Lead Research Scientist, Financial Risk Quantitative Research, SS&C Algorithmics Adjunct Professor, University of Toronto
MIE1624H – Introduction to Data Science and Analytics Lecture 2 – Python Programming
University of Toronto January 18, 2022
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Lecture outline
Introduction to Data Science and Analytics (continuing Lecture 1)
Python essentials
▪ IPython notebooks
▪ Variables and types
▪ Operators and comparisons
▪ Compound types – strings, tuples, lists and dictionaries ▪ Control flow – conditional statements (if, elif, else), loops ▪ Functions
▪ Files and the operating system ▪ Exception handling
Lecture outline
Introduction to Pandas
▪ Introduction to pandas data structures – DataFrame, index objects ▪ Pandas essential functionality
▪ Summarizing and computing descriptive statistics
▪ Pivot tables in pandas
Web-scrapping with Python
Pandas pivot_table cheat sheet
To Do before Lecture 3
Run IPython examples provided in class
◼ Use Python on cloud via Google Colab
❑ You can use Python on Google cloud via https://colab.research.google.com
◼ Install Python on your laptop
❑ Recommended to use Python version 3.X
❑ You may use your own Python distribution, Anaconda distribution is recommended to install https://www.anaconda.com/products/individual
◼ Form groups of seven students for in-class presentations and course project
❑ Add all your group members to Group X on Quercus
❑ All groups should have exactly seven members
❑ In-class presentations will be done in the order of group numbers
❑ Course Project will be the same for all groups
❑ Every group member get the same mark, independently on how you split responsibilities inside each group
◼ Check class web-page on Quercus regularly 6
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