Data Bootcamp: MBA Summer 2019


Where and When



Problem Set 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 (May 22, 2019): 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 (May 29, 2019): 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 (Jun 3, 2019): Python fundamentals 3, Intro to packages and Pandas

Handouts: Outline | Book chapter |
Summary: True and False; comparisons; conditionals; slicing; loops; function definitions and returns; dictionaries.
Packages; import; Pandas; What’s due: Problem Set 2; Team submission (Just team member names..)


Class 4 (Jun 5, 2019): Cleaning & Filtering

Handouts: Outline |Code_Pandas_Cleaning|applications)
Summary: Cleaning and filtering data.
What’s due: Problem Set 3; Project ideas submission


Class 5 (Jun 12, 2019): 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 (Jun 17, 2019): Shaping and Continuation of Matplotlib

Handouts: (https://github.com/nyusterndatabootcamp/teaching_materials/blob/master/documents/bootcamp_topic_pandas-shape.pdf) | Code_Pandas_Shaping
Code (examples | current indicators | demography | Airbnb)
Summary: Aggregations and grouping the data
What’s due: Problem Set 5


Class 7 (Jun 19, 2019): Regression

Handouts: Summary: Basic Regression Analysis
What’s due: Nothing!


Class 8 (Jun 24, 2019): Merging

Handouts: Outline |Code_Pandas_Cleaning|applications)
Summary: Merging
What’s due: Problem Set 6


Class 9 (Jun 26, 2019): Machine Learning 1

Handouts: [Outline] What’s due: Nothing!


Class 10 (Jul 1, 2019): Machine Learning 2

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: Problem Set 7, Submit project data & show input with basic diagnostics


Class 11 (Jul 3, 2019): Wrap Up & Data Analysis workflow

Handouts: (Code_Pandas_Combining|summarizing)
Summary: More into ML and project discussions.
Walk through a data analysis pipeline from importing, exploring, cleaning, visualizing and forming analysis.
What’s due: Problem Set 8

Final Project Due Date: Jul 10, 2019