Avdhesh Johri — Portfolio

Projekte

Zertifiziert

Selbstgesteuertes Projekt

Business Analytics Sprint

Data Science · Juli 2026

72/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a structured profitability investigation end to end: framed testable hypotheses, mapped KPI drivers correctly, and quantified the margin collapse behind the revenue/profit paradox. The work then translated analysis into action with a prioritised recovery plan, stakeholder-ready messaging, and explicit validation of AI-generated conclusions against source logic.

Bewertet nach

Problem Framing & Hypotheses10%
Data Analysis & SQL30%
Root-Cause & Forecasting25%
Dashboard & Visualization15%
Executive Communication & Strategy20%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Framed three plausible causes up front and tied each to a testable business signal.
Mapped all eight KPI drivers correctly, showing clear separation of price, cost, and unit levers.
Identified the profit paradox with concrete numbers: revenue +17% while profit fell 55% and margin dropped from 13.4% to 5.2%.
Prioritised actions in a business-aware way, focusing first on logistics, raw-material costs, and discount governance.
Structural Hypothesis Framing
KPI Driver Mapping

Meine eingereichte Arbeit19 Aufgaben

11 Aufgaben · 5.3k Zeichen · 8 Textantworten · 397 Wörter

Hypothesis Sheet949 Zeichen
KPI Driver Map174 Zeichen
Week1 Update482 Zeichen
Sql Category Profit310 Zeichen
Sql Region Join359 Zeichen
Dashboard47 Zeichen
AI Reflection370 Zeichen
Recommendations1.0k Zeichen
Stakeholder Qa548 Zeichen
Bosch Real Case Study701 Zeichen
Teach Back358 Zeichen
Hypothesis Sheet113 Wörter
KPI Driver Map1 Wort
Week1 Update61 Wörter
Sql Category Profit32 Wörter
Sql Region Join41 Wörter
Dashboard1 Wort
AI Reflection51 Wörter
Recommendations97 Wörter

Bewusst zusammengefasst — der eingereichte Code und die Texte bleiben privat, damit diese Seite nicht als Lösungsvorlage dient. Die vollständige Einreichung steht hinter dem verifizierten Zertifikat.

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Business & Sponsorships — The FC Barcelona Case

Data Science · Juli 2026

88/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a data-backed sponsorship case study for FC Barcelona: calculated revenue mix, segmented fans by region, matched sponsor objectives to the right commercial strategy, and evaluated campaign KPIs against target. Delivered a clear renewal recommendation for ElectraX that balanced strong awareness and traffic results against a specific content-reach shortfall, showing practical commercial judgment and honest reporting.

Bewertet nach

Business reasoning & decision quality35%
Correct use of data25%
Frameworks applied20%
Communication & structure20%

Bestanden · Bestehensgrenze 55/100

Was herausstach6

Correctly identified commercial revenue as the stream the club can directly scale
Computed the revenue mix accurately and translated it into a clear share-of-total view
Flagged content reach as the true laggard KPI while still reporting the stronger metrics honestly
Recommended a value-based renewal for ElectraX while explicitly tying the decision to measured outcomes and caveats
Leakage-Free Commercial Analysis
Honest KPI Evaluation

Meine eingereichte Arbeit16 Aufgaben

8 Aufgaben · 9.0k Zeichen · Python · 8 Textantworten · 791 Wörter

Revenue MixPython · 1.3k Zeichen
Revenue Mix Compute340 Zeichen
Fan SegmentationPython · 1.0k Zeichen
Negotiation Simulator486 Zeichen
Inbox Responses1.4k Zeichen
KPI ScorecardPython · 1.1k Zeichen
Mid Season Performance Summary1.1k Zeichen
Final Recommendation2.3k Zeichen
Revenue Mix101 Wörter
Revenue Mix Compute46 Wörter
Fan Segmentation53 Wörter
Negotiation Simulator64 Wörter
Inbox Responses160 Wörter
KPI Scorecard83 Wörter
Mid Season Performance Summary132 Wörter
Final Recommendation152 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Introduction to Tokenized Real-World Assets

Produkt · Juli 2026

84/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a clear beginner-friendly explanation of tokenized real-world assets and made a sensible first product recommendation: a tokenized Treasury fund. The submission shows the candidate can explain the token as a claim on assets held by a custodian, distinguish yield-bearing Treasury exposure from a bare stablecoin, and identify redemption risk in plain language.

Bewertet nach

Product choice & reasoning40%
Grasp of the basics35%
Risk awareness25%

Bestanden · Bestehensgrenze 55/100

Was herausstach6

Chose a tokenized Treasury fund as the sensible first product and explained the choice in everyday language
Described the token as a claim on real Treasury assets held and managed by a fund, not as the asset itself
Named redemption delay/restriction as a real risk and connected it to the product’s exit mechanics
Leakage-Free Concept Mapping
Plain-Language Financial Explanation
Risk-Aware Product Selection

Meine eingereichte Arbeit12 Aufgaben

6 Aufgaben · 2.3k Zeichen · 6 Textantworten · 231 Wörter

Coat Check94 Zeichen
Tokenization Demo344 Zeichen
Product Sort349 Zeichen
Depeg Sim330 Zeichen
Risk Match347 Zeichen
First Recommendation838 Zeichen
Coat Check1 Wort
Tokenization Demo32 Wörter
Product Sort17 Wörter
Depeg Sim29 Wörter
Risk Match24 Wörter
First Recommendation128 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV