Tony Ojeda
Tony Ojeda is an accomplished data scientist and entrepreneur, with expertise in business process optimization and over a decade of experience creating and implementing innovative data products and solutions. He has a Master's degree in Finance from Florida International University and an MBA with concentrations in Strategy and Entrepreneurship from DePaul University. He is the founder of District Data Labs, a cofounder of Data Community DC, and is actively involved in promoting data science education through both organizations.
Sean Patrick Murphy
Sean Patrick Murphy spent 15 years as a senior scientist at The Johns Hopkins University Applied Physics Laboratory, where he focused on machine learning, modeling and simulation, signal processing, and high performance computing in the Cloud. Now, he acts as an advisor and data consultant for companies in SF, NY, and DC. He completed his graduation from The Johns Hopkins University and his MBA from the University of Oxford. He currently co-organizes the Data Innovation DC meetup and cofounded the Data Science MD meetup. He is also a board member and cofounder of Data Community DC.
Benjamin Bengfort
Benjamin Bengfort is an experienced data scientist and Python developer who has worked in military, industry, and academia for the past 8 years. He is currently pursuing his PhD in Computer Science at the University of Maryland, College Park, doing research in Metacognition and Natural Language Processing. He holds a Master's degree in Computer Science from North Dakota State University, where he taught undergraduate Computer Science courses. He is also an adjunct faculty member at Georgetown University, where he teaches Data Science and Analytics. Benjamin has been involved in two data science start-ups in the DC region: leveraging large-scale machine learning and Big Data techniques across a variety of applications. He has a deep appreciation for the combination of models and data for entrepreneurial effect, and he is currently building one of these start-ups into a more mature organization.
Abhijit Dasgupta
Abhijit Dasgupta is a data consultant working in the greater DC-Maryland-Virginia area, with several years of experience in biomedical consulting, business analytics, bioinformatics, and bioengineering consulting. He has a PhD in Biostatistics from the University of Washington and over 40 collaborative peer-reviewed manuscripts, with strong interests in bridging the statistics/machine-learning divide. He is always on the lookout for interesting and challenging projects, and is an enthusiastic speaker and discussant on new and better ways to look at and analyze data. He is a member of Data Community DC and a founding member and co-organizer of Statistical Programming DC (formerly, R Users DC).
发表于2024-11-16
Practical Data Science Cookbook - Real-World Data Science Projects to Help You Get Your Hands On You 2024 pdf epub mobi 电子书
为啥第一个project里边很多数据图做出来跟书里做出来的趋势甚至相反,不知道是我弄错了还是数据本身改动过…… 书是不错,上手容易,但如果对代码增加一点注释会更易懂。 ***********************************
评分R语言方面:还可以,毕竟R语言作为数据科学的语言已经有很长的额历史了,各方面也都比较成熟了,而且我本身也有R语言基础所以读起来没什么问题,内容也还可以,不过当我转身开始学习Python的时候就出现问题了。 Python语言方面:首先全文都是2.X语言写的,如果你完全是从3.X开...
评分R语言方面:还可以,毕竟R语言作为数据科学的语言已经有很长的额历史了,各方面也都比较成熟了,而且我本身也有R语言基础所以读起来没什么问题,内容也还可以,不过当我转身开始学习Python的时候就出现问题了。 Python语言方面:首先全文都是2.X语言写的,如果你完全是从3.X开...
评分为啥第一个project里边很多数据图做出来跟书里做出来的趋势甚至相反,不知道是我弄错了还是数据本身改动过…… 书是不错,上手容易,但如果对代码增加一点注释会更易懂。 ***********************************
评分为啥第一个project里边很多数据图做出来跟书里做出来的趋势甚至相反,不知道是我弄错了还是数据本身改动过…… 书是不错,上手容易,但如果对代码增加一点注释会更易懂。 ***********************************
图书标签: 数据分析 R 数据 Python 机器学习 data 科普 数据科学家
Data's value has grown exponentially in the past decade, with 'Big Data' today being one of the biggest buzzwords in business and IT, and data scientist hailed as 'the sexiest job of the 21st century'. Practical Data Science Cookbook helps you see beyond the hype and get past the theory by providing you with a hands-on exploration of data science. With a comprehensive range of recipes designed to help you learn fundamental data science tasks, you'll uncover practical steps to help you produce powerful insights into Big Data using R and Python.
Use this valuable data science book to discover tricks and techniques to get to grips with your data. Learn effective data visualization with an automobile fuel efficiency data project, analyze football statistics, learn how to create data simulations, and get to grips with stock market data to learn data modelling. Find out how to produce sharp insights into social media data by following data science tutorials that demonstrate the best ways to tackle Twitter data, and uncover recipes that will help you dive in and explore Big Data through movie recommendation databases.
Practical Data Science Cookbook is your essential companion to the real-world challenges of working with data, created to give you a deeper insight into a world of Big Data that promises to keep growing.
非常适合数据科学入门,也是R和python入门的补充,跟随实际项目去了解数据分析方法和思路。
评分案例太冗长,难度适中,适合认真型小白自学 @jessiejcjsjz
评分非常适合数据科学入门,也是R和python入门的补充,跟随实际项目去了解数据分析方法和思路。
评分非常适合数据科学入门,也是R和python入门的补充,跟随实际项目去了解数据分析方法和思路。
评分案例教学,不太简单不太难,高年级本科生水平。
Practical Data Science Cookbook - Real-World Data Science Projects to Help You Get Your Hands On You 2024 pdf epub mobi 电子书