.. _complabs_label: Computer Labs ******************** .. note:: Please bring your own laptop to the weekly labs if you can. In our experience, many students are able to bring their own computers and actually prefer to do so. There's a good chance that your own laptop is faster and more flexible to configure. Also, you can take your laptop home with you and continue your econometrics work there. If you do not have your own laptop: don't worry! We run a few labs in dedicated ANU computer rooms in the Copland building. You can use the PCs in those computer rooms and we will explain to you how to set up a powerful cloud computer that you can even access remotely (for example, if you have your own PC at home). Instructions are in :ref:`jupyter_label`. We will help and assist you with the computer setup during our first computer labs (in week 2). .. _datasets: Datasets ============ I will post data sets for our econometric work here. * :download:`World Development Indicators <../computer_tutes/Python/datasets/world_bank_wdi.csv>` * | :download:`EarningsHeight dataset <../computer_tutes/Python/datasets/earnings_and_height.csv>` and its :download:`description <../computer_tutes/Python/datasets/Earnings_and_Height_Description.pdf>` | (from Stock and Watson, *Introduction to Econometrics*, 4th global edition) * | :download:`Growth dataset <../computer_tutes/Python/datasets/growth.csv>` and its :download:`description <../computer_tutes/Python/datasets/Growth_Description.pdf>` | (from Stock and Watson, *Introduction to Econometrics*, 4th global edition) * | :download:`Birthweight dataset <../computer_tutes/Python/datasets/birthweight.csv>` and its :download:`description <../computer_tutes/Python/datasets/Birthweight_Description.pdf>` | (from Stock and Watson, *Introduction to Econometrics*, 4th global edition) * | :download:`LeadMortality dataset <../computer_tutes/Python/datasets/lead_mortality.csv>` and its :download:`description <../computer_tutes/Python/datasets/Lead_Mortality_Description.pdf>` | (from Stock and Watson, *Introduction to Econometrics*, 4th global edition) * | :download:`cpi_aus_2024 dataset <../computer_tutes/Python/datasets/cpi_aus_2024.csv>` | (was updated on 6 May 2024) * | :download:`gdp_us_2024 dataset <../computer_tutes/Python/datasets/gdp_us_2024.csv>` | (was updated on 6 May 2024) Week 1 ======= Don't panic! In-person computer labs only start in week 2. But I would like you to use the first week to get **Python-ready**: .. important:: In preparation for the week 2 computer lab, please get Python-ready! We recommend two options: * (**preferred**) installing Anaconda on your own laptop, or * setting up a Google Colab account. Go to :ref:`jupyter_label` and follow the information offered there. Week 2 ======= Before attending this week's computer lab, make sure to prepare your computer by following the steps given in :ref:`jupyter_label`. * Download the following Juypter notebook into your ``notebooks`` folder: :download:`week_2.ipynb <../computer_tutes/Python/notebooks/week_2.ipynb>`. * Launch Jupyter, like so: * Anaconda users: open **Anaconda Navigator** through which you can launch Jupyter. **Do not double-click the** ``week_2.ipynb`` **file!** * Colab users: go to your Google Drive wherever you have saved ``week_2.ipynb`` and double click on it. Then Google will automatically launch a Jupyter session for you. * Now you can work on the file by editing and executing code cells. You will learn how to this during this week's lab. .. important:: Make sure you downlaod the above notebook into your folder named ``notebooks`` as explained in my instructions given in :ref:`jupyter_label`. Also make sure you have saved the data set *World Development Indicators* into your folder ``datasets`` as explained in my instructions given in :ref:`jupyter_label`. Week 3 ======= * Put the following Juypter notebook inside your ``notebooks`` folder: :download:`week_3.ipynb <../computer_tutes/Python/notebooks/week_3.ipynb>`. * Launch Jupyter, like so: * Anaconda users: open **Anaconda Navigator** through which you can launch Jupyter. **Do not double-click the** ``week_3.ipynb`` **file!** * Colab users: go to your Google Drive wherever you have saved ``week_3.ipynb`` and double click on it. Then Google will automatically launch a Jupyter session for you. * Now you can work on the file by editing and executing code cells. Week 4 ======= Before you get started on the week 4 lab exercises, just a random note that should be obvious: .. important:: All coding this semester has to be done in Python. Do not use other software! Also: Only use packages or libraries that were used during the weekly computer labs! Do not use any other packages or libraries. We understand that some of you may have some familiarity already with Python and perhaps would like to use different packages or libraries to work on our notebooks, but we do not permit this. Sorry. * Put the following Juypter notebook inside your ``notebooks`` folder: :download:`week_4.ipynb <../computer_tutes/Python/notebooks/week_4.ipynb>`. * Make sure to put the EarningsHeight csv-file into your ``datasets`` folder (see at top of this page) * Launch Jupyter, like so: * Anaconda users: open **Anaconda Navigator** through which you can launch Jupyter. **Do not double-click the** ``week_4.ipynb`` **file!** * Colab users: go to your Google Drive wherever you have saved ``week_4.ipynb`` and double click on it. Then Google will automatically launch a Jupyter session for you. * Now you can work on the file by editing and executing code cells. Week 5 ======= * Put the following Juypter notebook inside your ``notebooks`` folder: :download:`week_5.ipynb <../computer_tutes/Python/notebooks/week_5.ipynb>`. * Make sure to put the EarningsHeight csv-file into your ``datasets`` folder (see at top of this page) * Launch Jupyter, like so: * Anaconda users: open **Anaconda Navigator** through which you can launch Jupyter. **Do not double-click the** ``week_5.ipynb`` **file!** * Colab users: go to your Google Drive wherever you have saved ``week_5.ipynb`` and double click on it. Then Google will automatically launch a Jupyter session for you. * Now you can work on the file by editing and executing code cells. Week 6 ======= * Put the following Juypter notebook inside your ``notebooks`` folder: :download:`week_6.ipynb <../computer_tutes/Python/notebooks/week_6.ipynb>`. * Make sure to put the Growth csv-file into your ``datasets`` folder (see at top of this page) Week 7 ======= * Put the following Juypter notebook inside your ``notebooks`` folder: :download:`week_7.ipynb <../computer_tutes/Python/notebooks/week_7.ipynb>`. Week 8 ======= * Put the following Juypter notebook inside your ``notebooks`` folder: :download:`week_8.ipynb <../computer_tutes/Python/notebooks/week_8.ipynb>`. * Make sure to put the Birthweight csv-file into your ``datasets`` folder (see at top of this page) Week 9 ======= * Put the following Juypter notebook inside your ``notebooks`` folder: :download:`week_9.ipynb <../computer_tutes/Python/notebooks/week_9.ipynb>`. * Make sure to put the LeadMortality csv-file into your ``datasets`` folder (see at top of this page) Week 10 ======= * Put the following Juypter notebook inside your ``notebooks`` folder: :download:`week_10.ipynb <../computer_tutes/Python/notebooks/week_10.ipynb>`. * Make sure to put the cpi_aus_2024 csv-file into your ``datasets`` folder (see at top of this page) Week 11 ======= * Put the following Juypter notebook inside your ``notebooks`` folder: :download:`week_11.ipynb <../computer_tutes/Python/notebooks/week_11.ipynb>`. * Make sure to put the cpi_aus_2024 csv-file into your ``datasets`` folder (see at top of this page) Week 12 ======= .. note:: This week we're posting a Jupyter notebook that contains a **complete worked solution**. All you need to do is run the code cells to produce Python output. Your tutor will guide you through each and every step! * Put the following Juypter notebook inside your ``notebooks`` folder: :download:`week_12.ipynb <../computer_tutes/Python/notebooks/week_12.ipynb>`. * Make sure to put the gdp_us_2024 csv-file into your ``datasets`` folder (see at top of this page)