Stefan Papp – författare

Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Featuring:- A comprehensive overview of the various fields of application of data science- Case studies from practice to make the described concepts tangible- Practical examples to help you carry out simple data analysis projectsThe book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term. Contains these current issues:- Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms.- Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice- Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies- Computer vision: How can we gain insights from images and videos with data science?- Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios.- ML and AI in production: How to turn experimentation into a working data science product?- Presenting your results: Essential presentation techniques for data scientistsContributors: Stefan Papp / Wolfgang Weidinger / Katherine Munro / Bernhard Ortner / Annalisa Cadonna / Georg Langs / Roxane Licandro / Mario Meir-Huber / Danko Nikoli? / Zoltan Toth / Barbora Vesela / Rania Wazir / Günther Zauner

Maximize your portfolio, analyze markets, and make data-driven investment decisions using Python and generative AI.Investing for Programmers shows you how you can turn your existing skills as a programmer into a knack for making sharper investment choices. Youll learn how to use the Python ecosystem, modern analytic methods, and cutting-edge AI tools to make better decisions and improve the odds of long-term financial success.In Investing for Programmers youll learn how to:Build stock analysis tools and predictive modelsIdentify market-beating investment opportunitiesDesign and evaluate algorithmic trading strategiesUse AI to automate investment researchAnalyze market sentiments with media data miningIn Investing for Programmers you'll learn the basics of financial investment as you conduct real market analysis, connect with trading APIs to automate buy-sell, and develop a systematic approach to risk management. Dont worrytheres no dodgy financial advice or flimsy get-rich-quick schemes. Real-life examples help you build your own intuition about financial markets, and make better decisions for retirement, financial independence, and getting more from your hard-earned money.About the technologyA programmer has a unique edge when it comes to investing. Using open-source Python libraries and AI tools, you can perform sophisticated analysis normally reserved for expensive financial professionals. This book guides you step-by-step through building your own stock analysis tools, forecasting models, and more so you can make smart, data-driven investment decisions.About the bookInvesting for Programmers shows you how to analyze investment opportunities using Python and machine learning. In this easy-to-read handbook, experienced algorithmic investor Stefan Papp shows you how to use Pandas, NumPy, and Matplotlib to dissect stock market data, uncover patterns, and build your own trading models. Youll also discover how to use AI agents and LLMs to enhance your financial research and decision-making process.What's insideBuild stock analysis tools and predictive modelsDesign algorithmic trading strategiesUse AI to automate investment researchAnalyze market sentiment with media data miningAbout the readerFor professional and hobbyist Python programmers with basic personal finance experience.About the authorStefan Papp combines 20 years of investment experience in stocks, cryptocurrency, and bonds with decades of work as a data engineer, architect, and software consultant.Table of Contents1 The analytical investor2 Investment essentials3 Collecting data4 Growth portfolios5 Income portfolios6 Building an asset monitor7 Risk management8 AI for financial research9 AI agents10 Charts and technical analysis11 Algorithmic trading12 Private equity: Investing in start-ups13 The road goes ever on and onA Setting up the environment