Souvik G.’s Post

💼 Looking for a Career in Financial Data Science? 🚀 Are you ready to dive into the world of financial datasets but unsure where to start? Whether you're aiming to build predictive models, optimize investment portfolios, or analyze stock market trends, understanding financial data is key. And guess what? It’s not as complicated as it seems! In my latest blog post, "Getting Started with Financial Datasets: A Beginner’s Guide," I cover everything you need to kickstart your journey into financial data science: 💡 Where to find reliable financial data: Explore trusted sources like Yahoo Finance, Quandl, Kaggle, and APIs like Alpha Vantage Inc.. These platforms offer incredible resources—many for free—that make financial data accessible to all, whether you're a beginner or an expert. 📊 How to fetch and use data: Learn how to use Python libraries like yfinance to grab stock price data. 📈 Practical example: See how I analyze Apple Inc.'s stock prices, and how you can do the same for any stock. 👉 Are you a beginner in data science or a finance enthusiast? This guide will give you the foundation you need to explore, analyze, and work with financial datasets with confidence. Why should you care? Financial data science is a rapidly growing field, and with the right skills, you can unlock incredible opportunities in: Investment analysis Risk management Predictive modeling for market trends 🔍 Want to land a job in this exciting field? The right knowledge of financial datasets can set you apart from the competition. ✅ Take action: Read the full blog here: https://lnkd.in/dg_68ReC Drop a comment or message me if you’re curious about how to get started in this field. Let’s connect and explore career opportunities in financial data science! 🚀 Shoutout to platforms like Yahoo Finance, Kaggle, Quandl, and Alpha Vantage Inc. for providing these incredible, free resources that make it easier for everyone to access high-quality financial data. Your support helps make financial data science more accessible to all! 📩 Want to collaborate? Reach out: tanejanitij4002@gmail.com #DataScience #FinanceJobs #FinancialAnalysis #StockMarket #DataScienceCareer #JobSeekers #Python #MachineLearning #DataAnalysis #CareerGrowth #Fintech #YahooFinance #Kaggle #Quandl #AlphaVantage

The demand for skilled data scientists is skyrocketing in today’s data-driven world. If you want to get in on the action, here are the best data science certifications of 2024.

mastering both technical and business metrics is key for any data scientist that’s why we’re adding a new chapter to The Little Book of ML Metrics, covering the top Business Metrics every data scientist should know we are starting with Sharpe Ratio it is a go-to metric for understanding risk-adjusted returns in investment strategies and machine learning models in finance it compares the performance of an investment against a risk-free asset, adjusting for risk taken when should you use it? ✔️ Comparing different investment strategies ✔️ Evaluating algorithmic trading systems ✔️ Analyzing asset allocation models however, it’s important to avoid over-reliance in volatile markets, where returns might skew the ratio what business metric do you look for the most in your industry? Link to the book below 👇

With Cláudia M. Viana and Sandra Oliveira Introductory Chapter in our book about Time Series Analysis. Time series data, common in various fields, present unique challenges due to random noise and interdependencies between measurements at different time points.

Financial sentiment analysis with an LLM? I'm excited to share that I've just uploaded a fine-tuned, quantized Mistral-7B model specifically tailored for financial sentiment analysis. Check it out: https://lnkd.in/eiSaYNMh

365 Days in 12 Columns: Binary Functions This bootcamp is blowing my mind. Storing 365 days of behavior in just 12 columns using binary functions is an approach I had never considered before today. In the context of Open Finance, where platforms share daily information about our customers, this is a game-changer. With a base of 400,000 customers, 30% active, and an average of 10 transactions per month, storing active and inactive days becomes expensive. Zach Wilson Boot Camp trick: Use binary functions to compact data. Each column represents the active days of a month. For example, if a customer was active on days 1, 3, and 5, it would be 10101 in binary or 21 in decimal. This not only reduces storage costs but also helps identify key usage patterns and optimize campaigns based on days of high interaction. Although my focus is data science, this solution showed me how data engineering makes life easier for those of us who analyze information. #OpenFinance #DataEngineering #DataScience #ChurnPrediction

A few weeks ago we benchmarked Ragie's RAG pipeline against FinanceBench - a complex data set of real world financial documents and questions. We outperformed the benchmark by 42%. That was then. Since then, we have made huge improvements in our RAG pipeline both in accuracy and speed, especially when it relates to structured data embedded within unstructured documents. In our new benchmark we outperformed the FinanceBench benchmark by a whopping 137% while improving data ingestion speed by 25%! In our latest engineering blog, we talk about how we achieved this!

I am excited to share our latest blog post, "Nulls: Revisiting Null Representation in Modern Columnar Formats." This comprehensive piece delves into the evolving landscape of data representation, specifically focusing on how null values are handled in contemporary columnar storage formats. As these formats gain popularity for their performance and efficiency, understanding their treatment of nulls becomes increasingly critical for data analysts and engineers. Join us as we explore innovative approaches to null representation and their implications for data integrity and retrieval performance. Read the full post here: https://ift.tt/jqrycwK.