Introduction to Data Science 2019/20 fall

Practice sessions

Before the first practice session

Before the first practice session please make sure that you have Python version 3 and Jupyter Notebook installed on your computer. If you don't have it yet, then we recommend to choose between the following options:

1. Full Anaconda (can take up to 3 Gb of space):
   • Install Anaconda (Jupyter Notebook is included)
     • Anaconda (it has most of the necessary packages installed and GUI, however, it takes 3 Gb of space)
2. Mini-version of Anaconda (takes less space):
   • Install MiniConda and Jupyter Notebook
     • MiniConda (it only installs Python and Conda, no unnecessary space taken, more installing will be needed later)
     • conda install jupyter (install Jupyter Notebook)
3. No Anaconda, relying on the system Python:
   • Installing Jupyter Notebook to the system environment
     • In these instructions see the last section to install with pip and without Anaconda

After installing Jupyter Notebook you can simply run Jupyter notebook from command line:

• jupyter notebook (alternatively open Anaconda prompt to open Jupyter Notebook)

If you use Python in multiple courses and potentially different versions of packages, then you might want to create an environment into which you would install the packages needed for this course:

• Optional: Creating and using an environment:
  • Creating a new virtual environment for this course after installation of Conda
    • conda create -n IDS python=3.7 (create a new environment)
  • Now you should activate the environment before launching Jupyter notebook:
    • conda activate IDS or activate IDS (Windows) or source activate IDS (Linux, Mac) - activates the environment
    • conda install jupyter (make sure the environment is activated before installing)
    • jupyter notebook (make sure the environment is activated)
  • Any further installations must then also be done within the environment
  • The environment can be closed by conda deactivate

Practice session 01 - Sept 9-11 - Introduction

Useful materials: Jupyter Notebook Tutorial video

Practice session 02 - Sept 16-18 - DataFrames and Plotting

Notebook: Tutorial for DataFrames and Plotting