Data Bootcamp: MBA Spring 2019
Where and When
 Instructor: Benjamin Zweig (bzweig@stern.nyu.edu)
 Teaching fellow: Aditya Vashishta(aav331@stern.nyu.edu)
Rahul Menon(rrm423@stern.nyu.edu)  office hour: TBA

Meeting times: TBA
 Meeting place: TBA
Important Links

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.

Final Project Due Date: TBA
Code Pratice Submissions
Assignments will be posted on NYU Classes. Submit your python code in ipython notebook format on NYU Classes.
Week By Week Guide…
Class 1: Python Fundamentals 1
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.
Class 2: Python fundamentals 2
Handouts: Outline  Book chapter
Summary: Calculations; assignments; strings; lists; tuples; builtin functions; objects; methods; tab completion.
What’s due: Problem Set 1
Class 3: Python fundamentals 3
Handouts: Outline  Book chapter 
Summary: True and False; comparisons; conditionals; slicing; loops; function definitions and returns; dictionaries.
What’s due: Problem Set 2
Class 4: Data Input, Packages, & Pandas
Handouts: Outline  Book chapter  Code  (code template)
Summary: Packages; import; Pandas; csv files; reading csv/xls files; dataframes; columns; index; APIs.
What’s due: Problem Set 3
Class 5: Matplotlib
Handouts: Outline  Book chapter  Code (Download “Raw” as ipynb) 
Summary: Three approaches to graphics: dataframe plot methods, plot(x,y), and fig/ax objects and methods; lines, scatters, bars, horizontal bars, styles.
What’s due: Problem Set 4
Class 6: Pandas Methods & Review
Handouts:
Outline  Project Examples  Code (examples  current indicators  demography  Airbnb)
Summary: Different Pandas methods and review.
What’s due: Team submission (Just the names..)
Class 7: Cleaning & Filtering
Handouts: Outline Code_Pandas_Cleaningapplications)
Summary: Cleaning and filtering data.
What’s due: Problem Set 5
Class 8: Regression
Handouts: Outline  Project Examples  Code (examples  current indicators  demography  Airbnb)
Summary: Basic Regression Analysis
What’s due: Project ideas submission
Class 9: Shaping
Handouts: Outline  Code_Pandas_Shaping
Summary: Aggregations and grouping the data.
What’s due: Problem Set 6
Class 10: Combining & intro to Machine Learning
Handouts:
(Code_Pandas_Combiningsummarizing)
Summary: Combining dataframes (merge, concatenate). We will also cover Scikitlearn, Machine Learning package to model various classification, regression and clustering algorithms.
What’s due: Submit project data & show input with basic diagnostics
Class 11: Project help & Machine Learning
Handouts: (Code_Pandas_Combiningsummarizing)
Summary: More into ML and project discussions.
What’s due: Problem Set 7
Class 12: Data Analysis workflow
Summary Walk through a data analysis pipeline from importing, exploring, cleaning, visualizing and forming analysis.
What’s due: Problem Set 8