tomsb459 / ProgrammingForAnalytics

Course material for an intro to programming class for analytics students

Welcome to University Of Chicago's Programming For Analytics course

This course introduces programming concepts to students who are preparing to study analytics and data science

Please follow the instructions below to get your computer ready for this class.

Note Mac users: Once software is downloaded, if you double click to launch it, you may get permission errors. Try to right click on the downloaded software, pick "open" and continue. (Apple is trying to protect you from accidentally starting malware/virus)

Install Python (anaconda distribution)

Please install Python from this website: https://www.anaconda.com/distribution/ (modern computers are 64 bit so please pick that option)

Mac users: Accept all default prompts

Windows users: Accept all default prompts

Anaconda's distribution of Python is widely used in the industry, particularly among data scientists. This distribution makes it easy to use many libraries and packages for data analysis, building models, visualization, etc.

Once installed, please start jupyter notebook and execute code provided below

1. Start Anaconda Navigator and click Launch on the panel labeled Jupyter Notebook
2. Create new notebook from the web interface
3. Execute this code:

%%timeit
sum(range(1_000_000))

4. Execute this code:

from psutil import virtual_memory, disk_usage, cpu_count, os

bytes_in_gb = 1024**3

print("Memory:\t",round(virtual_memory().total/bytes_in_gb,4), "Gigabytes")
print("Disk:\t",round(disk_usage(os.path.abspath(os.sep)).total/bytes_in_gb,4), "Gigabytes")
print("CPUs:\t", cpu_count())

Install all packages in environment.yml

Available at https://github.com/falconair/ProgrammingForAnalytics/blob/master/environment.yml Also available in the repository you cloned in the previous step.

Using Mac terminal or Windows command prompt, change to the directory you just cloned and execute this command: conda env update --file environment.yml

Install RStudio

Please install R and RStudio.

1. Install R from this address: https://cran.rstudio.com/
2. Install RStudio from this address: https://rstudio.com/

Additional steps

1. Start RStudio
2. Follow directions at: https://irkernel.github.io/installation/#binary-panel

(install.packages('IRkernel') then IRkernel::installspec() for non-mac computers)

Install Git and Git Bash [Optional on day 1]

Please intall Git, a version control sotware, from this website: https://git-scm.com/downloads (you are ok to use default settings)

Note that this is a command-line tool. Once installed, you may not see a new icon to click. We will install a Desktop client to remedy this.

Although we don't make heavy use of version control, you will be introduced to the concept. Installing Git also installs "Git Bash," and comand line environment which simulates Unix/Linux. We will do several exercises which will require this environment.

Additional steps:

1. Install a Graphical interface to Git from this website: https://desktop.github.com/
2. [Windows users only] a. type cd (this will take you to your home directory) b. type echo cd >> .profile (this will make sure your home directory is loaded when you start Git Bash)

Install Visual Studio Code

Please install Visual Studio Code from https://code.visualstudio.com/

Additional steps:

Install Python extensions from https://marketplace.visualstudio.com/items?itemName=ms-python.python (visit that page and click "Install")

Clone this repository [Optional on day 1]

1. Visit this web page: https://github.com/falconair/ProgrammingForAnalytics
2. Click "Clone or download" and pick the "Download ZIP" option (unless you already have a GitHub account)