- Statistics for Data Science
- James D. Miller
- 108字
- 2021-07-02 14:58:51
Thinking like a data scientist
As we've already stressed, agreement on the concepts of what a data scientist is and does are still just emerging. The entire field of data science is at best roughly defined. Transitioning to data science is perhaps as much about finding an organization or group whose needs match your skills as it is about understanding what skills and concepts are involved in data science and then working towards developing those skills.
Just as a data developer stays up to date and knowledgeable on the trends and tools in and around the manipulation of and access to data, so should the would-be data scientist.
推薦閱讀
- 基于C語(yǔ)言的程序設(shè)計(jì)
- Practical Data Analysis
- Visualforce Development Cookbook(Second Edition)
- 大數(shù)據(jù)專業(yè)英語(yǔ)
- Effective DevOps with AWS
- Windows程序設(shè)計(jì)與架構(gòu)
- 嵌入式操作系統(tǒng)原理及應(yīng)用
- 啊哈C!思考快你一步
- Python文本分析
- 30天學(xué)通Java Web項(xiàng)目案例開(kāi)發(fā)
- Oracle 11g Anti-hacker's Cookbook
- Hands-On Microservices with C#
- Keras Reinforcement Learning Projects
- 智能座艙之車載機(jī)器人交互設(shè)計(jì)與開(kāi)發(fā)
- Ubuntu 9 Linux應(yīng)用基礎(chǔ)