Best of Data Science — February 2024
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Towards Dev·2y
3 Essential SQL Tricks You Absolutely Need to Know
Learn three essential SQL tricks that can improve efficiency and analytical capabilities. Topics include using Common Table Expressions (CTEs), creating Partial Indexes for faster searches, and implementing Conditional Aggregation in SQL queries.
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Hacker News·2y
Your Phone Isn’t Spying on You to Show You Ads (It’s Worse Than That)
Your phone is not listening to you to show you ads, but it is collecting data such as location information, search history, browsing history, purchase history, and physical interactions. Advertisers use this data to target ads based on your interests and the people you spend time with. While targeted advertising can seem accurate and sometimes mysterious, it is based on algorithms and large datasets. The next step in advertising is generative AI, which could create personalized ads that target your insecurities and dreams. However, there are tools available to help protect your privacy online, such as Apple's App Tracking Transparency feature and privacy controls on operating systems and browsers.
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Taro·2y
The Fundamentals of Data Engineering - Preface + Chapter 1: Data Engineering Described
This post provides an overview of the book 'Fundamentals of Data Engineering', discussing the motivations behind the book and the importance of data engineering in relation to data science and machine learning. It covers the Data Science Hierarchy of Needs, the Data Engineering Lifecycle, and the skills and activities of a Data Engineer. The post also discusses the stages of Data Maturity and the different types of Data Engineers. The next blog post will cover Chapter 2 of the book.
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Fireship·2yhow god programmed birds probably
Birds fly together in unison using an algorithm built into nature, which can be reproduced in computers using three simple rules. These rules include separation, alignment, and cohesion, and they create cool patterns that simulate flocking behavior in nature.
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Medium·2y
Building an Investment Portfolio Management App with Python
Learn how to build an investment portfolio management app with Python and Streamlit. The app helps track investment performance, analyze portfolio allocation, and evaluate the historical performance of individual securities. It provides interactive visualizations and insights to guide decision-making.
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KDnuggets·2y
Collection of Free Courses to Learn Data Science, Data Engineering, Machine Learning, MLOps, and LLMOps
A collection of free courses for learning data science, data engineering, machine learning, MLOps, and LLMOps is provided. The courses are self-paced and community-based, offering valuable resources for beginners and experienced professionals.
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I am Aurora, the Most Powerful AI Financial Assistant That The World Has Ever Seen
Aurora is a powerful AI financial assistant developed by Austin Starks. Aurora specializes in trading automation and financial research, aiming to make investing and trading more accessible. Aurora can generate tailored indicators, design trading strategies, and analyze company financial information. Austin believes that Aurora's ability to find novel investing opportunities using natural language makes it more powerful than any other platform. Aurora is a neural network powered by OpenAI's GPT APIs and plans to improve its functionality in 2024.
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How to Find the Best Stocks in the Stock Market Using AI
Learn how to use AI to find the best stocks in the stock market using the NexusTrade Platform. Discover the features of intelligent stock screening and the implementation process. Also, explore the MongoDB Aggregation Framework for executing complex queries.
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AWS Tip·2y
Django vs Streamlit Frameworks for Implementing Machine Learning Models
Comparison of Django and Streamlit frameworks for implementing machine learning models. Django simplifies and accelerates web development, while Streamlit is used to build interactive user interfaces for data science applications. Django allows for more flexible front-end development, but Streamlit is easier to implement. Django is recommended for more complex projects, while Streamlit is recommended for beginners or testing models.
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Stock Market Sentiment Prediction with OpenAI and Python
This article explores the use of OpenAI and Python for predicting stock market sentiment. It discusses the importance of sentiment analysis in making strategic decisions and introduces the Stock Market and Financial News API. The article provides step-by-step instructions on importing packages, activating the API key, extracting and cleaning the data, setting up the LLM chain, and visualizing the sentiment analysis results. The analysis reveals a prevailing optimistic sentiment towards Apple Inc. in recent news articles.
