Data Bootcamp: Undergraduate Spring 2020


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 Topic Guide…


Python Fundamentals I: (1/29)

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: (2/5)

Handouts: Outline | Book chapter
Summary: Slicing; loops; function definitions and returns; dictionaries; packages; import; Pandas.
What’s due:


Cleaning: (2/12)

Handouts: Outline | Code_Pandas_Cleaning| Applications
Summary: Cleaning datasets.
What’s due: Problem Set 1 by 11:55pm


Filtering: (2/19)

Handouts: Outline | Code_Pandas_Cleaning| Applications
Summary: Filtering data.
What’s due: Problem Set 2 by 11:55pm


Shaping: (2/26)

Handouts: Shaping Outline| Book chapter
Code_Pandas_Shaping
Code examples | current indicators | demography | Airbnb
Summary: Aggregations and grouping data
What’s due:


3/4 - PROJECT TOUCHPOINT

What’s due: Problem Set 3 by 11:55pm


Matplotlib: (3/11 & 3/25)

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: Problem Set 4 by 11:55pm on 3/11


3/18 - NO CLASS (SPRING BREAK)


Merging: (4/1)

Handouts: Code_Pandas_Combining | Summarizing
Summary: Merging. Combining dataframes (merge, concatenate).
What’s due: Problem Set 5 by 11:55pm


4/8 & 4/15 - NO CLASS(HOLIDAY)


Regression: (4/22)

Handouts:
Summary: Basic Regression Analysis
What’s due:


Machine Learning: (4/29 & 5/6)

Handouts:
Summary: We will cover Scikit-learn, a Machine Learning package, to model various classification, regression and clustering algorithms.
What’s due:


5/6 - PROJECT TOUCHPOINT

What’s due: Problem Set 6 by 11:55pm


5/13 - FINAL PROJECT DUE