| Brief | Book | |
|---|---|---|
| Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the math or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment ― and this book shows you how. |
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Monday, July 27, 2020
Big Data and Machine Learning in Quantitative Investment
Competition Law and Big Data:
| Brief | Book | |
|---|---|---|
| In this timely book with insights on how to analyses and evaluates how the characteristics of information as a good, as well as the characteristics of digital platforms, affect the application of competition law in both theory and practice.
Chapters offer a full evaluation and in-depth analysis of several key case studies in which information such as big data has been obtained, made use of, sold, or biased in an uncompetitive way. Such critical case studies include the European Commission's 2017 judgement against Google for granting illegal advantage to their own comparison shopping service. The book offers guidance on how competition law can evolve to accommodate digital markets, such as classifying information as 'commons' or 'commodity', in order to realize social goals such as fairness. Compelling and insightful, this book will prove an important companion for students and scholars studying digital markets, as well as competition law more widely. It will also appeal to practitioners working on cases involving the regulation and usage of big data. |
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Sunday, July 26, 2020
Advanced Data Science and Analytics with Python
| Brief | Book | |
|---|---|---|
| Enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python;
Note, this book targets readers with a background in programming, who are interested in the tools used in data analytics and data science. |
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People Analytics... A Practical Guide to Data-driven HR
| Brief | Book | |
|---|---|---|
| An understanding of people analytics is a crucial skill for all HR professionals. This introduction to People Analytics allows all HR professionals to get to grips with analytics, feel confident in their ability to handle employee and organizational data and use analytics to move from opinions to insights. From where to find data in an organization, how to collect it and analyze it through to how to use these findings to add business value, this book is essential reading for all HR professionals. With case studies and thought leadership insights from companies who have leveraged people analytics to improve culture and employee engagement, increase performance and reduce costs. There is also expert guidance and practical advice on how to embed analytics into HR processes and adopt a data-driven approach to all workplace activities. |
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Naked Statistics: Stripping the Dread from the Data
| Brief | Book | |
|---|---|---|
| Once considered tedious, the field of statistics is rapidly evolving into a discipline. From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. In Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more. For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions. |
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Marketing Analytics...Improving Consumer Insights Using Data Techniques
| Brief | Book | |
|---|---|---|
| Who is most likely to buy and what is the best way to target them? How can businesses improve strategy without identifying the key influencing factors? The second edition of Marketing Analytics enables marketers and business analysts to leverage predictive techniques to measure and improve marketing performance. By exploring real-world marketing challenges, it provides clear, jargon-free explanations on how to apply different analytical models for each purpose. From targeted list creation and data segmentation, to testing campaign effectiveness, pricing structures and forecasting demand, this book offers a welcome handbook on how statistics, consumer analytics and modelling can be put to optimal use. |
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Marketing Analytics: Strategic Models and Metrics
| Book | Brief | |
|---|---|---|
With a blend of strategic models and metrics, this books offers marketing students and professionals a practical guide to strategic decision models and marketing metrics. The tools described in the book will aid marketers in making intelligent decisions to drive revenue and results in their organizations.
The book contains a wealth of information on marketing analytics:
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