Resources

Download the repository (that includes the data) here: https://github.com/chendaniely/2017-10-26-python_crash_course/archive/gh-pages.zip additional datasets, if you already downloaded the zipfile:

• https://github.com/chendaniely/pandas_for_everyone/raw/master/data/scientists.csv

Other paper references:

• Wickham 2014 - Tidy Data
• Noble 2009 - Organizing Projects
• Setting up the notebook extensions
• https://github.com/Jupyter-contrib/jupyter_nbextensions_configurator
• Python properties: https://github.com/chendaniely/2015-scipy-lightning-mann-properties/blob/master/slides.ipynb
• odo: the shapeshifter: https://odo.readthedocs.io/en/latest/
• dash for distributed computing

Data Camp Courses

• Cleaning Data in Python

  • Review from the class

• Supervised Learning with scikit-learn

  • All of it
  • Particularly, Ch4: Preprocessing and pipelines

• Unsupervised Learning in Python

  • Clustering
  • Projections

• Pandas Foundations

  • Ch3: Time series in pandas

• Manipulating Time Series Data in Python

  • All of it

• Intro to Python for Data Science

  • Ch4: NumPy

• Importing Data in Python (Part 1)

  • Importing data from other file types
  • SAS/Stata
  • HDF5
  • MATLAB
  • SQL

• Importing Data in Python (Part 2)

  • If you are working with web interfaces and APIs

• Importing & Managing Financial Data in Python

• Python Data Science Toolbox (Part 1)

  • Ch3: Lambda functions and error-handling

• Python Data Science Toolbox (Part 2)

  • More "pythonic" code
  • Ch3: Bringing it all together!
    • Iterators to load data in chunks

• Manipulating DataFrames with pandas

  • Ch2: Advanced indexing: MultiIndex
  • Ch4: Grouping data

• Intermediate Python for Data Science

  • Ch1: Matplotlib

• Introduction to Data Visualization with Python

• Interactive Data Visualization with Bokeh

• Statistical Thinking in Python (all the parts)

• Natural Language Processing Fundamentals in Python (as needed)

• Introduction to Databases in Python

  • Using SQLAlchemy in stead of raw SQL querry strings

Schedule

Syllabus

• Anaconda, Python, IPython, and Jupyter notebooks
• Spyder, nteract, Rodeo IDEs
• Installing packages

• Loading libraries, dot notation, and namespaces
• Load, subset, slice, filter data
• The Pandas Series and DataFrame object
• Quick whirlwind of grouped operations and plots

• Manual creation of data objects
• Series and DataFrame object methods
• Conditional and ‘fancy’ subsetting
• Saving data: csv, excel, feather, odo library

• Parts of a matplotlib figure
• The Seaborn library

Setup

To participate in a workshop, you will need access to the software described below. In addition, you will need an up-to-date web browser.

We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.

Testing If Anaconda was installed

1. Open up the Anaconda Command Prompt
2. Type "ipython" into the prompt
3. You should see Python open up with Python 3.6.x and using the Anaconda distribution
4. Type "quit()" to exit
5. Type "jupyer notebook" to launch the notebook (this may take a while if it is the first time you are launching it)
6. Close the anaconda prompt when you're done

Installing Packages

To install the packages needed for the class you can follow the instructions below:

1. Open your Anaconda Command prompt (Windows)
2. Run the following lines of code (note that ctrl+v may not paste in windows, you can paste by pressing shift + insert, or by clicking the icon to the top right of the Anaconda Command promt > edit > paste)

conda install xlwt openpyxl feather-format seaborn statsmodels scikit-learn regex wget odo numba
pip install lifelines pandas-datareader