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This D2C Marketing course is designed to give you hands-on experience with Meta and Google Ads, not just theory. You’ll learn by running real campaigns on live businesses, understanding budgets, creatives, targeting, and optimisation in real time. High-performing participants will also get opportunities for internships and job roles, based on performance and intent.

Systematic Mean Reversion & Cointegration: From Statistical Tests to Trade Execution (Recording) Most people talk about mean reversion like it’s a buzzword. In reality, it’s one of the few phenomena in markets that consistently scales across stat-arb, relative value, pairs trading, and high-frequency market making — but only if you know how to test it, validate it, and execute it without bleeding edge, spread, and slippage. This session drills straight into the mechanics of mean reversion and cointegration from a practitioner who’s built and traded these models at places likes of Citadel, and Exodus Point. Speaker: Dr. Nick Firoozye Dr. Nick Firoozye has over 20 years of experience in the finance industry across both buy-side and sell-side firms. He started at Lehman Brothers in MBS/ABS modeling and later held senior roles in Quant Research and Strategy at Goldman Sachs and Deutsche Bank. He also worked at asset managers and hedge funds like Sanford Bernstein, Citadel, and Exodus Point, focusing on quantitative strategies and trading. We’ll break down: What “mean reversion” actually means in statistical terms — not the loose textbook version How cointegration provides structure for multi-asset and spread-based strategies Testing frameworks that avoid false positives and overfitting traps When mean reversion fails, how to detect regime shifts, and how not to blow up Execution constraints: slippage, adverse selection, and why most paper models die in production Concrete examples of how to convert statistical signals into systematic trading rules Who Is It For? This webinar is built for people who actually want to trade or build serious models, not passive spectators: Quant researchers and data scientists Algorithmic traders and systematic PMs HFT and market-making practitioners Students and early-career quants aiming to break into stat-arb or derivatives roles Portfolio managers exploring relative value or multi-asset signals Anyone who wants to systematise mean-reversion strategies with proper validation and execution logic If you want a surface-level introduction, this is not it. If you want a rigorous, practitioner-grade breakdown from someone who has built these models at Citadel, and Exodus Point — you’re in the right place. If you’re serious about building or improving quantitative trading strategies, this session will force you to rethink your assumptions and show you how real desks evaluate and scale mean-reverting signals.

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Change initiatives often focus on processes, tools, and timelines—but overlook the most critical factor: people. In The Human Side of Change, I explore the emotional and behavioral dynamics that surface during transformation—resistance, uncertainty, fear, and change fatigue—and how leaders can respond with empathy, clarity, and confidence. This session blends real-world transformation experiences with practical leadership techniques to help you: Understand why people resist change Build trust and psychological safety during uncertainty Strengthen resilience in yourself and your teams Lead conversations that move people from resistance to renewal Designed for project leaders, change agents, and people managers, this session is highly interactive and grounded in lived experience—not theory. What You’ll Learn Why resistance is a signal—not a problem How to respond to emotional reactions during change Practical questions leaders can use to build trust Simple techniques to reduce change fatigue How to lead renewal—not just adoption Who This Is For Project Managers & Program Managers Change Managers & Agile Coaches People Leaders & Transformation Leads PMO & Delivery Leaders Anyone leading teams through uncertainty Session Format 🕒 40–45 minute interactive session 🎯 Polls, reflections, and practical leadership prompts 📄 Includes downloadable participant handout & leadership tools

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This LinkedIn Profile Optimisation Guide is designed to help you build a strong, recruiter friendly LinkedIn profile even if you are a fresher or early professional. Inside this guide, you will learn how to write a clear headline that attracts recruiters, a powerful summary that highlights your skills, and experience sections that look job ready even with limited experience. You will also learn how to choose the right keywords for LinkedIn search, improve profile visibility, structure skills properly, and avoid common mistakes that cause profiles to get ignored. This guide is practical, easy to follow, and created specifically for students, freshers, and job seekers who want real results without paid tools or fake hacks. Perfect for anyone serious about internships, jobs, and professional growth on LinkedIn.

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This ebook introduces essential production-level React optimizations every MERN developer should know. You’ll get a practical overview of key topics like debouncing, memoization, lazy loading, and list virtualization. Each pattern is explained with real-world examples and code snippets, giving you the foundations to optimize your apps and dive deeper on your own. Perfect for developers who want to write faster, more reliable, production-ready React code.

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A role based roadmap of the must know topics for quantitative finance interviews Covers 7 common roles: quantitative researcher, quantitative trader, quantitative developer, derivatives quant, risk quant, systematic quant, and data scientist in markets or risk For each role, lists what you should know across mathematics, probability and statistics, finance, and programming Also Includes - A priority matrix so you can focus on high yield topics first instead of studying randomly - A simple 2 week preparation checklist to turn the roadmap into an actionable plan - A quick red flags vs green flags section showing what commonly fails candidates and what strong candidates do differently

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