DataCamp Code

A commonplace book and portfolio for experiments, projects, and course work relating to data science and data analysis. Covers data manipulation, visualization, Python programming, and real-world analysis projects.

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Root-Level Notebooks

Notebook	Description
inner_joins_31026.ipynb	Follows the "Joining Data with Pandas" DataCamp course. Demonstrates inner joins to find the alderman of the most populous ward in Chicago. Includes notes on sourcing government data via Socrata.
inner_joins_31126.ipynb	Playground for experimenting with small toy datasets to verify understanding of join mechanics.
genres_and_sequels_31226.ipynb	Multi-table merge exploration using DataCamp movie datasets to identify which genre has the most sequels.
Python_Review_31226.ipynb	Review notes covering general Python and Pandas fundamentals.
general_notes_and_snips.ipynb	Scrapbook of useful code snippets and techniques, especially for tricky or non-obvious problems.

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Courses

Joining Data with Pandas

Notes in Data Notes/Joining Data with Pandas/

Detailed reference notes and worked examples for every join type:

• Inner, Left, Right, Outer joins
• Semi-join and Anti-join
• Visual diagrams and code examples for each

Exploratory Data Analysis in Python

Exploratory Data Analysis in Python/

Four-notebook module covering the full EDA workflow:

• Getting to know a dataset (summary statistics, dtypes)
• Data cleaning and imputation
• Identifying relationships and correlations
• Turning findings into actionable insights

Intro to Data Viz with Matplotlib

Intro to data viz with Matplotlib/

Four-notebook course covering:

• Matplotlib basics
• Plotting time-series data
• Quantitative comparisons and statistical visualizations
• Preparing publication-ready figures

Intro to Data Viz with Seaborn

Intro to data viz with Seaborn/

Six-notebook course covering:

• Seaborn fundamentals
• Visualizing two quantitative variables
• Visualizing categorical vs. quantitative variables
• Integration with Pandas DataFrames
• Plot customization and styling

Intro to Functions in Python

Intro to functions in Python/

Three-notebook course covering:

• Writing functions
• Default arguments, variable-length arguments, and scope
• Lambda functions and error handling

Python Toolbox

Python Toolbox/

Three-notebook course on advanced Python patterns:

• List comprehensions and generators
• Iterators and iteration patterns
• Case study applying these tools

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Projects

Analyzing Crime in Los Angeles

Projects/Analyzing Crime in Los Angeles/

End-to-end analysis of crime patterns in LA using a 27 MB dataset (crimes.csv). Outputs and visualizations are saved to the Outputs/ subdirectory.

Nobel Prize Winners

Projects/NobelPrizeWinners/

Analysis of historical Nobel Prize winner data (data/nobel.csv), exploring trends across categories, countries, and time.

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Reference Materials

Cheatsheets

Cheatsheets/

• Pandas cheat sheet
• Matplotlib cheat sheet
• Exploratory Data Analysis in Python cheat sheet
• Data Analysis Workflow diagram

Data Notes

Data Notes/

Markdown notes on key topics including .merge() usage and join types. TO-DO.md tracks topics still to be covered (concat, query, merge_ordered(), merge_asof(), melt).

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Datasets

datasets/

~219 MB of datasets used across projects and exercises, including:

Dataset	Size	Topic
WDICSV.csv	188 MB	World Development Indicators
crimes.csv	27 MB	Los Angeles crime data
raw-responses.csv	2.2 MB	Survey responses
planes.csv	1.1 MB	Aviation data
divorce.csv	195 KB	Divorce prediction
ds_salaries_clean.csv	29 KB	Data science job salaries
countries-of-the-world.csv	38 KB	Country statistics
seattle_weather.csv / austin_weather.csv	—	Weather data
student-alcohol-consumption.csv	37 KB	Youth survey
mpg.csv	21 KB	Vehicle fuel economy
movies.p / movie_to_genres.p / sequels.p	—	Movie metadata (pickled)