Internships vs Certifications: What Recruiters Value

The internships vs certifications question comes up constantly among students and career switchers, and online advice is split. For data roles, official job data gives an unusually clear answer. The US Department of Labor's O*NET database rates how important a professional certification is in each occupation and how much related experience the work requires (O*NET 31.0 database).

import pandas as pd

# O*NET 31.0: importance of a job-related professional certification (1-5) and required experience and training
edu = pd.read_csv("onet/Education.txt", sep="\t")
te = pd.read_csv("onet/Training and Experience.txt", sep="\t")
names = pd.read_csv("onet/Occupation Data.txt", sep="\t").set_index("O*NET-SOC Code")["Title"]

cert = edu[edu["Element ID"].eq("2.D.4.a")].set_index("O*NET-SOC Code")["Data Value"]
pct = cert.rank(pct=True)
show = ["15-2051.01", "15-2051.00", "15-1243.01", "15-1242.00", "15-1252.00", "15-1244.00", "15-1299.04", "13-2011.00"]
print(f"C1 importance of a professional certification (1 = not important, 5 = extremely), {len(cert)} occupations rated")
for code in show:
    print(f"   {names[code][:44]:<44}{cert[code]:>5.2f}   higher than {pct[code]:.0%} of occupations")


def share(code, element, cats):
    rows = te[te["O*NET-SOC Code"].eq(code) & te["Element ID"].eq(element)]
    return rows.loc[rows.Category.astype(int).isin(cats), "Data Value"].sum() / 100


print("\nC2 related work experience required (share of ratings), and on-the-job training expected")
for code in show[:4]:
    print(f"   {names[code][:44]:<44} up to 1 year {share(code, '3.A.1', range(1, 6)):>4.0%} | over 2 years {share(code, '3.A.1', range(7, 12)):>4.0%}"
          f" | on-the-job training over 6 months {share(code, '3.A.3', range(5, 10)):>4.0%}")

C1 importance of a professional certification (1 = not important, 5 = extremely), 683 occupations rated
   Business Intelligence Analysts               1.82   higher than 15% of occupations
   Data Scientists                              1.88   higher than 17% of occupations
   Data Warehousing Specialists                 1.74   higher than 11% of occupations
   Database Administrators                      2.28   higher than 36% of occupations
   Software Developers                          2.55   higher than 48% of occupations
   Network and Computer Systems Administrators  3.77   higher than 80% of occupations
   Penetration Testers                          4.35   higher than 88% of occupations
   Accountants and Auditors                     3.24   higher than 68% of occupations

C2 related work experience required (share of ratings), and on-the-job training expected
   Business Intelligence Analysts               up to 1 year   9% | over 2 years  77% | on-the-job training over 6 months  41%
   Data Scientists                              up to 1 year  56% | over 2 years  28% | on-the-job training over 6 months  33%
   Data Warehousing Specialists                 up to 1 year   0% | over 2 years  96% | on-the-job training over 6 months  30%
   Database Administrators                      up to 1 year   6% | over 2 years  83% | on-the-job training over 6 months  29%

Certifications rate low for data roles

On O*NET's scale from 1 (not important) to 5 (extremely important), a professional certification scored 1.82 for business intelligence analysts, 1.88 for data scientists and 1.74 for data warehousing specialists. That puts data roles below roughly 85% of the 683 rated occupations. Software developers scored 2.55, about the middle.

Compare that with roles where certifications genuinely act as gatekeepers: network and computer systems administrators scored 3.77 and penetration testers 4.35, higher than nearly nine in ten occupations. Accountants, where professional qualifications are part of the career, scored 3.24.

The reason is simple. In data work, employers can test skills directly in a SQL exercise, a case study or a take-home project, and they care about what you have built. A certificate adds little to a strong demonstration.

Experience rates high

The same database shows how much related experience the work requires. For business intelligence analysts, 77% of ratings said over two years; for data warehousing specialists, 96%. Data scientists were an exception: 56% of ratings said up to one year, reflecting how often the role is filled by people with advanced degrees, where study counts as preparation.

All four data occupations also expect substantial on-the-job training: 29% to 41% of ratings said more than six months. Employers expect to keep teaching you; they want proof you can do real work in the meantime.

Why internships win

An internship is real experience: real data, a real manager, real deadlines and a reference. It is also often a direct path to a job. In a 2026 survey of 284 US employers by the National Association of Colleges and Employers, 63.1% of eligible interns from 2024-25 were converted into full-time hires (NACE: intern conversion).

To get the most from one:

• Ask for a project with a visible result, such as a dashboard, an analysis that informs a decision, or a pipeline that runs.
• Write it up (without confidential data) for your portfolio and resume.
• Ask your manager for feedback mid-way, not only at the end.
• Say early that you would like a full-time offer if one is possible.

When a certification is worth it

Certifications are not useless for data careers. They help in specific situations:

• Cloud data engineering roles, where some employers use cloud certifications as a quick check of platform knowledge. See the AWS certification path for data roles.
• Career switchers who need a structured syllabus and a signal that they finished something.
• Getting through automated filters for postings that list a certification by name.
• Learning a platform systematically, when the certificate is a side effect of studying properly.

What a certification does not do is replace evidence. Pair any certificate with a project that uses the same skills.

How to judge a specific certificate

Before paying for a certificate, look at twenty real postings for the role you want and count how many name it. If only one or two do, the money and time are probably better spent on a project. Check who issues it: a certificate from the vendor of a platform you will use, such as a cloud provider, carries more weight than a generic "data science certificate" from an unknown provider. Check whether it has a proctored exam, because employers know that completion certificates for watched videos prove little. Finally, ask whether studying for it will teach you something you will use in your first months on the job. If the answer to all four is yes, the certificate is a reasonable investment; if not, build something instead.

If you cannot get an internship

Create internship-like experience: analyse data for a non-profit, a college department or a small business; contribute to an open-source data project; or join a structured project in a course, with a real dataset and feedback. Write each one up as you would an internship. Your first data job with no experience covers more routes in.

A simple rule

If you have to choose with limited time: internship first, a well-documented project second, certification third, unless you are targeting cloud or infrastructure roles, where a certification moves up. Whatever you choose, the evidence of what you built is what gets discussed in interviews.

Projects that count as real experience are part of the Data Analytics course.

Sources

• O*NET 31.0 Database (O*NET Resource Center) — O*NET Resource Center
• Intern Conversion Rate Hits Highest Mark in Five Years (National Association of Colleges and Employers) — National Association of Colleges and Employers