Data Science for Finance Professionals
Kogan Page Ltd (Verlag)
978-1-78966-617-5 (ISBN)
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The financial services sector is one of the most data-intensive fields to work in, but data science can be an enigma for non-technical professionals. Between the media incorrectly throwing around big data jargon and firms not utilizing vast data resources, it has never been more important for finance managers to drive the creation of data-driven organizations and get involved in technical discussions. Data Science for Finance Professionals is a practical guide for finance professionals who need a clear introduction to machine learning, common algorithms and major data science models.
The book begins with an overview of data science, machine learning, AI and cloud and offers easy-to-understand explanations of major data science models including logistic regression, decision trees, random forests and neural networks. Interviews with financial leaders and investment professionals explore how they use data science in their roles and include advice to managers looking to become more data-driven. With guidance on hiring technical talent and developing a data culture in an organization, Data Science for Finance Professionals gives readers the confidence to evaluate technology proposals, get involved in technical discussions, and improve data strategies to achieve superior stakeholder returns.
Neal Kumar is the co-founder of Cognitir, a global training company based in the US which helps finance and business professionals acquire in-demand technology skills. He also runs a boutique consulting practice, advising on issues ranging from data science strategy to financial modelling. He was previously an investment banker at Lazard, JPMorgan, and Houlihan Lokey, and received his MBA from London Business School (UK) and BBA in Finance from the University of Notre Dame (USA). He is a CFA Charterholder and a Member of the CFA Institute Education Advisory Committee (EAC) Working Body.
Section - ONE: Introduction to Data Science, the Internet, and CPUs;
Chapter - 01: What is Data Science?;
Chapter - 02: What is Machine Learning?;
Chapter - 03: What is AI?;
Chapter - 04: Cloud vs. On-Premises;
Chapter - 05: Overview of Major Cloud Providers;
Section - TWO: Understanding Data Science Problems and Common Algorithms;
Chapter - 06: Supervised vs. Unsupervised Learning;
Chapter - 07: Four Main Data Science Problem Genres;
Chapter - 08: Crisp-DM Model;
Chapter - 09: Major Data Science Models Simply Explained
Section - THREE: Interviews;
Chapter - 10: Interviews with Finance Leaders and Investment Professionals;
Section - FOUR: Building a Data-Driven Organization;
Chapter - 11: Centralized Data vs. Siloed;
Chapter - 12: Chief Data/Information Officer;
Chapter - 13: Data Infrastructure Investment;
Chapter - 14: Evaluating Technical Proposals;
Chapter - 15: Hiring Technical Talent - Tips and Best Practices;
Chapter - 16: Developing a Culture of "Data" in your Organization;
| Erscheint lt. Verlag | 3.6.2021 |
|---|---|
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung | |
| ISBN-10 | 1-78966-617-1 / 1789666171 |
| ISBN-13 | 978-1-78966-617-5 / 9781789666175 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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