diff --git a/README.md b/README.md
index f782f02a..109818af 100644
--- a/README.md
+++ b/README.md
@@ -33,10 +33,10 @@ The primary goals of this workshop are:
 ||Setup|Download files required for the lesson|
 | 00:00 | 1. sMRI modalities | How is MR image acquired? What anatomical features do different modalities capture?  |
 | 00:45 | 2. sMRI preprocessing (Part 1: image clean-up) | How do we clean-up MR images and extract brains? |
-| 01:30 | 3. sMRI preprocessing (Part 2: image registration) | What are "templates", "spaces", "atlases"? What is spatial normalization? |
+| 01:30 | 3. sMRI preprocessing (Part 2: spatial normalization) | What are "coordinate spaces", "templates", "atlases"? What is image registration? |
 | 02:00 | 4. sMRI quantification | How do we delineate brain anatomy and quantify phenotypes? |
 | 02:30 | 5. sMRI quality-control | How do we identify image preprocessing failures? |
-| 03:00 | 6. Statistical analysis (Part 1: ROIs) | How to look at group differences in regional anatomical features? |
+| 03:00 | 6. Statistical analysis | How to compare regional anatomical differences in case-control groups? |
 | 04:00 | 7. Reproducibility considerations | How sensitive are the findings to your MR pipeline parameters? |
 | 04:30 | Finish | |
  
diff --git a/_episodes/03-Image_Preproc_Part2.md b/_episodes/03-Image_Preproc_Part2.md
index ccda0fe3..a1dff4e0 100644
--- a/_episodes/03-Image_Preproc_Part2.md
+++ b/_episodes/03-Image_Preproc_Part2.md
@@ -206,7 +206,8 @@ print("Subject space to refernce space mapping:\n {} --> {}".format(cut_coords,c
 {: .language-python}
 
 ~~~
-Text(0.5, 1.0, 'fsaverage reference space')
+Subject space to refernce space mapping:
+ (-40, 10, 0) --> (47,-2,11)
 ~~~
 {: .output}
 
diff --git a/setup.md b/setup.md
index b8c50321..878a733d 100644
--- a/setup.md
+++ b/setup.md
@@ -1,7 +1,94 @@
 ---
 title: Setup
 ---
-FIXME
+## Setting up the tutorial environment
+
+### Getting workshop material
+
+#### Method 1: Downloading directly from the repository
+
+On the GitHub repo (this page), click the green button that says "Clone or download", then click **Download ZIP**. Once downloaded, extract the ZIP file.
+
+#### Method 2: Using Git
+
+Using this method requires a (very) useful piece of software called <code>git</code>. The process of installing git depends heavily on whether you're using MacOS, Windows or Linux. Follow the instructions in the link below to set up <code>git</code> on your PC:
+
+[Installing Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
+
+Once you've installed <code>git</code>, open up your terminal and do the following:
+
+```
+git clone https://github.com/carpentries-incubator/SDC-BIDS-sMRI.git
+```
+
+This will download the repository directly into your current directory.
+
+### Setting up Python environment
+We use python version 3.6.0, but any newer version should also work (Python 2 versions haven't been tested). There are many methods to setting up a python environment but we'd recommend using some sort of virtual environment as to not break your system python install. Two methods (of many) are listed below:
+
+#### Method 1: Setting up conda environment (easiest) [Windows, Linux, MacOS]
+For easy set-up we recommend [Anaconda](https://www.anaconda.com/download/) to manage python packages for scientific computing. Once installed, setting up the python environment can be done quite easily:
+
+##### Windows
+1. Install Anaconda Python version 3.7
+2. Open **Anaconda Navigator**
+3. Click on **Environments** on the left pane
+4. Click **Create** then type in <code>SDC-BIDS-sMRI</code>
+5. In the <code>SDC-BIDS-sMRI</code> entry click the play button then click **Open Terminal**
+6. In terminal type:
+```
+conda install -y numpy pandas scipy scikit-learn matplotlib jupyter ipykernel nb_conda
+conda install -y -c conda-forge awscli
+pip install nilearn nibabel
+```
+7. Close the terminal, click on the play button again and open **Jupyter Notebook**
+8. Navigate to <code>SDC-BIDS-sMRI</code> folder you downloaded earlier.
+9. Done!
+
+##### Linux and MacOS
+
+After installing Anaconda, open terminal and type:
+
+```
+cd SDC-BIDS-sMRI
+conda create -p ./SDC_sMRI_workshop_2021
+source activate $(pwd)/SDC_sMRI_workshop_2021
+conda install numpy pandas scipy scikit-learn matplotlib jupyter ipykernel nb_conda
+conda install -c conda-forge awscli
+pip install nilearn nibabel
+
+```
+#### Method 2: Using pyenv (my favourite) [Linux, MacOS]
+An alternative method uses [pyenv](https://github.com/pyenv/pyenv) with [pyenv virtualenv](https://github.com/pyenv/pyenv-virtualenv). This is a favourite because it seamlessly integrates multiple python versions and environments into your system while maintaining use of pip (instead of conda).
+```
+cd SDC-BIDS-sMRI
+pyenv virtualenv 3.6.0 SDC_sMRI_workshop_2021
+echo SDC_sMRI_workshop_2021 > .python-version
+pip install --requirement requirements.txt
+```
+
+## Acquiring the data
+Most data (small files) are in the ./local_data dir 
+#TODO: Add download snippets for openneuro and aws
+
+```
+Finally open up the jupyter notebook to explore the tutorials:
+```
+cd SDC-BIDS-sMRI
+
+#Include below line if using anaconda environment
+source activate $(pwd)/SDC_sMRI_workshop_2021
+
+jupyter notebook
+```
+
+**Reference**
+
+[1] Gorgolewski KJ, Durnez J and Poldrack RA. Preprocessed Consortium for Neuropsychiatric Phenomics dataset [version 2; referees: 2 approved]. F1000Research 2017, 6:1262
+(https://doi.org/10.12688/f1000research.11964.2)
+
+{% include links.md %}
+
 
 
 {% include links.md %}