# Practical Data Analysis (@practical.data.analysis) on tiktok

- id: koinon:creator:tiktok_practical.data.analysis
- url: https://koinonlink.com/kol/tiktok/practical.data.analysis
- schema_version: 1.4

## Identity
- platform: tiktok
- handle: practical.data.analysis
- platform_url: https://www.tiktok.com/@practical.data.analysis
- country: United States
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 4221
- following: 44
- posts_total: 15
- posts_observed: 1
- verified: false
- size_tier: nano

## Engagement
- status: not_collected
- status_note: Engagement has not been collected for this creator yet. This is a gap in our collection, NOT a statement that the creator has no engagement. Follower count is known, so it is measurable.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: software
- subcategory: ai_tools
- category_confidence: 0.9
- category_source: derived_from_recent_content
- topics: Python and VS Code for data analysis, ChatGPT and Code Interpreter workflows, Machine learning and clustering basics, Excel tips and data cleaning, SQL, ETL and data engineering intro
- bio: Modern Data Analysis at Your Fingertips
Machine Learning, SQL, Excel
Python, C#

## Audience (inferred)
- status: present
- description: aspiring data analysts and students learning Python, Excel, SQL and AI tools like ChatGPT for data tasks
- roles: data
- seniority: learner
- pain_points: struggling to learn practical Python and data analysis skills outside formal education, figuring out how to use ChatGPT/Code Interpreter effectively on real datasets, improving Excel efficiency and discovering useful shortcuts, preparing for data analyst interviews and building portfolio projects
- posts_analysed: 18
- confidence: 0.6

## Commercial (inferred)
- audience_buyer_fit: 0.8
- promotion_intent: 0.7
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 39
- authenticity_basis: profile_shape_only
- credibility_score: 39
- flagged_for_review: false
- koinon_bd_grade: C — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [tiktok_practical.data.analysis#11e9e83f] #codeinterpreter #chatgpt #machinelearning #datascience #clustering #datascience #dataanalytics #vscode #python (https://www.tiktok.com/@practical.data.analysis)
- [tiktok_practical.data.analysis#6ed43d83] original sound - practical.data.analysis
- [tiktok_practical.data.analysis#807f275a] It is a becoming a big trend on TikTok now! Click here: codeinterpreter
- [tiktok_practical.data.analysis#41587443] Check out Practical Data Analysis’s video! #TikTok >
- [tiktok_practical.data.analysis#0ba96709] Data science - logistic regression #datascience #codeinterpreter #python #dataanalytics #chatgpt #machinelearning #llm
- [tiktok_practical.data.analysis#bb2e47a3] #codeinterpreter #datascience #machinelearning #chatgpt #dataanalytics #python
- [tiktok_practical.data.analysis#420f4486] I wish I found this book before I finished grad school. I'm legitimately starting to read through it now, and might even do some study sessions with followers on TikTok live (if there is interest 🤔) #machinelearning #datascience #dataanalytics #math #mathematics #computerscience
- [tiktok_practical.data.analysis#0434fc4a] Coffin Nails

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.6, freshness_days=69, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.6, freshness_days=69, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.6, freshness_days=69, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=69, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=69, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=93, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.25, freshness_days=69, method=Profile-shape heuristic from a single snapshot (follower count, following ratio, account age, posting volume, verification), reduced for engagement red flags where post engagement was collected. Where only a follower count is available the score saturates at 58. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=93, method=llm_partner_fit_classifier + fit_score >= 0.6 AND authenticity >= 60 => A; fit_score >= 0.4 AND authenticity >= 45 => B; else C. authenticity < 30 or a promotional-pattern flag => quarantine. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=93, method=platform_profile

## Freshness
- profile_checked_days_ago: 93
- content_analysed_days_ago: 69
- audience_updated_days_ago: 69

## AI retrieval
- indexing_requests: 6
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=tiktok_practical.data.analysis
- Request removal: https://koinonlink.com/claim?k=tiktok_practical.data.analysis
- Terms: https://koinonlink.com/for-ai