Flora Li's starred repositories
Crawler-of-LinkedIn
By Leverage the project, we can crawler an LinkedIn user's whole profile information.
linkedIn-crawler
领英的爬虫-linked-scrapy
Beyond-LeetCode-SQL
Analysis of SQL Leetcode and classic interview questions, common pitfalls, anti-patterns and handy tricks. Sample databases.
goodreads_etl_pipeline
An end-to-end GoodReads Data Pipeline for Building Data Lake, Data Warehouse and Analytics Platform.
Udacity-Data-Engineering-Projects
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
advertools
advertools - online marketing productivity and analysis tools
Marketing-for-Engineers
A curated collection of marketing articles & tools to grow your product.
udacity_ABTesting
Project work for Udacity's AB Testing Course
simple-neural-network
A simple Python script showing how the backpropagation algorithm works.
120-DS-Interview-Questions
My Answer to 120 Data Science Interview Questions
DS-Take-Home
My solution to the book A Collection of Data Science Take-Home Challenges
DS-Take-Home
My solution to the book A Collection of Data Science Take-Home Challenges
TakeHomeDataChallenges
My solution to the book <A collection of Data Science Take-home Challenges>
storytelling-with-data
Course materials for Dartmouth Course: Storytelling with Data (PSYC 81.09).
datastorytelling
NYU ITP data storytelling
pandas_exercises
Practice your pandas skills!
MachineLearning
Basic Machine Learning and Deep Learning
scipy-lecture-notes-zh-CN
中文版scipy-lecture-notes. 网站下线, 以离线HTML的形式继续更新, 见release.
120-Data-Science-Interview-Questions
Answers to 120 commonly asked data science interview questions.
interview_internal_reference
2023年最新总结，阿里，腾讯，百度，美团，头条等技术面试题目，以及答案，专家出题人分析汇总。
awesome-interview-questions
:octocat: A curated awesome list of lists of interview questions. Feel free to contribute! :mortar_board:
pumpkin-book
《机器学习》（西瓜书）公式详解
pandas_tutorial
Pandas tutorial for SciPy2015 and SciPy2016 conference
ad_examples
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
elasticsearch-spark-recommender
Use Jupyter Notebooks to demonstrate how to build a Recommender with Apache Spark & Elasticsearch
datastream.io
An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana