Data Bootcamp: Fall 2020
Where and When
 Instructor: Benjamin Zweig (bzweig@stern.nyu.edu)
 Teaching Fellow: Praxal Patel (psp334@nyu.edu)
 Meeting times: Tuesday and Thursday (3:30PM  4:45PM)
 Meeting place: Online (Meeting links can be under Zoom tab on NYU classes)
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.
Problem Set Submissions
Assignments will be posted on NYU Classes. Submit your python code in ipython notebook format on NYU Classes.
Week By Week Topic Guide…
Python Fundamentals I:
Handouts: Outline 1  Outline 2  Book  Three ideas
Examples: Gapminder  Cancer Screening  Uber in NYC  Medical Expenditures  Mortality  Earthquake  Gender Pay Gap  Fertility  Vaccines
Summary: Intro; calculations; assignments; strings; lists; tuples; builtin functions; objects; methods; tab completion; True and False; comparisons; conditionals; slicing; loops; function definitions and returns; dictionaries.
What’s due:
Python Fundamentals II, Intro to Packages:
Handouts: Outline  Book chapter
Summary: Slicing; loops; function definitions and returns; dictionaries; packages; import; Pandas.
What’s due:
Cleaning:
Handouts: Outline  Code_Pandas_Cleaning Applications
Summary: Cleaning datasets.
What’s due:
Filtering:
Handouts: Outline  Code_Pandas_Cleaning Applications
Summary: Filtering data.
What’s due:
Shaping:
Handouts: Shaping Outline Book chapter
Code_Pandas_Shaping
Code examples  current indicators  demography  Airbnb
Summary: Aggregations and grouping data
What’s due:
Matplotlib:
Handouts: Matplotlib Outline  Book chapter
Code_Matplotlib (Download “Raw” as ipynb)
Code examples  current indicators  demography  Airbnb
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:
Merging:
Handouts: Code_Pandas_Combining  Summarizing
Summary: Merging. Combining dataframes (merge, concatenate).
What’s due:
Regression:
Handouts:
Summary: Basic Regression Analysis
What’s due:
Machine Learning:
Handouts:
Summary: We will cover Scikitlearn, Machine Learning package to model various classification, regression and clustering algorithms.
What’s due: