In [1]:

    

import pandas as pd
df = pd.read_csv('WorkshopSurvey.csv')
topics = df.filter(regex=r'^Please.', axis=1)
topics.columns = [t.split('[')[1][:-1] for t in topics.columns]
print('Number of responses: {}'.format(len(topics)))
topics.head(3)


    

    




Number of responses: 52






    
Out[1]:






  

    

      

      
Data Visualization Principles
      
Visualization: Dynamic & Interactive
      
Statistical/Machine Learning methods
      
Natural Language Processing (NLP)
      
Hadoop & MapReduce
      
UNIX Shell Tools & Programming
      
Introduction to R
      
Introduction to Python
      
Parallel Programming
      
Using Graphical Processing Units (GPUs)
      
Version Control with git
      
Reproducible Computations, Dynamic Documents with R and/or IPython
      
Introduction to Cloud Computing
      
Basics of Databases
    
  
  

    

      
0
      
Very interested
      
Very interested
      
Very interested
      
Very interested
      
Very interested
      
Very interested
      
Only slightly interested
      
Very interested
      
Very interested
      
Very interested
      
Very interested
      
Very interested
      
Very interested
      
Very interested
    
    

      
1
      
Very interested
      
Interested
      
Very interested
      
Only slightly interested
      
Only slightly interested
      
Not interested
      
Only slightly interested
      
Very interested
      
Very interested
      
Interested
      
Not interested
      
Interested
      
Only slightly interested
      
Only slightly interested
    
    

      
2
      
Very interested
      
Very interested
      
Very interested
      
Interested
      
Only slightly interested
      
Interested
      
Not interested
      
Not interested
      
Interested
      
Only slightly interested
      
Only slightly interested
      
Very interested
      
Only slightly interested
      
Only slightly interested
    
  

In [2]:

    

interest = topics.apply(pd.Series.value_counts)
interest = interest.reindex(['Very interested', 'Interested',
                             'Only slightly interested', 'Not interested'])

%matplotlib inline
from IPython.core.pylabtools import figsize
figsize(10, 8)
interest.T.sort_values('Very interested').plot(kind='bar', stacked=True)


    

    
Out[2]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f60728fdcf8>






    

In [3]:

    

for row in df['Suggest other topics for Workshops, Tutorials or Seminars'].dropna():
    print(row)
    print('\n')


    

    




Spatial Statistics, Game theory/decision making, Big data Applications in Economics, Business, GIS and etc, Training on presentation and writing skills on data science.


Hi! This is such a great idea!!! Is there a listserv I can join with updates and event announcements? I'm in the astronomy/physics dept. My UCD email is <retracted>, I would love to join if possible!

Ali


Introduction to R:
Titanic: Machine Learning from Disaster
<https://www.kaggle.com/c/titanic-gettingStarted>
<https://www.kaggle.com/c/titanic-gettingStarted/details/new-getting-started-with-r>


data scraping from the web.


Data mining


In the health fields, we could really use better support, and instruction, for multilevel modeling (i.e. mixed effects models).  We are starting to get a lot of repeated measures (time series) data about people's health, but few health researchers know how to analyze it.  And some of us have found that the statistical consulting available to us here at UCD is not real strong in this area.


Spatial Data Analysis, Spatial Modeling


I am an incoming graduate student of the Hydrologic Sciences Graduate Group of LAWR. I have little background in computing (some Matlab and ArcGIS model builder experience), and will utilize a lot of remote sensing images for my research. So, anything relating to processing these images (e.g. automation via Python, machine learning, speeding up the process) would


SQL, the basic of Spark and Hive