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).
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.
發表於2025-01-11
Practical Data Science Cookbook - Real-World Data Science Projects to Help You Get Your Hands On You 2025 pdf epub mobi 電子書 下載
R語言方麵:還可以,畢竟R語言作為數據科學的語言已經有很長的額曆史瞭,各方麵也都比較成熟瞭,而且我本身也有R語言基礎所以讀起來沒什麼問題,內容也還可以,不過當我轉身開始學習Python的時候就齣現問題瞭。 Python語言方麵:首先全文都是2.X語言寫的,如果你完全是從3.X開...
評分為啥第一個project裏邊很多數據圖做齣來跟書裏做齣來的趨勢甚至相反,不知道是我弄錯瞭還是數據本身改動過…… 書是不錯,上手容易,但如果對代碼增加一點注釋會更易懂。 ***********************************
評分為啥第一個project裏邊很多數據圖做齣來跟書裏做齣來的趨勢甚至相反,不知道是我弄錯瞭還是數據本身改動過…… 書是不錯,上手容易,但如果對代碼增加一點注釋會更易懂。 ***********************************
評分為啥第一個project裏邊很多數據圖做齣來跟書裏做齣來的趨勢甚至相反,不知道是我弄錯瞭還是數據本身改動過…… 書是不錯,上手容易,但如果對代碼增加一點注釋會更易懂。 ***********************************
評分R語言方麵:還可以,畢竟R語言作為數據科學的語言已經有很長的額曆史瞭,各方麵也都比較成熟瞭,而且我本身也有R語言基礎所以讀起來沒什麼問題,內容也還可以,不過當我轉身開始學習Python的時候就齣現問題瞭。 Python語言方麵:首先全文都是2.X語言寫的,如果你完全是從3.X開...
圖書標籤: 數據分析 R 數據 Python 機器學習 data 科普 數據科學傢
利益相關,參與瞭後四分之一的翻譯。優點例子很生動實踐性很強,缺點理論部分偏弱,並且難度偏低,廢話偏多。裏麵介紹瞭很多Python以及R相關工具和類庫,可以幫助入門者迅速構建自己的工具鏈並找到實際應用的例子,這大概是這本書最大的貢獻瞭吧
評分利益相關,參與瞭後四分之一的翻譯。優點例子很生動實踐性很強,缺點理論部分偏弱,並且難度偏低,廢話偏多。裏麵介紹瞭很多Python以及R相關工具和類庫,可以幫助入門者迅速構建自己的工具鏈並找到實際應用的例子,這大概是這本書最大的貢獻瞭吧
評分案例教學,不太簡單不太難,高年級本科生水平。
評分案例太冗長,難度適中,適閤認真型小白自學 @jessiejcjsjz
評分非常適閤數據科學入門,也是R和python入門的補充,跟隨實際項目去瞭解數據分析方法和思路。
Practical Data Science Cookbook - Real-World Data Science Projects to Help You Get Your Hands On You 2025 pdf epub mobi 電子書 下載