There is no text book or required reading materials for this module. However, there are some things that it may be useful to look at prior to or during the first stages of the course. Here are some suggestions:
If you have not used Python before, you may be interested to try an online tutorial course. There is free self-directed beginners courses at learnpython.og, which enables you to run small Python programs directly in your browser.
Another interactive Python tutorial is provided by W3Schools. This one does not contain much explanation but has a very good coverage of Python commands and code structure, as well has also enabling running code in a browser.
If you are familiar with Python or have learned enough to be comfortable to move on to learn about using Python in the Jupyter notebook environment, there is a good tutorial on that provided by DataQuest.
The material and support given for this module assumes that you have a working installation of Anaconda Python (which includes Jupyter) running on a suitably powerful computer. Installation of this software should in most cases be very straightforward and you may find it useful and interesting to install and experiment with this system prior to beginning the module.
It is recommended that you install the latest Anaconda version, which will include recent versions of Python (and the standard modules. Nearly all code examples that you will encounter in Programming for Datascience should work with Python 3.6 and with any later version. However, you may want to install modules that require later versions. Hence it is usually best to install the latest widley used version (e.g. 3.9 if installing during 2022).
Note: The autograders for Programming for Datascience were originally written for Python 3.7 and should work with code written using any recent version. Though you may use more recent modules, it is unlikely that you will need to use any built-in features of the Python language that were added after version 3.7. If you do, an autograder may not be able to run part of your code. In such a case, the autograder output will tell you which of your function definitions is causing the problem and it should be relatively easy to recode your solution in a way that is compatible with the autograder.
Anaconda is a very widely used and reliable verion of Python, so it should be easy to install on any of the major operating systems. All the information and downloads you should need are avalable from the Anaconda web pages:
Once you install Anacoda Python, your system should also be ready to run Jupyter notebook. Way to start the Jupyter notebook server program will vary depending on the operating system you are using.
jupyter notebook &On Windows systems, after installing jupyter you should be able to start
Jupyter by clicking on the Windows 'Start Menu' button and typing jupyter notebook
into the search window. (You may want to create a launch icon on your task
bar to start Jupyter more quickly.)
The first time you run Jupyter you may be prompted to choose which browser
you wish to use and which folder you want to be your Jupyter home directory.
On MacOS sytems the procedure for starting Jupyter is similar to that for Linux.
Futher instructions regarding starting Jupyter on all the popular operating systems can be found in the Jupyter Notebook Quick Start Guide