Prateek Kumar's Linkedin Analytics

**Top 10 Python Libraries Powering the Future of Trade Finance & Supply Chain** --- **Want to move beyond Excel in supply chain operations or trade finance?** Here are the **10 most powerful Python libraries** you should know if you're solving problems in logistics, documents, forecasting, or fraud risk: --- ### **1. pandas** The backbone of supply chain data — time series, stock levels, LC doc tables, delay logs, etc. ### **2. numpy** High-speed numeric computation — perfect for rolling KPIs, lead time volatility, and risk metrics. ### **3. plotly / matplotlib** Interactive & static dashboards: shipment delays, port trends, inventory heatmaps. ### **4. statsmodels** For classical models — ARIMA, trend decomposition (STL), or OLS for freight & pricing analysis. ### **5. Prophet** Used to forecast volume, demand, or delays — great for seasonality & calendar-driven data. ### **6. XGBoost / LightGBM** For anomaly detection, fraud pattern mining, or customs clearance prediction. ### **7. spaCy / transformers** Extract clauses from LCs, contracts, emails — power NLP in trade documents. ### **8. pdfplumber / pytesseract** Parse PDFs or scanned LC documents and generate structured outputs. ### **9. networkx** Visualize and optimize trade corridors, shipping routes, or supplier networks. ### **10. SQLAlchemy / DuckDB / Polars** Bridge Python with WMS/ERP data systems for blazing fast, scalable analysis. --- **These tools are quietly powering ops-first teams at banks, logistics firms, and trade platforms.** And they’re free. Open-source. Global. --- **Which one do you want to master next? Or which one helped you solve a hard ops problem recently?** Drop your pick in the comments. --- #SupplyChain #TradeFinance #PythonLibraries #GTSTLabs #DataScience #OperationalExcellence #NLP #Forecasting #AnomalyDetection #DocumentAI #SmartLogistics

This wasn’t about going viral or doing something flashy. It was about showing up — again and again — when someone needed a hand. This April, I was in the top 5% on Topmate for being steady, present, and helpful. Sometimes, that’s all it takes. A little consistency. A little care. And the willingness to keep answering the call. https://lnkd.in/dj-zGPbH

**Stop glamorizing models. Start fixing data.** “Data science is 90% data wrangling, 10% modeling, and 100% storytelling.” ### The Reality Most aspiring data scientists envision their careers as a whirlwind of cutting-edge algorithms and AI breakthroughs. In truth, many find themselves entrenched in data cleaning, navigating ambiguous business requirements, and striving to make sense of messy datasets. The real challenge isn't mastering the latest machine learning model—it's ensuring the data is accurate, relevant, and actionable. ### Why Conventional Thinking Falls Short * Overemphasis on tools: Many focus on mastering the newest tools and frameworks, neglecting the foundational understanding of the data itself. * Neglecting data quality: Without clean and well-structured data, even the most sophisticated algorithms yield poor results. * Misunderstanding the role: There's a common misconception that data science is solely about building models, overlooking the importance of data preparation and communication. ### What Truly Matters * Data understanding → Grasping the nuances, origins, and limitations of your data. * Effective communication → Translating complex data insights into clear, actionable business strategies. * Problem-solving mindset → Approaching challenges with a focus on finding practical solutions, not just technical elegance. ### A Closing Insight In data science, the most impactful professionals are those who bridge the gap between raw data and real-world decisions. It's not about the complexity of your models, but the clarity and relevance of your insights. ### Your Turn Have you encountered misconceptions about the data science role in your experience? Share your thoughts or stories in the comments below. Let’s demystify data science together. **#DataScience #CareerGrowth #DataAnalytics #MachineLearning #DataCleaning #BusinessImpact #AI #DataStorytelling #LinkedInCommunity #AnalyticsProfessionals**

**Distributions aren't academic theory. They define whether your KPIs are real or misleading.** **Real Use Case: Lead Time Distribution for a Trade Vendor** A global procurement team calculated *average lead time* as **6.2 days**. But here’s what the data actually looked like: - Most shipments arrived in 3–4 days - A few extreme delays stretched into 12–18 days - The **distribution was positively skewed** > The average didn’t reflect operations. > It only reflected *risk exposure*. --- **What Went Wrong?** - Safety stock buffers were miscalculated - Reorder logic assumed symmetric behavior - Planners didn’t flag suppliers with high lead time volatility --- **What to Use Instead:** - **Median** for central tendency - **IQR** for buffer planning - **Skewness** and **kurtosis** for supplier risk detection - **Boxplots + KDE plots** to *see the truth* ### **Python Tip:** ```python df['lead_time'].plot(kind='kde') print("Skew:", df['lead_time'].skew()) print("Kurtosis:", df['lead_time'].kurtosis()) ``` **If you’re not checking skewness before modeling or thresholding — you’re modeling blind.** #SupplyChainAnalytics #LeadTime #SkewedData #PythonForOps #GTSTLabs #InventoryPlanning #RiskMetrics #DistributionMatters

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**“Why Most Supply Chain AI Agents Fail to Collaborate — And How to Fix It”** *(Inspired by Google’s Agent2Agent Protocol – but made relevant for trade + logistics)* **Multi-Agent Systems Could Revolutionize Supply Chain Ops…** But right now? Most fail. Here’s why: ### **The Problem:** In a typical global supply chain, you may have separate “AI agents” for: - Inventory optimization - Demand forecasting - Route planning - Payment & document validation - Customs clearance logic But these systems don’t talk to each other. - **The result?** You get fragmented predictions, duplicated rules, and conflicting decisions. --- ### **The Solution: Agent-Oriented Coordination** Borrowing from Google’s *Agent2Agent* idea, here's how multi-agent orchestration could fix your ops: Meet the AI Agents of a Future-Ready Supply Chain 1. Planner → Forecasting Agent Sends daily demand projections based on market trends, weather, and point-of-sale signals. 2. Controller → Inventory Agent Uses forecast input to rebalance SKUs across DCs and stores — in real time. 3. Transporter → Routing Agent Schedules routes dynamically, pulling inventory priorities and delivery windows from other agents. 4. Auditor → Compliance Agent Monitors invoices, location data, and flag mismatches — like shipment claims from routes that didn’t exist --- **Imagine if these agents shared JSON-based capability cards, published APIs, and dynamically exchanged tasks.** **Result?** - Real-time rerouting when inventory changes - Early fraud alerts based on logistics data - Auto-triggered working capital actions. #SupplyChainAI #MultiAgentSystems #GTSTLabs #OperationalAutomation #TradeFinanceTech #PythonForOps #AgentCoordination #SmartLogistics

Definitely worth reading for 17+ LPA Job topics:- I always emphasize that surface-level learning is not enough to build a strong career. You need industry-level depth to truly stand out. ➡️ However, structured lists like this can give you a great starting point.⬅️ They help you create a mental map of what’s needed and make the journey feel less overwhelming. Let this serve as motivation, but remember, real success comes when you go beyond checklists and apply these concepts in real scenario.

I spent ~5 years mastering data analytics. Here's the shortcut to save you countless hours... 🔍 Are you just starting out in data analytics or looking to polish your skills? Here’s a little nugget of wisdom: It’s not about knowing every tool and technology available; it’s about mastering the fundamentals. Let me break it down for you: 1. Focus on SQL: Understanding databases is crucial. Get comfortable with writing queries! It’ll help you pull the exact data you need without pulling your hair out. 2. Dive into Python: It might seem intimidating, but trust me, it’s the Swiss Army knife of data analysis. You can automate tasks and manipulate data like a pro once you get the hang of it! 3. Data Visualization: This isn’t just for pretty charts—it’s essential for storytelling with your data. Tools like Tableau or Power BI can transform your insights into impactful visuals that everyone understands! 4. Statistics & Probability: Understanding these concepts will set you apart from others who only know how to use tools without grasping what they’re doing! Know why methods work, not just how to execute them! Practice Makes Perfect: Work on real projects—whether they're through internships, freelancing, or personal projects—apply what you've learned and watch your confidence soar! I wish someone had told me this when I was starting out... If you're serious about becoming a top-notch analyst in the financial services space, prioritize these areas and watch how fast you'll progress! 🚀 What are some skills you're focusing on right now? Let’s share knowledge in the comments below! #DataAnalytics #DataScience #SQL #Python #DataVisualization #PowerBI #Tableau #Statistics #Probability #AnalyticsCareer #DataAnalyst #StorytellingWithData #LearningPath #FinanceAnalytics #CareerGrowth #SkillUp #RealWorldSkills #DataDriven #UpskillYourself

7 Credit Card Fraud Types You Must Know (And How They Actually Work) Most people *know* fraud happens. But very few understand *how* it actually works. Let me break it down with real fraud modus operandi (MO) examples. 1. First-Party Fraud (Fake Intentions, Real Identity) ↳ Customer applies for a card with no plan to repay. Looks legit, but was fraud from day one. 2. Synthetic Identity Fraud (Frankenstein IDs) ↳ Fraudsters stitch fake + real details (like a real SSN with fake name) to create a new identity. 3. Third-Party Fraud - Identity Theft (IDT) ↳ Criminals steal your real details to apply for a card. You find out only when bills arrive. 4. Account Takeover (ATO) Fraud (Your Card, Their Control) ↳ Fraudsters gain access to your card account via phishing, social engineering, or hacking. 5. Counterfeit Card Fraud (Physical Duplication) ↳ Your card data is cloned using skimming devices and used to make physical purchases. 6. Card Not Present (CNP) Fraud (Online Purchase Scams) ↳ Stolen card numbers used online without needing the physical card. Common in e-commerce. 7. Friendly Fraud (It’s Not That Friendly) ↳ Genuine customers falsely claim “I didn’t do this transaction” to reverse charges after using the service. 💡The Connection You Missed: Credit cards are trust-based products. They drive the economy, convenience, and credit access. But that same ease of use makes them a prime fraud target. Understanding the MO behind each fraud type is how fraud strategists protect both consumers and businesses. Which fraud type surprised you the most? Or have you experienced any of these personally? Comment below or share this with your network to spread awareness. #CreditCardFraud #FraudAwareness #FraudRiskManagement #FintechRisk #FraudPrevention #CyberSecurity #RiskStrategy #IdentityTheft #FraudDetection #CardNotPresentFraud #AccountTakeover #DataSecurity #FinancialCrime #FintechCommunity #PaymentsIndustry

If You Still Think Logistics = Supply Chain… You’re Optimizing the Wrong Problem. **Logistics ≠ Supply Chain** Mistaking one for the other is why some teams optimize routes… while missing the *real bottleneck* in planning, procurement, or working capital. **Let’s Break It Down:** **Logistics = Movement** - Trucks, routes, last-mile delivery - 3PL/4PL coordination - Carrier contracts - WMS + TMS execution **Supply Chain = System** - Procurement - Inventory policies - Forecasting - Trade finance - Freight, customs, compliance - Risk, ESG, supplier health > **Logistics asks: How do we get it from A to B?** > **Supply Chain asks: Should we even ship it? From where? Why now?** --- **Real Example (Global Distributor):** Spent **$22,000/year** optimizing fleet routes. But the real *supply chain* issues were: - Forecast off by 20% - Lead time volatility ignored - Reorder logic never adjusted post-COVID **Result:** Routes got faster. Wrong goods still arrived. At the wrong time. ### **Python Helps in Both — But Differently** **In Logistics:** `networkx`, `geopandas`, `OSRM`, `plotly` → Route optimization & visibility **In Supply Chain:** `Prophet`, `statsmodels`, `XGBoost`, `pandas` → Forecast drift detection, delay prediction, anomaly detection #SupplyChain #Logistics #PythonForOps #GTSTLabs #OperationalStrategy #Forecasting #TradeFinance #SmartLogistics

5 Hard Truths About the Data Analyst Role (Timing is Everything): 1. **The right insight delivered too late is worthless.** ↳ When analysis misses the decision window, it might as well not exist. Often, a good answer now beats a perfect answer later. 2. **Bad analysis is worse than no analysis.** ↳ A flawed report can mislead the team completely. Quality and accuracy aren’t optional — they define our reputation. 3. **Data Analyst ≠ Data Scientist (or Data Engineer).** ↳ A data analyst’s focus is turning raw data into immediate value. Unlike data scientists who build models or data engineers who maintain pipelines, we deliver quick, actionable insights on business timelines. 4. **We don’t just answer questions — we question them.** ↳ A good analyst digs into the “why” behind every request. We reframe problems and challenge assumptions to make sure we’re solving the right problem, not just the asked one. 5. **If it doesn’t drive action, it doesn’t matter.** ↳ For a data analyst, an insight only matters if it leads to action. We measure success by decisions made, not by reports created. Bottom line: even a brilliant analysis delivered too late becomes just trivia. Real impact comes from delivering accurate insights at the speed of decision-making. Which of these points resonates most with you? What other tough truth would you add about being a data analyst? 🔖 Save this post for later and 🔄 share it if you found these insights useful — someone in your network might need to see this! #DataAnalysis #DataScience #DataScientist #Dashboard #AnalyticsCareers #DataAnalysisMatters #DecisionMaking #DataDrivenDecisions #RealWorldAnalytics

2 years ago, I used to struggle with SQL case studies like this Amazon membership query. Today, I solve them with clarity, speed, and optimization mindset. Let me walk you through a real Amazon SQL interview-style problem to show you how. You are asked to report which shipments were delivered during the customer’s membership period. Given Tables:- 1. customers * customer_id (INTEGER) * membership_start_date (DATETIME) * membership_end_date (DATETIME) 2. shipments * shipment_id (INTEGER) * ship_date (DATETIME) * customer_id (INTEGER) * quantity (INTEGER) The difference? Most stop at getting the output. I focus on correct logic and optimization. Before improvement: * Many skip showing the schema, which confuses beginners. * Most just paste the SQL query without explaining the problem. * Optimization is completely ignored. After improvement: * Clear context with table structures and requirements. * Step-by-step logic before presenting the query. * Discussing optimization by using appropriate joins and comparisons. The Logical Approach:- 1. Join the shipments table with the customers table on customer_id. 2. Compare if ship_date falls between membership_start_date and membership_end_date. 3. Flag as 'Y' or 'N' using CASE WHEN. Optimized SQL Query: SELECT s.shipment_id, s.ship_date, s.customer_id, CASE WHEN s.ship_date BETWEEN c.membership_start_date AND c.membership_end_date THEN 'Y' ELSE 'N' END AS is_member, s.quantity FROM shipments s JOIN customers c ON s.customer_id = c.customer_id; Why this works:- 1. Join ensures we only work with valid registered customers. 2. CASE WHEN with BETWEEN makes the logic readable and accurate. 3. Direct column selection keeps it performance-friendly. Think of this like checking your train ticket validity. If your ticket date covers your journey date, you travel. If not, you’re denied entry. Simple, clear, verifiable. Most people think SQL is just about syntax. The reality is it’s about clarity, business context, and execution efficiency. Want more real-world SQL scenarios explained like this? Drop a “SQL” in the comments and I’ll share another breakdown soon. #sql #datascience #interviewpreparation #analytics #amazon #businessanalytics

LIFE SAVERS YOU WISH YOU KNEW EARLIER IN CORPORATE LIFE Sharing this as a small piece of advice for all my juniors and peers who are just starting their corporate journey. We all learn these the hard way, but here’s something that might save you from future surprises: 1. Always get work confirmation on Mail or Teams Chat. Verbal discussions often lead to confusion. Written confirmation protects both sides and keeps expectations clear. 2. Never agree to unrealistic expectations on the spot. If you’re unsure, it’s absolutely okay to say you’ll need some time to think it through. Blind commitments only create pressure later. 3. Keep your manager updated, even if you don’t meet often. A quick status update can go a long way in building trust and showing ownership. 4. Be careful when you hear, "Feel free to take leave if you need it." It sounds supportive in the moment, but if work gets affected, that same kind suggestion might turn into negative feedback later. Always balance your time off with work impact. These might sound simple, but practicing them consistently can help you build credibility and stand out as a reliable team member. What’s one lesson you learned the hard way? I’d love to hear your experience in the comments. #CorporateLife #WorkplaceLearning #CareerGrowth #RealTalk #TrustBuilding