IMSE 586 - Course intro

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## IMSE 586 
## Big Data Analytics and Visualization

### Course Introduction
 
### Instructor: Fred Feng

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## About the instructor

Fred Feng

.red[fredfeng]@umich.edu

Associate Professor @ IMSE

Research group: [fenggroup.org](https://fenggroup.org/)
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![:scale 40%](./images/feng.png)
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# Course website

.huge[<https://imse586.github.io>]

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# Some general study tips

- Type along during the coding sessions
  - Learning by doing

.center[![:scale 40%](./images/ridebike.jpg)]

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# Some general study tips

- The homework are meant to be an essential part of your learning experience. Start early!

- Ask for help

- Email us <imse586instructors@umich.edu>
 - Come to the office hours

- Back up your work (homework, project)

- [Dropbox at UM](https://its.umich.edu/communication/collaboration/dropbox)

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# Why programming in data analytics?

- Flexibility & customization
- Reproducible research
- Collaborative work
- Version control

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# Why Python?

.center[![:scale 60%](./images/python-logo.png)]

- Open source, free, and widely available
- Massive open-source community

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# Why Python?

- ### Extensive libraries
  - [NumPy](https://numpy.org/)
  - [pandas](https://pandas.pydata.org/)
  - [Matplotlib](https://matplotlib.org/)
  - [Seaborn](https://seaborn.pydata.org/index.html)
  - [statsmodels](https://www.statsmodels.org/)
  - [scikit-learn](https://scikit-learn.org/)
  - and many more

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.center[![:scale 100%](./images/pythonvislandscape.png)]

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.center[![:scale 80%](./images/xkcd-python.png)]

<div style="margin-top: -20px; text-align: center;"><a class="sourcelink" href="https://xkcd.com/353/">Image source</a></div>

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# Why Python?

- ### Beginner friendly

- High-level programming language
  - Intuitive syntax

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# Why Python?

- ### Widely popular

- [IEEE: Top Programming Languages 2024](https://spectrum.ieee.org/top-programming-languages-2024)

- [Widely used in industry](https://scikit-learn.org/stable/testimonials/testimonials.html)

- Job qualifications

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# JupyterLab

To run Python, we will use a browser-based application called [JupyterLab](https://jupyter.org/).

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# Install Python / JupyterLab

We will use Anaconda, a common Python distribution bundled with other popular tools.

You can follow the installation instruction at

.small[<https://github.com/imse440/imse440/blob/main/docs/source/software-setup.rst>]

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# Local recourses of data science

* [Michigan Institute for Data Science (MIDAS)](https://midas.umich.edu/) -->

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# Assignments

- [Software installation](https://www.imse440.org/software-setup.html)
- Watch & follow along the "Python basics" video
- Read the syllabus in its entirety
- Course survey (link on Canvas)
- Suggested readings
  - [Big Data Analytics: What It Is, How It Works, Benefits, And Challenges](https://www.tableau.com/learn/articles/big-data-analytics)
  - [The JupyterLab Interface](https://jupyterlab.readthedocs.io/en/stable/user/interface.html)
  - [The scientific paper's obsolete-here's what's next](https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/) -->