@@ -92,27 +92,45 @@ Course materials for weekly Python/Data science class in Hong Kong, partnered wi
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#### Lesson 6: Data Manipulation
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- -
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+ - Web Scraping II
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+ - Introduction to Python Class Objects
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+ - Pandas Basics with Case study
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##### Homework:
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- -
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+ - Flight Delay Dataset: Create your own tables by Pandas
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##### Resources
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- -
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+ - [ 10 mins of Pandas] ( https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html )
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+ - [ Pandas CheatSheet] ( https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf )
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+ - [ SQL Intro resource] ( https://www.w3schools.com/sql/sql_intro.asp )
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---
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#### Lesson 7: Introduction to Data Science
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- -
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+ - What is Data Science?
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+ - Essential Skills of Data Scientist
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+ - Foundation of Probability
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+ - Permutation vs Combination
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+
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##### Homework:
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- -
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+ - [ L7 Homework ] ( /homework/L7_homework.md )
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##### Resources
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- -
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+ - [ Probabilities and Statistics Refresher] (
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+ https://stanford.edu/~shervine/teaching/cs-229/refresher-probabilities-statistics ) From Stanford
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#### Lesson 8: Data Manipulation and Visualization
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- -
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+ - Case Study: Titanic Dataset
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+ - Understand Machine Learning Workflow
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+ - First EDA Training
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+ - Visualization: Matplotlib, Seaborn
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##### Homework:
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- -
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+ - Build your first model with scikit-learn
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##### Resources
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- -
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+ - [ Matplotlib Official Guide] (
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+ https://matplotlib.org ) for Visualization
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+ - [ Seaborn Official Guide] (
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+ https://seaborn.pydata.org ): Another great package to create beautiful charts
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+ - [ Scikit-learn] ( https://scikit-learn.org/stable/user_guide.html ) : User Guide for machine learning
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#### Lesson 9: Black box machine learning
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