fixed requirements and added a few instructions

README.md

@@ -1,11 +1,18 @@
-# Project-Prep-Series-02-Data-Preparation
 Data preparation is a critical prerequisite to any data analysis or machine learning application. The purpose of the following workshop is to familiarize students with some data preparation (ie preprocessing) basics in Python 3+, using .csv and .png files.
 
 Part 1 will focus on .csv data preparation and we will be going through the `CSV_preparation.ipynb` notebook. This notebook contains a mini assignment, the answers of which can be found in `CSV_preparation_mini_assignment_answers.ipynb`.
 
 Part 2 will focus on .png (2D image) data preparation and we will be going through the `CSV_preparation.ipynb` notebook. This notebook also contains a mini assignment, the answers of which can be found in `CSV_preparation_mini_assignment_answers.ipynb`.
 
-## Part 1: About the CSV Data
+# Setting-Up
+Make sure you have Python `3.10.14` or above. To check, type `python -V` from your command-line.
+Next, run the following from your command-line:
+```
+conda create -n WorkshopEnv python=3.10.14
+conda activate WorkshopEnv
+```
+
+# Part 1: About the CSV Data
 
 ### 1.1 Behavioral data: `data_csv/unrestricted_HCP_behavioral.csv`
 
@@ -17,7 +24,7 @@ Structural MRI data was acquired from the WU-Minn HCP S1200 Release ([Van Essen
 
 Total brain volume (TBV) as well as the volume and total surface area of 6 other structures (left striatum, right striatum, left thalamus, right thalamus, left globus pallidus, right globus pallidus) were obtained by Nadia Blostein and colleagues from the [Computational Brain Anatomy (CoBrA) Laboratory](https://cobralab.ca/) (Cerebral Imaging. Center, Douglas Mental Health University Institute) under the supervision of Dr. Mallar Chakravarty. Images were processed and volume and surface area measures extracted using a standard lab pipeline that involved the publicly available [minc-bpipe library](https://github.com/CoBrALab/minc-bpipe-library) and [MAGeTbrain segmentation algorithm](https://github.com/CobraLab/MAGeTbrain). More thorough details on image processing and volume obtention can be found [here](https://www.biorxiv.org/content/10.1101/2022.04.11.487874v1).
 
-## Part 2: About the PNG Data
+# Part 2: About the PNG Data
 
 We will be using 20 2D chest X-ray images (.png files) from the 500 images available in the open-access [Pulmonary Chest X-Ray Abnormalities](https://www.kaggle.com/kmader/pulmonary-chest-xray-abnormalities) Kaggle dataset. This data was collected by the National Library of Medicine (USA) in collaboration with Shenzhen No.3 People’s Hospital, Guangdong Medical College (China). More info can be found in `data_png/NLM-ChinaCXRSet-ReadMe.docx`. `chest_xrays_pngs` contains the 20 .png files, 10 of which represent a normal lung (`CHNCXR_ID_0.png`) and 10 of which represent an abnormal lung (`CHNCXR_ID_1.png`).
 

requirements.txt

@@ -6,5 +6,4 @@ protobuf==4.22.4
 scikit_image==0.19.3
 scikit_learn==1.2.2
 scipy==1.10.1
-seaborn==0.12.2
-skimage==0.0
+seaborn==0.12.2