Data Bootcamp: Spring 2021
Where and When
- Instructor: Benjamin Zweig (bz425@stern.nyu.edu)
- Teaching Fellow: Praxal Patel (psp334@nyu.edu)
- Meeting times: Thursday (2:00PM - 4:50PM)
- Meeting place: Online (Meeting links can be under Zoom tab on NYU classes)
Important Links
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THE SYLLABUS All the important details about the course, procedures, important dates, etc.
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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.
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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; built-in 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 Scikit-learn, Machine Learning package to model various classification, regression and clustering algorithms.
What’s due: