Data Bootcamp: MBA Spring 2019


Where and When



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; built-in 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_Cleaning|applications)
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_Combining|summarizing)
Summary: Combining dataframes (merge, concatenate). We will also cover Scikit-learn, 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_Combining|summarizing)
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