Data Analyst Certificate Tier List

Product being reviewed

The video is a ranked “tier list” of data analytics certificates (mainly Coursera-style and similar programs), aimed at beginners. It evaluates each certificate with pros/cons and a letter grade tier.

Overall verdict

For most beginners, the creator repeatedly steers viewers toward certificates that are practical, job-relevant, and finishable quickly. They call the original Google Data Analytics certificate weak, while strongly recommending:

• Microsoft Power BI Analyst (PL-300 pathway)
• Google Advanced Data Analytics

Tier list & key points (unique mentions)

1) Google Data Analytics (original)

• Cons: “Completely useless” for job readiness; doesn’t teach most of what current data analyst jobs require.
• What it covers (per video): long mindset discussions; very basic SQL and Google Sheets.
• Job impact: if you only have this certificate, you won’t have anywhere near required skills (estimated ~5% or a tenth).
• Misconception about length: marketed as ~6 months, but the creator says serious learners should finish in 2–3 weeks.
• Pros: light introduction to what data analysts do; helps understand the field.
• Verdict/tier: C tier.

2) IBM Data Analyst (Python-focused)

• Pros: useful content; more practical than the Google version; heavy focus on Python.
• Why Python matters (as stated): called the most valuable programming language after SQL (with a minor aside about SQL not being a “programming language”).
• Cons: teaches “Cosmos” for visualization; creator thinks it’s less valuable than Tableau or Power BI (more in-demand for resumes).
• UI/quality gripe: IBM presentations look dated/like YouTube from 2010 (but content is good).
• Verdict/tier: A tier.

3) IBM Data Analytics with Excel and R

• Pros: shorter (3 months, 10 hrs/week); good for Excel and R.
• Cons: creator personally dislikes R and says beginners should learn Python instead.
• Verdict/tier: C tier.
• Recommendation nuance: “Take it” if you want Excel + R; otherwise skip.

4) Tableau Business Intelligence Analyst

• Pros: Tableau is highly in demand; overlaps with BI roles; includes projects and portfolio building using Tableau.
• Cons: not the best starting point; creator wants beginners to learn SQL and Python first.
• Verdict/tier: A tier.

5) UC Davis Data Visualization with Tableau

• Pros: shorter, so it’s easier to start/finish; still viable if you’re time-limited.
• Cons: less focused on data analytics; more about general Tableau skills (less “job-ready data analyst” alignment).
• Verdict/tier: not explicitly letter-graded in the subtitles, but positioned as a “good option” due to shorter length and lower overwhelm.

6) Meta Marketing Analytics

• Pros: marketing analytics is interesting; if you want to become a marketing analyst, there are jobs.
• Cons: not a good general data analytics course—first part is generic data analytics, then it becomes mostly marketing-specific.
• Specialization caveat: creator supports specialization, but says start broader unless you’re sure you want marketing analytics.
• Verdict/tier: B tier (with note: “S tier” if you’re into marketing analytics).

7) Google Advanced Data Analytics

• Pros: much better than the original Google certificate; focuses more on Python and practical data skills.
• Difficulty framing: “Not that advanced,” but harder than the original.
• Cons: doesn’t teach SQL (needs supplementation). Also likely needs Tableau/Power BI elsewhere.
• Extra option: ML-heavy courses later in the certificate may be “advanced/overkill” for data analyst goals, but can be taken if time allows for a possible data science path later.
• Verdict/tier: S tier.

8) Microsoft Power BI Analyst

• Pros: creator’s “personal favorite”; very practical and job-oriented; deep focus on Excel and Power BI.
• Market validation: mentions ~500,000 students and “top ratings.”
• Career value: prepares for Microsoft’s official PL-300 exam; provides a 50% discount.
• Cert value: described as possibly the most valuable data analytics certification for beginners.
• Cons (only if preference mismatch): not ideal if you prefer Tableau instead.
• Verdict/tier: S tier.

9) UniLever Supply Chain Data Analyst

• Pros: good if you’re sure about supply chain analytics; specialization helps you stand out; more openings/opportunities in supply chain analytics.
• Cons: “Terrible for beginners” who are still figuring their path.
• Verdict/tier: tiered as “a tier” (not clearly defined letter; described as a “very good” specialized option).

10) UniLever Digital Marketing Analyst

• Pros: largely overlaps with what general data analytics courses teach (dashboards, clicks, conversions).
• Cons: not as important as general programs for most learners.
• Verdict/tier: B tier.

11) IBM Data Analysis and Visualization Foundations

• Pros: implicitly uses Excel (which is fine).
• Cons: creator sees “no reason” to take it; uses Cognos, which they don’t recommend; suggests taking a different certificate focused on Power BI or Tableau instead.
• Verdict/tier: D tier.

12) University of Michigan: Data analytics in the public sector with R

• Pros: useful for those targeting public policy/government roles; uses data to inform public decisions.
• Cons: not for beginners; requires prior programming/data knowledge. Useful for a small segment (“not 99% of people”).
• Verdict/tier: B tier.

13) Duke University: Data Analysis with R

• Pros: strong if choosing R vs Python; teaches deeper theory like probabilities/statistics and math concepts often skipped in short online certificates.
• Why university matters (stated): universities are good for theory; creator has taken Duke math courses before and recommends them.
• Cons: none explicitly stated beyond it being theory-focused.
• Verdict/tier: “a tier” (letter not clearly shown in subtitles; appears better than B, but not explicitly S/A in the provided text).

14) Wesleyan University: Data analysis and interpretation

• Format: “four course introduction to data science,” completes in 4 weeks.
• Pros: short and accessible.
• Cons: nothing special; creator says you’re better off with other options already mentioned.
• Verdict/tier: B tier.

Repeated evaluation criteria / user experience themes (unique points)

• Finishability matters: long “6–8 month” courses can cause people to never start/finish; creator prefers short courses.
• Resume alignment: skills employers actually look for should dictate learning.
• Practical stack preference: SQL + Python + (Power BI/Tableau) prioritized over niche tools.
• Specialization vs generalization:
  • general beginner track first (for most)
  • specialized tracks (marketing/supply chain/public sector) only if you’re sure of that path

Comparisons made

• Google Data Analytics vs “more practical modern needs”: Google is outdated/insufficient for today’s job requirements.
• IBM Data Analyst (Cosmos) vs Tableau/Power BI: Cosmos is viewed as less valuable than Tableau or Power BI.
• Power BI vs Tableau preference: Power BI certificate praised; Tableau preferred if that’s your tool preference (Power BI option would be a “horrible idea” otherwise).
• General data analytics vs marketing-specialized: marketing certificate is considered unnecessary unless targeting marketing analytics.

Speakers/views

• The subtitles appear to reflect one main speaker who assigns all tiers and gives personal recommendations.
• No distinct additional speakers are clearly identified in the provided subtitles.

Final recommendation (concise)

• Best picks for most beginners: Microsoft Power BI Analyst (S tier) and Google Advanced Data Analytics (S tier).
• Good option with a specialization or portfolio route: Tableau Business Intelligence Analyst (A tier).
• Avoid / not recommended as a first path: Google Data Analytics (C tier) and IBM Data Analysis and Visualization Foundations (D tier).
• Only choose specialized tracks if you’re sure: marketing, supply chain, or public sector analytics.