# Before the lecture - The slides URL (also linked from Canvas page) ### .center[
] - Please go to the GitHub repo ### .center[
] - Follow the instructions in the .red[README.md]. --- class: center, middle ### .gray[Big Data Summer Institute]
.huge[Data analysis in Python] ![:scale 60%](./images/python-logo.png) ### Instructor: Fred Feng --- class: split-60 .column[ ## About the instructor Fred Feng
Assistant Professor [Industrial Engineering](https://umdearborn.edu/cecs/departments/industrial-and-manufacturing-systems-engineering) [University of Michigan-Dearborn](https://umdearborn.edu/) [umich.edu/~fredfeng](http://umich.edu/~fredfeng) [fenggroup.org](https://fenggroup.org/) ] .column[
![:scale 38%](./images/fredfeng.png) ] --- # Teaching - IMSE 317 Engineering Probability and Statistics - IMSE 440 Applied Statistical Models in Engineering - IMSE 586 Big Data Analytics and Visualization --
.center[.huge[[probstats.org](https://probstats.org)]] --- # Why programming in data analysis?
-- - Flexibility & customization -- - Reproducible research -- - Collaborative work -- - Version control --- # Why Python? .center[![:scale 100%](./images/python-logo.png)] - Open source, free, and widely available - Massive open-source community --- # Why Python? - ### Extensive libraries - [NumPy](https://numpy.org/) - [SciPy](https://www.scipy.org/) - [pandas](https://pandas.pydata.org/) - [statsmodels](https://www.statsmodels.org/) - [scikit-learn](https://scikit-learn.org/) - and many more --- .center[![:scale 100%](./images/pythonvislandscape.png)] --- class: middle, center .center[![:scale 100%](./images/xkcd-python.png)]
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--- # Why Python? - Beginner friendly .center[![:scale 50%](https://i2.wp.com/lightroastcomics.com/wp-content/uploads/2018/04/parselfingers.png)]
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-- - "High-level" programing language - Intuitive syntax --- # Why Python? - ### Widely popular - [IEEE: Top Programming Languages 2021](https://spectrum.ieee.org/top-programming-languages/) - [Widely used in industry](https://scikit-learn.org/stable/testimonials/testimonials.html) - Job qualifications --- # Purpose of this workshop
- ### Build basic skills and confidence - ### Hands-on experience - ### Whet your appetite to learn more! --- # Topics
- ### Python basics - ### Data analysis with [pandas](https://pandas.pydata.org/) - ### Data visualization with [matplotlib](https://matplotlib.org/) --- # Tips - You are highly encouraged to type along. - Learning by doing .center[![:scale 30%](./images/ridebike.jpg)] -- - If you get stuck, ask our TA for help.
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--- class: center, middle ![:scale 60%](./images/changingstuff.jpg)
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--- # Free online learning resources - ### [Python for Everybody](https://www.py4e.com/) - ### [Foundations of Python Programming](https://runestone.academy/runestone/books/published/fopp/index.html) - ### [CS50's Intro to Programming with Python](https://cs50.harvard.edu/python/2022/) - ### [QuantEcon](https://quantecon.org/lectures/)
- ### More specific resources in the notebooks