Exploratory data analysis
Visualization (for exploratory data analysis and reporting)
Dashboards and metrics
Find business insights
Data-driven decision making
Data engineering/Big Data (Mapreduce, Hadoop, Hive, and Pig)
Get the data themselves
Build data pipelines (logs→mapreduce→dataset→join with other data→mapreduce→scrape some data→join)
Build products instead of describing existing product usage
Hack
Patent writing
Detective work
Predict future behavior or performance
Write up findings in reports, presentations, and journals
Programming (proficiency in R, Python, C, Java, etc.)
Conditional probability
Optimization
Algorithms, statistical models, and machine learning
Tell and interpret stories
Ask good questions
Investigation
Research
Make inferences from data
Build data products
Find ... (查看原文)
Being humanist in the context of data science means recognizing the role your own humanity plays in building models and algorithms, thinking about qualities you have as a human that a computer does not have (which includes the ability to make ethical decisions), and thinking about the humans whose lives you are impacting when you unleash a model onto the world. (查看原文)
Now that answering complex and compelling questions with data can make the difference in an election or a business model, data science is an attractive discipline. But how can you learn this wide-ranging, interdisciplinary field? With this book, you’ll get...
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Chebyshev
O'Reilly Media2013版
涅瓦纳
O'Reilly Media2013版
3 有用 軒轅鍾書 2015-12-07 00:06:21
對於ML 來說，這本書講得太淺。興許對於DS 還尚可。不過最後對於data scientist 所應具備的技能，還是蠻認可的。scientist 比engineer 做好了還是技高一籌，對於研究結果，論文輸出或報告輸出擴大影響還是蠻有好處。只是本書翻譯欠佳，讀來不禁搖頭嘆息。要是要理解各算法，還是MMDS 更值得推薦。
0 有用 元英 2016-05-31 16:07:56
什么是data science
0 有用 Reguping 2022-04-01 14:52:34
简单的介绍了以下DS的一些任务，描绘了这个行业的一个大致框图。
1 有用 葱芽Aspire 2016-05-02 20:51:20
结合一线数据科学家日常工作的一手资料，在山顶眺望全局，非工具书，也有不明觉厉之处，但对打开视野大有裨益，技能和方法岂是朝夕之间，实战嘛，纸上得来终觉浅
2 有用 melete 2016-11-11 19:36:38
强调数据科学总体视角，区别于算法教材，不难但适合有一定基础后阅读，有很多大牛的经验