualizations
and models. In comparison, 52% described their organization’s decision-makers as mostly data literate but needing some coaching on the stories told byvisualizations and models.
This lack of data literacy at the executive level ultimately hurts the ability to make data-driven business decisions. Additionally, 25% of respondents said that a lack of data literacy among
decision-makers at their organization limited their team’s ability to impact business decisions.
Other factors limiting data scientists’ ability to impact business decisions are that priorities shift quickly (33%), there are not enough resources for effective analysis (25%), and that they or theirteam cannot effectively demonstrate business impact (11%).

Data literacy of leadership and skills that are missing in the data science/ML area of the organization

36%

52%

12%

My organization’s decision-makers
are very data literate and understandthe stories told by visualizations and models

My organization’s decision-makers are mostly
data literate but need some coaching on
the stories told by visualizations and models

My organization’s decision-makers
have trouble understanding the stories told
by data visualizations and models

n = 2,177

2021 STATE OF DATA SCIENCE REPORT

27

DATA LITERACY AND BUSINESS IMPACT

What’s limiting our respondents’ ability to impact business decisions based on their job function?
When breaking this question down by job function, there were trends between different roles and what’s limiting their ability to impact business decisions. Cloud Engineers, Cloud Security
Managers, and CloudOps all felt their most significant limitation was decision-makers struggling with data literacy. Business Analysts, Data Scientists, Machine Learning Engineers, and more felt
their limitations stemmed from business priorities shifting quickly. Additionally, MLOps indicated that they do not have enough resources, mirroring their biggest roadblock to deploying models:compute resources.
37%

Business analyst
Cloud engineer

26%

26%

52%

31%

50%

CloudOps

32%

47%

Data engineer

34%

Data scientist

32%
38%

Developer

37%

29%

Researcher (non-academic)

30%
16%

 Business priorities
shift quicklyn = 1,015

2021 STATE OF DATA SCIENCE REPORT

24%
23%

15%
27%

14%

8%
13%

21%

9%

6%

50%
27%

24%

8%

5%

26%

12%

5%

26%

33%

Product manager
System administrator

18%

17%

6%

30%

9%

39%

3%

21%
9%

20%

13%
14%

33%

27%

ML engineer
MLOps

22%
24%

DevOps5%

6%

Cloud security manager

Line-of-business manager

22%

11%

29%

13%
35%

11%
21%

 Decision-makers at

 I or my team can’t

my organization
struggle with data
literacyeﬀectively
demonstratebusiness impact

4%
13%

34%

 There are not5% Otherenough resources foreﬀective analysis

28

DATA LITERACY AND BUSINESS IMPACT

Skills gaps and how they’re impacting decision making
Our survey shows gaps between what enterprises need, what institutions are teaching, and where students feel the most confidence in theirskills. For example, nearly a quarter of enterprise respondents listed “business knowledge” as lacking from data practitioners at their organization.
“Business knowledge” also ranked low on questions about what data science students are learning (10%) and being taught in school (15%).

35% 34%26%

 What enterprises lack

 What students learn

 What universities oﬀer

n = 2,130

n = 1,074

n = 355

38%
24%

29%

29%
18%

21%

24%
10%

Advancedmathematics

48% 50%Big data
management

Businessanalytics

15%

Businessknowledge

22% 20%27%

23%

Communication
skills31%

17%

Computersystems

28%

27%

18% 20%22%

Computervision/image
processingDatavisualization

34% 32%

Deep learning

88%
71%

47%

27% 26%17%

15% 16% 17%

Engineeringskills

Ethics in data
science/machinelearning

10%

15%

48%

44%

39%

21%
12% 12% 14%

12%

JavaScript

Machine learning

NLP

19%

34%
26%

20%
11%

Probability andstatistics

31%

Python21%

Overall, soft andbusiness-related
skills were the mostsignificant gaps between
what universities teachand what organizations
need. Being involvedin strategic businessconversations andcommunicating andexplaining results tostakeholders are skill setsthat can bridge the gap
between data literacy
and decision making.

10%

R

SQL

Respondents were asked to select all that apply.
2021 STATE OF DATA SCIENCE REPORT

29

DATA JOBS AND THE
FUTURE OF WORK
We found that job satisfaction correlates with job function in
the data science field. Pain points that interfere with being able
to do jobs effectively are why individuals seek new positions.
According to the Bureau of Labor Statistics, data science is listed
in the top 15 fastest-growing occupations and is projected tohave a 31% job growth over the next 10 years. Considering there
are so many organizations looking for data roles coupled withthe fact that there is an overall talent shortage for these technicalpositions, organizations need to ensure they’re meeting theneeds of their employees to attract and retain talent. Whether
from not having enough resources to do their job effectively or adata literacy gap in leadership, there is potential for a high rate of
employee churn in the 1-2 year horizon.

2021 STATE OF DATA SCIENCE REPORT

30

DATA JOBS AND THE FUTURE OF WORK

How long our respondents plan to stay with their current employer
Business analyst

25%

Cloud engineer

9%

Cloud security manager

6%

CloudOps

4%

24%

27%

12%

64%

9%

Data scientist

18%

16%

14%

13%

Line-of-business manager

37%

14%

22%

18%

Researcher (non-academic)

13%
50%

System administrator

 For the foreseeable

2021 STATE OF DATA SCIENCE REPORT

28%

20%

21%

28%
21%
29%

27%
15%

54%

n = 2,177

38%

59%
30%

future

22%

39%

9%

Product manager

24%

32%

38%

3%

27%

18%

Developer

MLOps

22%
33%

36%

ML engineer

24%
55%

21%

DevOps

15%

60%
19%

Data engineer

23%

 I am currently looking
for a new employer

30%
15%

10%

19%

20%
17%

 I will probably look for a  I will probably look for adiﬀerent employer in
6-12 months

diﬀerent employer in
the next 2-3 years

32%of our total respondents
are looking for a jobin the next 6-12 months.

Of our respondents, businessanalysts are most likely to look
for a new job right now. In
the next 6-12 months, Cloud
Engineers, Cloud Security, CloudOps, and ML Ops will most
likely look for a new job. Data
Scientists, Developers, System
Admins, Line-of-BusinessManagers and Researchers aremost likely to stay in their current
positions and are the mostsatisfied with their work. When
asked about how leadership
perceives our respondents’ roles,
10% of respondents said, “Mydata science work is valued, but[leadership] doesn’t understandwhat I do.” Business readiness,having the right resources,
as well as perception of data
science roles versus the reality
could impact overallsatisfaction at work.

31

DATA JOBS AND THE FUTURE OF WORK

Which of the following do you think best
describes how leadership perceives your role?
“Exploring data, asking questions, and building models that lead to better
decision-making” was our top response for how leadership perceives dataroles. Yet, while data practitioners are involved in decisions, they may notalways feel their insights are accurately considered.

38%
Exploring data, asking questions, and building models that lead to better
decision-making

26%
The go-to person for all things data, reporting, and analytics

22%
Building AI and machine learning applications

10%
My data science work is valued, but they don’t understand what I do

3%
Other
n = 2,177

2021 STATE OF DATA SCIENCE REPORT32

DATA JOBS AND THE FUTURE OF WORK

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

18%

28%

20%

Cost

Finding aninternship

Learning new
languages

16%

11%

6%

The saturated ﬁeld makes it
diﬃcult to stand out

Lack of resources
due to COVID-19

Othern = 1,074

2021 STATE OF DATA SCIENCE REPORT

28%
said finding an internshipis their biggest obstacle toobtaining experience,
followed by 20% who saidlearning a new language.

The data field is competitiveand saturated with a variety of
titles. Therefore, students needto learn about the technicaland soft skills required tosucceed and the differences
between job functions indata-related fields to better
chart a potential course fora career. In addition, thesefunctions and expectations
may change between matureand smaller organizations.
Understanding the pain pointsof these different roles, theiroverall job satisfaction, andtheir involvement in businessdecisions can guide students
toward any additional courses
or online learning to improvetheir abilities.
33

BIG QUESTIONS
As we think about the future, we want to know the sentimentsfrom our respondents toward trends, potential opportunities,
and any setbacks to growth. Given the influential role ML and AI
play in business and society, data professionals grapple with bigquestions that often impact how the field is evolving.

2021 STATE OF DATA SCIENCE REPORT34

BIG QUESTIONSWhat our respondents felt was the biggest problem to tackle in AI/ML

31%
Social impacts from bias in data and models

21%
Impacts to individual privacy

19%
A reduction in job opportunities caused by automation

15%
Advanced information warfare

10%
Lack of diversity and inclusion in the profession

4%

Our respondents said
the social impacts ofbias in data and models
are the most significant
problem in AI/ML.
Therefore, we asked
about their teams’
steps to mitigate bias
and ensure model
explainability.

Othern = 3,104

31% of respondents believe the most significant problem to tackle in AI/ML today is the social impacts caused bybias in data and models. However, not all organizations and educational institutions are taking the necessary steps totackle this issue.

2021 STATE OF DATA SCIENCE REPORT

35

BIG QUESTIONS

Is your data science team planning to take any steps to ensure fairness and mitigate bias or toaddress model explainability?
Fairness and bias mitigation

Model explainability
and interpretability

While only 10% of respondents indicated their organizations have already implemented asolution to ensure fairness and mitigate bias, it’s encouraging to see 30% said they plan toimplement steps in 12 months, a 7% increase from 2020 (23%).
Similarly, while 31% of respondents stated their organizations also don’t have any plans toensure model explainability and interpretability, 41% said they plan to take steps in the next
12 months or have already implemented at least one step.
While steps to mitigate bias and ensure fairness are also present in higher education, only
17% of educators responded that they are teaching students about ethics in data scienceand ML, and only 16% of students stated they are being taught ethics in data science and
ML in preparation to enter the field. Similarly, only 22% of educators and 24% of students
said that bias in AI/ML/data science is taught frequently in class or lectures – 41% ofeducators and 45% of students responded that it is rarely or never taught. To move theneedle forward for supporting fairness in data, educational institutions and educators mustalso make this a priority.

30% No, and we are not planning to
30% Yes, we are planning to in the

31% No, and we are not planning to
31% Yes, we are planning to in thenext 12 months

next 12 months

10% Yes, we have already

10% Yes, we have already

implemented at least one step

implemented at least one step

30% I don't know

27% I don't known = 2,135n = 2,1352021 STATE OF DATA SCIENCE REPORT

When breaking down the survey results further by organization size, organizations of
10,001+ employees had the most significant percentage of respondents say they had
already implemented at least one step to help ensure fairness and mitigate bias in data sets
and models. Since large organizations often require additional resources to be at the leadingedge of industry work, they can allocate resources to problems like combating bias in AI.
However, when employees of large organizations were asked about their plans to mitigate
bias, the most common response was still, “I don’t know.” This suggests that there’s room to
educate all employees about combating bias in AI/ML. In addition, more interdepartmental
work on these topics will allow employees to participate in discussions about theircompanies’ policies in these areas, advocating for further work where needed.

36

BIG QUESTIONS

Ensuring Fairness, Mitigating Bias,and Model Explainability
Data is always going to have some inherent bias due to natural limitations
and constraints. Because all data has an unintentional bias, data should be
examined from this lens, even if you think it is unbiased and fair.
Considering social impacts from bias in data and models is one of thebiggest concerns for organizations, it’s essential to ask questions like,“How did we choose which data to collect?” and, “Which data points may
be excluded as a result?” It is also beneficial to open up access to data forreview by multiple parties who bring different perspectives to the table.
With that review process, it’s also important to consider explainability and interpretability.
While the number of organizations implementing a solution slipped from last year, this
could be because of the pandemic. Of those who saw a decrease in resources due to
COVID-19, 35% of responses indicated that project timelines were on hold indefinitely or
for an extended period. We hope to see that as our world changes, mitigating bias, ensuring
fairness, and model explainability continues to progress.

2021 STATE OF DATA SCIENCE REPORT37

BIG QUESTIONS

Positive sentiment toward AutoML in data science
What is your sentiment toward automation or AutoML, the process of automating tasks involved in applying machinelearning to real-world problems, in data science?

55%
Positive
Hope to see more automation and AutoML in data science41%
NeutralNo strong feeling toward automation or AutoML in data science

4%

Negative
Concerned how automation and AutoML may impact data science

A common theme in
the news today is that
automation is taking
over and will eventuallyreplace human workers.
However, results
show that automation
is welcomed in thedata science sector
and isn’t viewed as acompetitor but rather acomplementary tool
to practitioners.

n = 3,104
55% of respondents hope to see more automation and AutoML in data science, while only 4% are concerned with how automation will impact data science.

2021 STATE OF DATA SCIENCE REPORT38

BIG QUESTIONSWhat is the biggest myth about data science?

15%
You need lots of data

19%
Data scientists don’t know how to code

31%
Having access to more data translates to greater accuracy

33%
Data scientists will be replaced by artiﬁcial intelligence soon

3%
Other

Additionally, when asked about data science myths, 33%
of respondents replied, saying the biggest myth is that data
scientists will be replaced by AI soon. On the contrary,
there is an opportunity for AI and automation to help
with easily repeatable tasks, to free up more resources
for work that requires human intervention, interpretation,and problem solving. Automation will allow individuals todevelop more complex models or algorithms and spend
less time on routine work that doesn’t need to rely on
personalization or a high level of detail.
2021 STATE OF DATA SCIENCE REPORTn = 3,10433%

of respondents believe thebiggest myth is data scientists
will be replaced by AI soon.

A majority of data science professionals spend their days prepping andcleansing data. While automating some data prep processes, such asdata anonymization, will help free up time, it’s important to rememberthat data preparation helps data scientists better understand the dataand its limitations in order to build better models.
Similarly, there is a myth in data science that says quantity over quality
is best. 31% of respondents said they believe the biggest myth indata science is that having access to more data translates to greater
accuracy. The old adage is still valid at the end of the day, “garbage in,garbage out.” Humans need to be in the mix to provide the context
to understand what is considered “good” and bring their different
perspectives to the table.
39

LOOKING AHEAD
As we prepare for what’s next in the field, we’ve outlined four
themes for enterprises to focus on.

2021 STATE OF DATA SCIENCE REPORT40

LOOKING AHEAD

1PYTHON WILL CONTINUE TO
DOMINATE THE DATA SCIENCEFIELD AND BEYOND.
Python is a top programming language and is at the core of data science andscientific computing. It makes the entry to data science simple for students andbeginners and is used by experts around the world. In addition, its open-source
nature allows for continuous innovation in the supporting ecosystem of libraries
and packages, making the experience a positive one for makers and users alike.
Additionally, the Python community is very active. There are countless developer
forums for questions and troubleshooting and many libraries to enable diverse
workstreams. And with ongoing development in the open-source ecosystem,
there are countless ways for people to get involved.
Python already has a large and diverse user base that we see becoming anorganizational standard. Our CEO and co-founder, Peter Wang, believes Python
will be the successor to Excel since it lowers the barrier to entry in data science
across multiple business functions. By bridging the gap between technical roles
and business functions, we can improve data literacy and the way businesses
use data.

2

ENTERPRISES ARE READY
TO CONTRIBUTE TO OPENSOURCE INNOVATION.
Open-source software adoption is high. 87% of survey respondents in commercialorganizations indicated they are already using open-source technology today.
It is no secret that open-source maintainers and contributors have traditionallybeen hobbyists, but now, we’re seeing more employers encouraging their teams
to contribute to open-source innovation. Through resources like increased
funding related to open-source project development, additional time dedicatedto contributing, and team members dedicated to contributing to open-source
projects, we can expect even more innovation through open-source software.
Even as the past year has presented its share of challenges, only 18% of survey
respondents said that employer support for open source decreased due to
COVID-19 or other factors.
However, with the rapid growth of open source adoption comes challenges.
Open-source software presents some potential risks and vulnerabilities thatmake some companies anxious and drive decisions to use vendor software
instead. Fear of CVEs, possible exposures, or risks are the main obstacles toadoption. But of those who have adopted, a quarter of respondents said theirorganization does not take any steps to secure its open-source data scienceand machine learning pipeline, despite the availability of such solutions.
As recent news cycles and the recent US Executive Order have shown,cybersecurity attacks pose a serious threat to organizations today. For companies
that use open source or third-party software, supply chain verification of theircode will become increasingly important. It is possible to integrate open-source
software into a business’s tech stack in a secure way by following steps such as
regular CVE database checks, timely patching of vulnerabilities, and working withsecurely-built packages from the outset.

2021 STATE OF DATA SCIENCE REPORT41

LOOKING AHEAD

3

SENTIMENT TOWARD
AUTOMATION WILL CONTINUE
TO GROW.
Despite themes in the media suggesting automation will ‘take over,’it’s actually welcomed by data practitioners and isn’t seen as a competitor.
AutoML is valued for its ability to quickly and efficiently tune many
hyperparameters, help choose the best model types to solve specificproblems and enable non-experts to train ML models. Automation is seen as
a complement to work. Data scientists aren’t worried because they recognize
how many aspects of their job still require expert human judgment thattechnology can’t replicate. Additionally, since most AutoML frameworks today
are fairly generic, and as organizations look to integrate data science moreclosely with their particular business domains, they’ll need data scientists
to carry out more complex and specific work. AutoML can help free up
data scientists’ time spent on some of their simpler tasks so that they canconcentrate on these higher-level challenges.

4

PREVENTING BIAS AND
DEVELOPING ETHICAL DATASCIENCE IS CRITICAL.
For all the benefits that AI/ML have brought to business and society, we also
must consider the problems and challenges these tools have introduced andthe steps we can take to mitigate those issues. When asked what they viewas the most significant problem to tackle in the AI/ML area today, the mostpopular answer was the social impacts caused by bias in data and models.
It’s encouraging to see an increase in organizations planning to implement
at least one step in the next year. However, most individuals still don’t know
if their organization is taking any steps to ensure fairness and mitigate bias.
Additionally, students and universities aren’t prioritizing ethics and mitigating
bias, so it’s often an afterthought in the field.
Although bias in AI/ML data and models is top of mind for many practitioners,
the gap between awareness of the issue and active implementation ofsolutions suggests that there’s still work to be done in this area.

As we consider what’s next for the data sciencefield, we’re building a dedicated home for ourcommunity to gain expert insights, connect withexperts, and more. Check out Anaconda Nucleus
to continue your learning journey.

2021 STATE OF DATA SCIENCE REPORT42

ABOUT ANACONDA
With more than 25 million users, Anaconda is the world’s
most popular data science platform and the foundation ofmodern machine learning. We pioneered the use of Python fordata science, champion its vibrant community, and continue
to steward open-source projects that make tomorrow’s
innovations possible. Our enterprise-grade solutions enablecorporate, 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.

© 2021 ANACONDA INC. ALL RIGHTS RESERVED.