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Discover key concepts for more than 10 classic algorithms and methods, including: • A / B Test • Detection of anomalies • Rules of Association • Clustering • Decision trees and random forests • Regression Analysis • Analysis of social networks • Neural networks To help you revisit what you've learned, we've also included: • Cheat sheets comparing pros and cons of algorithms • Summary of the final chapters • List of glossaries of commonly used terms What is this book for? You! This is a great opportunity to learn more about data science , technology and technology. Why should I be interested in data science? You saw how the data revolutionized the way we live and work. Google uses it to generate queries and queries. Recently, data science algorithms were also used to boost the development of digital personal assistants and standalone vehicles. Understanding how these algorithms work is crucial to assessing our potential impact on our future...

6 Business Concepts you need to become a Data Science Unicorn Data science projects are not just about ETL and building models but rather about understanding business and its strategic problems, asking the right questions, and using technology along with data science to solve those problems. Failure in clearly understanding the business and its problems can doom data science/analytics projects and ultimately whole business strategy. Typical analytics teams consist of business/data analysts (who know business, define business problems, and interpret the data science results from business point of view), data scientists (who are data science experts) and data engineers (who are technology experts, developers, testers and administrators). Though these 3 types of experts are on the same team, there might be a gap in correctly understanding the problem or interpreting the results after integrating their expertise. So, a champion is the one who knows all 3 areas and helps the team to b...

277 Data Science Key Terms, Explained This publication presents a collection of key terms related to data science with concise and harmless definitions, organized into 12 distinct topics. Beginning with Big Data and progressing in natural language processing, this definition train stops at learning machines, databases, Apache Hadoop, and many others. This may take time, but once you have completed the terminology presented here, you should have a good idea of ​​the key terms of importance in data science. And do not worry if the settings are too thin for you; Links abound to extend related reading opportunities where appropriate. 20 key terms of the data, explained Important information, if in a way you did this on this site and you did not hear that term as it grew to become a popular term for at least ten and a half years, I really do not know what to say. But simply because we hear the term, or participate (or the opposite) of its flawless use that does not really mean th...