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mkdocs.yml
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site_name: DSSG Hitchhickers guide
theme:
name: 'material'
palette:
primary: 'red'
accent: 'deep orange'
font:
text: 'Ubuntu'
code: 'Ubuntu Mono'
logo:
icon: 'bubble_chart'
favicon: 'assets/images/favicon.ico'
docs_dir: sources
repo_name: 'dssg/hitchhikers-guide'
repo_url: https://github.com/dssg/hitchhikers-guide
edit_uri: edit/master/sources
site_description: 'DSSG Hitchhikers guide'
extra:
social:
- type: 'github'
link: 'https://github.com/dssg/hitchhikers-guide'
- type: 'twitter'
link: 'https://twitter.com/datascifellows'
- type: 'linkedin'
link: 'https://linkedin.com/company/center-for-data-science-and-public-policy-university-of-chicago'
markdown_extensions:
- admonition
- toc:
permalink: "#"
baselevel: 1
toc_depth: 3
separator: "-"
- codehilite:
guess_lang: false
- def_list
- footnotes
- meta
- pymdownx.arithmatex
- pymdownx.b64
- pymdownx.caret
- pymdownx.critic
- pymdownx.details
- pymdownx.extrarawhtml
- pymdownx.keys
- pymdownx.highlight
- pymdownx.snippets
- pymdownx.superfences
- pymdownx.tasklist:
custom_checkbox: true
- pymdownx.tilde
extra_javascript:
- "js/mermaid.min.js"
- 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML'
nav:
- Home: README.md
- What is in this curriculum?: what-is-dssg.md
- DSSG Locations: host-cities.md
- DSSG Manual (for summer fellows):
- What is in this manual: dssg-manual/README.md
- Goals of the Fellowship: dssg-manual/conduct-culture-and-communications/goals.md
- The DSSG Environment: dssg-manual/conduct-culture-and-communications/environment.md
- Summer overview: dssg-manual/summer-overview/README.md
- Code of conduct: dssg-manual/conduct-culture-and-communications/README.md
- Curriculum:
- What is in this curriculum: curriculum/README.md
- Sample curriculum for Summer 2019: curriculum/sample.md
- Skills you need throughout a project:
- Ethics, Bias, Fairness: curriculum/skills/ethics_bias_fairness.md
- Communication:
- Intro: curriculum/communication/README.md
- Presentations: curriculum/communication/presentations.md
- Writing reports: curriculum/communication/writing.md
- Visualization: curriculum/eda/visualization.md
- User interface: curriculum/communication/user_interface.md
- Good Software Practices:
- Intro: curriculum/programming_best_practices/README.md
- Good repos, good code: curriculum/programming_best_practices/reproducible-software/good_repos_good_code.md
- Legible, good code: curriculum/programming_best_practices/legible-good-code/README.md
- Writing tests: curriculum/programming_best_practices/test-test-test/README.md
- Reproducible software: curriculum/programming_best_practices/reproducible-software/README.md
- Pimp my dotfiles!: curriculum/programming_best_practices/pimp-my-dotfiles/README.md
- Domain Understanding: curriculum/skills/domain_understanding.md
- Project Scoping:
- Scoping overview: curriculum/scoping/overview.md
- Project workflow: curriculum/scoping/project_workflow.md
- Data Maturity evaluation: curriculum/scoping/dme.md
- Project deliverables: curriculum/scoping/deliverables.md
- Tech setup:
- Software you need: curriculum/setup/software-setup/README.md
- Software setup session: curriculum/setup/software-setup/setup_session_guide.md
- Command line intro: curriculum/setup/command-line-tools/README.md
- Git and github:
- What is it?: curriculum/setup/git-and-github/README.md
- Basic tutorial: curriculum/setup/git-and-github/basic_git_tutorial/README.md
- Group tutorial: curriculum/setup/git-and-github/group_tutorial/README.md
- Advanced notes: curriculum/setup/git-and-github/branching_and_merging/README.md
- Git Workflow: curriculum/setup/git-and-github/githubflow/github-flow.html
- Git branching: curriculum/setup/git-and-github/branching_and_pull_requests.md
- Python:
- Basic Python: curriculum/software/basic_python.md
- Python, Pandas and Viz: curriculum/software/python_sql_pandas_viz.md
- Python and SQL: curriculum/software/python_sql.md
- SQL:
- SQL Basics: curriculum/software/basic_sql/README.md
- Postgres Tips and Pitfalls: curriculum/1_getting_and_keeping_data/databases/making_databases_work_for_you.md
- Good repos: curriculum/setup/good_repos.md
- Technical workflow: curriculum/setup/software-setup/TechnicalWorkflowAndBestPractices.md
- Getting, storing, and linking data:
- Intro: curriculum/1_getting_and_keeping_data/README.md
- Data security: curriculum/get_data/data-security-primer/README.md
- Get data:
- APIs and scrapping: curriculum/1_getting_and_keeping_data/basic-web-scraping/README.md
- Working with images: curriculum/get_data/images.md
- Working with text: curriculum/2_data_exploration_and_analysis/text-analysis/README.md
- Flat files: curriculum/1_getting_and_keeping_data/csv-to-db/README.md
- Store data:
- ETL - cleaning, loading: curriculum/1_getting_and_keeping_data/reproducible_ETL/README.md
- DBs:
- Why a DB?: curriculum/dbs/why.md
- Designing a DB: curriculum/dbs/relational_design.md
- Getting data in: curriculum/dbs/getting_data_in.md
- Getting data out: curriculum/dbs/getting_data_out.md
- Analyzing data (SQL): curriculum/software/sql_data_analysis.md
- Other types of DBs: curriculum/dbs/other_types.md
- Link data:
- Record linkage: curriculum/2_data_exploration_and_analysis/record-linkage/README.md
- Data Exploration:
- Introduction to EDA: curriculum/2_data_exploration_and_analysis/README.md
- Visualization: curriculum/eda/visualization.md
- SQL: curriculum/2_data_exploration_and_analysis/advanced_sql/README.md
- Python/Pandas: curriculum/2_data_exploration_and_analysis/data-exploration-in-python/README.md
- GIS: curriculum/2_data_exploration_and_analysis/gis_analysis/postgis-workshop/README.md
- Text: curriculum/2_data_exploration_and_analysis/text-analysis/README.md
- Network: curriculum/2_data_exploration_and_analysis/network-analysis/README.md
- Tableau: curriculum/eda/tableau.md
- Data stories concept and code: curriculum/eda/data_stories.md
- ML as a data exploration tool (Clustering): curriculum/eda/clustering.md
- Computational and Data Analysis Methods:
- Intro: curriculum/3_modeling_and_machine_learning/README.md
- Machine Learning: curriculum/3_modeling_and_machine_learning/machine-learning/README.md
- Causal inference methods: curriculum/methods/causal_inference.md
- Social science methods: curriculum/methods/social_science.md
- Other statistical analysis methods: curriculum/methods/statistical_analysis.md
- OR/optimization methods: curriculum/methods/or.md
- Problem Templates in Social Good and Public Policy: curriculum/ml/templates.md
- Practical Steps in Using Machine Learning to Solve Social Problems:
- Building an ML Pipeline:
- ML pipeline I: curriculum/ml/pipeline.md
- Set up problem:
- ML problem formulation: curriculum/ml/problem_formulation.md
- ML Checklist: curriculum/ml/checklist.md
- Templates of policy problems: curriculum/ml/templates.md
- Labels/Outcomes:
- One or many: curriculum/ml/labels/one_or_many.md
- Implications of a label: curriculum/ml/labels/implications.md
- Features/Predictors:
- Feature engineering: curriculum/ml/feature_engineering/intro.md
- Workshop on feature engineering: curriculum/ml/feature_engineering/workshop.md
- Case studies: curriculum/ml/feature_engineering/case_studies.md
- Validation Methodology:
- Process and goal: curriculum/ml/validation/process_and_goal.md
- K-fold cross-validation: curriculum/ml/validation/kfold.md
- Temporal cross-validation: curriculum/ml/validation/tcc.md
- Field trials: curriculum/ml/validation/field_trials.md
- Evalution Metrics:
- Overview: curriculum/ml/metrics/overview.md
- Examples: curriculum/ml/metrics/examples.md
- Models/Methods:
- Machine learning methods: curriculum/ml/methods.md
- Practical tips on how to use them, parameters, etc.: curriculum/ml/tips.md
- Model selection:
- Audition: curriculum/ml/selection/audition.md
- What metrics do we care about?:
- Performance: curriculum/ml/selection/performance.md
- Stability: curriculum/ml/selection/stability.md
- Interpretability: curriculum/ml/selection/interpretability.md
- Bias: curriculum/ml/selection/bias.md
- Postmodeling:
- Model understanding: curriculum/ml/postmodeling/understanding.md
- Feature importance: curriculum/ml/postmodeling/feature_importance.md
- Comparing different models: curriculum/ml/postmodeling/model_comparison.md
- Comparing lists: curriculum/ml/postmodeling/list_comparison.md
- Error analysis: curriculum/ml/postmodeling/error_analysis.md
- Bias analysis: curriculum/ml/postmodeling/bias_analysis.md
- Experimental design:
- Experiment design: curriculum/experimental_design/intro.md
- Case studies: curriculum/experimental_design/case_studies.md
- Deployment and Maintenance:
- How to deploy: curriculum/deployment/how_to.md
- Monitor: curriculum/deployment/monitor.md
- Update: curriculum/deployment/update.md
- Advanced pipelines: curriculum/deployment/advanced_pipelines.md