All creators
Shashank Kalanithi
Data science careers and analytics with a focus on practical workflows and industry reality.
Nutrition Label
Shashank provides a mix of practical data science tutorials and broader industry commentary. While his deep dives offer strong professional context and evidence, his casual project videos often remain surface-level.
Strengths
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Notes
- !Explainer videos offer significantly more professional insight than his casual project vlogs.
- !Verify specific code implementations against official documentation as technical depth varies.
Rating Breakdown
Experience Authenticity
5.8Rigor & Evidence
5.5Original Analysis
5.8Technical Depth
5.5Disclosure Clarity
7.5Title-Content Alignment
5.8Expertise Signal
5.8Communication Effectiveness
5.8Breakdown across the key dimensions we rate. Methodology →
Recent Videos
Scored
Using Code and GPT-3 to Learn Faster
Feb 19, 2023 • 10.8K views
Pending
Data Analyst MENTORSHIP - Q&A (while I drink coffee)
Nov 4, 2022 • 4.4K views
Scored
How Data Science ACTUALLY Works
Nov 1, 2022 • 94.0K views
Scored
Does Instagram think you live in an influential city? | A deep dive into web data
Oct 25, 2022 • 5.8K views
Pending
Data Analyst MENTORSHIP - Q&A (while I drink coffee)
Oct 7, 2022 • 3.2K views
Pending
Data Analyst MENTORSHIP - Q&A (while I drink coffee)
Sep 30, 2022 • 2.8K views
Pending
Data Analyst MENTORSHIP - Q&A (while I drink coffee)
Sep 16, 2022 • 2.5K views
Scored
How I start Data Science Projects | What to do when you're stuck
Sep 13, 2022 • 18.4K views
Pending
Is the Meta Data Engineering Certificate any good? (as a Data Engineer)
Sep 10, 2022 • 51.1K views
Pending
Data Analyst MENTORSHIP - Q&A (while I drink coffee)
Sep 9, 2022 • 2.3K views
Pending
Data Analyst MENTORSHIP - Q&A (while I drink coffee)
Sep 2, 2022 • 2.3K views
Pending
Data Analyst MENTORSHIP - Q&A (while I drink coffee)
Aug 26, 2022 • 2.7K views
Pending
A Data Crash Course | 100+ Key Data Concepts
Aug 20, 2022 • 12.5K views
Pending
Data Analyst MENTORSHIP - Q&A (while I drink coffee)
Aug 19, 2022 • 2.6K views
Pending
Why do Data Engineers Exist?
Aug 10, 2022 • 6.0K views
Why this rating
Evidence receipts showing why each dimension is rated the way it is.
Transparency5/10
“Explicit verbal disclosure: "This video is sponsored by Deepnote." (Also disclosed in description).”[02:20] →
Experience Authenticity5/10
“Demonstrates the 'Data Engineer' role by writing actual Python code to clean the dataset, showing the specific friction of handling null values.”[09:50] →
Expertise Signal5/10
“Accurately satirizes the vague requirements often faced in industry: "I need to know if we're going to make money next year... just get me the numbers."”[00:30] →
Technical Depth5/10
“Demonstrates specific technical workflow for handling a '40 GB Dataset', addressing the memory constraints and cleaning requirements of large-scale web data.”[19:32] →
rigor_and_evidence5/10
“Visualizes the processed data in a custom dashboard, connecting the raw 40GB input to the specific question about city influence posed in the title.”[39:10] →
Title-Content Alignment5/10
“In this video I work with REAL Instagram data and show you how you can start your analyses for any Data Science projects”[Description] →
Categories
Personal FinanceInvestingStock MarketBankingRisk Management
Formats
TutorialsExplainers