Data analysis & statistics
Taking a real dataset — census, NSSO, a household survey, ward-level municipal data — and getting a defensible finding out of it. The analytical spine of think-tank, policy, research-fellowship and multilateral work, where the deliverable is a claim someone will argue with.
Needed across Corporate ESG / Sustainability, PhD Abroad (US / UK / EU / Australia), PhD (India) — SPA / CEPT / IIT / Planning Schools, Urban Policy / Governance / Fellowships, Research Fellow — NIUA / Think Tanks / Funded Projects, Urban Think Tank / NGO (WRI India, NIUA, ICLEI, C40), World Bank / ADB Urban & Infrastructure Roles
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Course outline
Learn from selected clips, concept by concept
Concept 1
What a dataset can and cannot tell you
Descriptive Statistics vs Inferential Statistics
The Organic Chemistry Tutor · English · beginner
Why this clip: Illustrates the distinction between describing observed sample data and making inferential claims about a larger population.
What Are Observational And Experimental Studies In Statistics - Types Of Studies Explained
Whats Up Dude · English · beginner
Why this clip: The segment contrasts experimental and observational setups using a numerical example of resting heart rates.
Concept 2
Types of variable, and why the type decides everything
After: what-the-data-can-answer
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Concept 3
Getting a messy dataset into shape
After: variables-and-measurement
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Concept 4
Describing one variable honestly
After: variables-and-measurement
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Concept 5
Comparing groups with cross-tabs
After: describing-a-distribution
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Concept 6
Charting so the finding survives the slide
After: comparing-groups
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Concept 7
Samples, populations and who got left out
After: describing-a-distribution
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Concept 8
Putting error bars on an estimate
After: sampling
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Concept 9
Testing a claim, and what a p-value is not
After: uncertainty-and-intervals
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Concept 10
Correlation, and the sentence you must not write
After: charting-a-finding
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Concept 11
Regression, and reading a coefficient out loud
After: correlation, hypothesis-testing
Video title and channel appear once unlocked.
What was rejected
17 candidates did not meet the course criteria.
- Framed around study design classification rather than explicitly analyzing dataset constraints.
- Focuses mainly on calculating basic statistical metrics rather than exploring dataset limitations or research design.
- Does not discuss or show how variable types determine which charts or visualizations are legitimate.
- Lists the types as vocabulary to memorise with no consequence attached
- This video is mostly a conceptual lecture on tidy data principles rather than a hands-on tutorial demonstrating actual data cleaning work on a dataset.
- Is a pure Excel-formula tutorial (TRIM/VLOOKUP tips) with no dataset reasoning
- Demonstrates the Excel PivotTable interface only, with no discussion of what the comparison means; or presents raw counts across unequal group sizes without normalising
- Does not explain how to match specific chart types (histograms, scatter plots, bar charts) to variable types.
- Is a software feature tour (how to click through Excel/Power BI/Tableau chart menus) with no judgment about which chart is right
- lists sampling designs as exam definitions with no discussion of bias
- Does not explain what factors drive the width of the confidence interval (such as sample size or variability).
- Is z-table / t-table formula drilling for an exam with no interpretation
- Is z-table / t-table formula drilling for an exam with no interpretation
- Does not explicitly address common misinterpretations or clarify what a p-value is not.
- The video does not explain p-value or what p-value is not, postponing p-value to the next video.
- Mentions causation only in a brief throwaway line at the end without working through a confounder example.
- Does not explain the strength or direction of association or how to read values off a scatter plot.
Where this skill is used
Corporate ESG / Sustainability
In-house ESG/sustainability roles at corporates — reporting, GHG accounting, sustainability strategy — a fast-growing pivot for Environmental-specialisation planners. Specialisation tag: Environmental.
8 mapped employers
Explore path →PhD Abroad (US / UK / EU / Australia)
A funded doctorate abroad — the strongest credential for international academia and the multilateral international-staff track. Competitive, but a clear route for top students. Specialisation tags: all.
0 mapped employers
Explore path →PhD (India) — SPA / CEPT / IIT / Planning Schools
A funded doctoral path at Indian planning/architecture/IIT departments — a gateway stage (not a salaried career) that feeds academia and senior research/development-sector roles. M.Plan is the standard prerequisite. Specialisation tags: all.
0 mapped employers
Explore path →Urban Policy / Governance / Fellowships
Policy and governance roles — state policy units, legislative-support fellowships (e.g., LAMP), urban-governance programmes, and civic-tech — applying a planning lens to policy design and implementation. Specialisation tags: Regional, Urban.
8 mapped employers
Explore path →Research Fellow — NIUA / Think Tanks / Funded Projects
Structured research fellow/associate posts on funded urban projects — a salaried applied-research path that overlaps with the development sector and can precede a PhD or progress into senior research leadership. Project-funded roles do not use UGC-NET as a planning eligibility gate. Specialisation tags: all.
0 mapped employers
Explore path →Urban Think Tank / NGO (WRI India, NIUA, ICLEI, C40)
Research and programme roles at urban think tanks and NGOs — the standard first rung into the broader development sector, doing applied research, pilots, and city partnerships. The most reachable development-sector entry for fresh M.Plan grads. Specialisation tags: Urban, Transport, Environmental.
9 mapped employers
Explore path →World Bank / ADB Urban & Infrastructure Roles
Urban, transport, and infrastructure work at the multilateral banks — project preparation, technical assistance, and analytics, usually entered via consultant/STC contracts or (rarely, very competitively) the Young Professionals Program. Specialisation tags: Urban, Infrastructure, Transport.
9 mapped employers
Explore path →