Hacking Your Data Scientist Interview
我们是一个交流机器学习实践的社区,通过社群学习的方式来 review 有代表性的 notebook 和 ML 项目,积累机器学习的有效实践、面试经验和知识内容。欢迎投稿和建议,请提交 issue。
Coming events 永久 Skype 会议链接
Date Time | Host | Facilitator | 主题 | 详情 |
---|---|---|---|---|
11/13/2021 1300 PST | tanghong | suredream | take home-randomforest classifier | 详情 |
11/21/2021 1300 PST | tanghong | zhang | go-through hospital take-home | 详情 |
历史存档 |
如何参与
- 成为社群成员,介绍自己,为本 repo 和 wiki 贡献有价值的内容或提供反馈。
- 尽量参与weekly review meeting,成为主理人 (host) 和协理人 (facilitator)。永久 Skype 会议链接:
TOC
- The Zen of Data Science Review
- 如何进行 weekly review meeting
- 如果你是 host
- 如果你是 facilitator
- 如果你是 invited reviewer
- 如果你是 ML student
- 如何使用 reviewnb 在 notebook 上进行评论
- 日程安排
- Guidance
- 飞行规则 flight rules
- Other resource
- Presentations
- Contributor list
The Zen of Data Science Review
by suredream
- agility is better than faultless
- practicality beats fancy
- readability always matters
- topdown
- though eternal, uncertainty has to be measurable
- indispensable intellectual curiosity
如何进行 weekly review meeting
- 会前,由本周 host
- 会前,参与者
- 通过reviewnb对本周的 notebook 进行 review。
- 提交其它问题到对应的 issues 中。
- weekly review meeting (1hr)
- 会议时间通过本 repo 发布,skype meeting连接
- 由 host 简要说明其原理、应用前提、优缺点,最佳实践 (10 min)
- host goes through notebook (motivation, problem, data, model, 5 min)
- host 点评之前的评论 (5-10 min)
- host drive 自由讨论, facilitator 记笔记。(20-25 min )
- host / facilitator 进行小结 (10min)
- 确定下期活动的 host 和 facilitator (5min)
如果你是 host
- fork 这个 repo
- 选一个有代表性的实验 notebook,上传到 repo 中,提交 PR
- 在群中发布消息,notebook 的简要介绍,reviewnb 地址,更新日程
- 根据上小节内容组织讨论,分享你了解的知识,提供充足和准确的参考材料
- 持续地更新和维护文档
如果你是 facilitator
- 不需要你对本议题有很充分的了解,你通过分担 host 工作量的方式来贡献社区
- 帮助发布活动消息,帮助回答非技术性的问题
- 在会议过程中记录详细讨论,尝试区分要点和重要信息
- 讨论结束时,从自己的角度提供小结
- 会后,生成完整文档供 host 进一步审定
如果你是 invited reviewer
- 我们需要经验丰富的你来提供洞见,通过你的 comments 来说明业界的最佳实践
- 你的专业意见会被记录在每周的文档中,并注明出处
- 你会在我们的社群里成为有影响力的专业人士,你对于 wiki 和社区的意见会受到特别的重视
- 我们会努力让你同样能够受惠于社区的文档,on the top
如果你是 ML student
- 我们需要你问好的问题,收集和记录好的答案
- 你可以在 issue区 提请大家注意新的文献、模型、工具
- 你可以在群里寻找 mentor,并建立互惠的 mentorship 关系
- 帮助宣传我们,使得更多的人参与进来
如何使用 reviewnb 在 notebook 上进行评论
阅读其文档。
Guidance
- springboard: 109 Data Science Interview Questions and Answers
- 10 Ways to Profit from Job Interview Services with No Coaching
飞行规则 flight rules
Other resource
-
Readlist
- Precison and Recall
- Gradient Boosting from scratch
- So you built a Machine Learning model?(Bias and Varience)
- Understanding LSTM Networks : part 1
- Understanding LSTM Networks : part 2
- Choosing the Right Metric for Evaluating ML Models — Part 1(Regression)
- Choosing the Right Metric for Evaluating Machine Learning Models — Part 2(Classification)
-
Statistics
- 1. Glossary of common Machine Learning, Statistics and Data Science terms
- 2. Basics of Probability for Data Science explained with examples
- 3. Bayesian Statistics explained to Beginners in Simple English
- 4. Comprehensive & Practical Inferential Statistics Guide for data science
- 5. Your Guide to Master Hypothesis Testing in Statistics
- 6. A Simple Introduction to ANOVA (with applications in Excel)
- 7. 40 questions on Statistics for data scientists & analysts - Part - 1
- 8. 41 questions on Statistics for data scientists & analysts - Part - 2
Presentations
- How to Become a Data Scientist
- Introduction to Data Science
- Intro to Data Science for Enterprise Big Data
- How to Interview a Data Scientist
- How to Share Data with a Statistician
- The Science of a Great Career in Data Science
- What Does a Data Scientist Do?
- Building Data Start-Ups: Fast, Big, and Focused
- How to win data science competitions with Deep Learning
- Full-Stack Data Scientist