Data Bootcamp: MBA Fall 2018
Where and When
- Instructor: Benjamin Zweig (email@example.com)
- Teaching fellow: Yu-Ping Lin (firstname.lastname@example.org)
- office hour: KMC 7-100, Thursday 1 PM - 2 PM (knock the door, I will open for you)
Meeting times: Tuesday 6 PM- 9 PM
- Meeting place: KMC 4-80
THE SYLLABUS. All the important details about the course, procedures, important dates, etc.
THE BOOK. The topics in the first half are all in the book. We will follow this closely. At the book link, click the large blue Read button to read online – or download the pdf. Both come with links.
NOTEBOOKS Github repository of notebooks used in class.
DISCUSSION GROUP Post your doubts on NYU Classes forum tab.
Code Practice 1 Due Date: October 11, 2018
Code Practice 2 Due Date: October 18, 2018.
Code Practice 3 Due Date: October 25, 2018.
Midterm Exam: November 8, 2018. Quick info: In class, open book, open internet if the wireless is up, bring one page of notes.
Final Project Due Date: TBA
Code Pratice Submissions
Assignments will be posted on NYU Classes. Submit your python code in PDF format or ipython notebook in NYU Classes.
Week By Week Guide…
Topic 1. Data + Python = Magic!
Day and Date: Thrusday 9/27/2018
Handouts: Outline | Book | Three ideas
Examples: Gapminder | cancer screening | Uber in NYC | medical expenditures | mortality | earthquake | Gender pay gap | Fertility | Vaccines
Summary: It’s nice to have skills; installing Anaconda; Spyder and Jupyter/IPython; data; questions; idea machines.
Topic 2. Python fundamentals 1
Day and Date: Thursday 10/04/2018
Handouts: Outline | Book chapter
Summary: Calculations; assignments; strings; lists; tuples; built-in functions; objects; methods; tab completion.
Topic 3. Python fundamentals 2
Day and Date: Thursday 10/11/2018
Handouts: Outline | Book chapter | Code Practice #1 (Due October 11)
Summary: True and False; comparisons; conditionals; slicing; loops; function definitions and returns; dictionaries.
Topic 4. Data input: Packages and Pandas
Day and Date: Thursday 10/18/2018
Handouts: Outline | Book chapter | Code | Code Practice #2 (Due October 18) (code template)
Summary: Packages; import; Pandas; csv files; reading csv/xls files; dataframes; columns; index; APIs.
Topic 5. Python graphics: Matplotlib fundamentals
Day and Date: Thursday 10/25/2018
Handouts: Outline | Book chapter | Code (Download “Raw” as ipynb) | Code Practice #3 (Due by October 25)
Summary: Three approaches to graphics: dataframe plot methods, plot(x,y), and fig/ax objects and methods; lines, scatters, bars, horizontal bars, styles.
Topic 6. Review & applications
Day and Date: Thursday 11/01/2018
Handouts: Outline | Code (review | applications)
Summary: Exam review, followed by applications to get us thinking about interesting datasets and how to work with them.
Topic 7. Midterm Exam: November 8, 2018
Posted after class: Exam with answers
Topic 8. Thinking about projects and Introduction to Pandas DataFrame
Day and Date: Thursday 11/15/2018
Handouts: Outline | Project Examples | Code (examples | current indicators | demography | Airbnb)
Summary: Projects: say something interesting with data. Idea machines. Examples.
Topic 9. Pandas Cleaning and Statmodels
Day and Date: Thursday 11/29/2018
Handouts: Outline | Code_Pandas_Cleaning
Summary: Pandas has incredible facilities for managing data. We look at fixing numbers misidentified as strings, managing missing observations, selecting variables and observations, and the
We also cover statmodels, python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration.
Topic 10. Pandas Shaping and Scikit-learn
Day and Date: Thursday 12/06/2018
Handouts: Outline | Code_Pandas_Shaping
Summary: Here we introduce four key methods for “shaping” our data:
df.unstack. When we say shaping we mean manipulating the data so we get specific row and column labels.
We also cover Scikit-learn, Machine Learning package to model various classification, regression and clustering algorithms.
Topic 11. Pandas Combining and Scikit-learn
Day and Date: Thursday 12/13/2018
Summary: Combining dataframes (merge, concatenate). Statistics (mean, median, quantiles), categorical variables, grouping data by categories, counts and statistics by category.
We will also cover remaining topics in Scikit-learn in this class.
Topic 12. Wrap up.
Day and Date: Thursday 12/20/2018 Discussion regarding project status, challenges, etc.