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| # Class 5: Towards the practice of data science | ||
| - Theme: Program like a software engineer; think like a data scientist | ||
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| ## WEEK ONE @nickeubank | ||
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| - Theme: Big data | ||
| - Learning objectives | ||
| - Understand what makes big data big and how to work with it | ||
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| ### Big Data | ||
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| - What makes big data big (e.g. memory hierarchy) | ||
| - Big data strategies (e.g. chunking) | ||
| - Tools for big data: dask | ||
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| ## WEEK TWO @kylebradbury | ||
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| - Theme: Plotting and Exploratory Data Analysis | ||
| - Learning objectives: | ||
| - Never trust your data too much | ||
| - Learn tools for data summarization | ||
| - Understand the plotting ecosystem in Python | ||
| - Understand the graphics of grammar | ||
| - Be able to do basic plots in altair | ||
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| ### Plotting Principles | ||
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| - Altair and its alternatives (matplotlib, plotly, etc.) | ||
| - Why Altair? | ||
| - Grammer of Graphics | ||
| - Hierarchy of encodings | ||
| - Common plot forms: scatter, line, bar, histogram, and heatmaps | ||
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| ### Effective plotting | ||
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| - Exploration and Exposition | ||
| - Communication and summarization | ||
| - Examples from real data sources | ||
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| ### Interactive plotting | ||
| - Enables rapid exploration of data and is web-friendly | ||
| - Altair can create interactive plots implicitly | ||
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| ## WEEK THREE | ||
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| - Theme: Data Modeling | ||
| - Learning objectives: | ||
| - Examples of common data science applications and the programming strategies used | ||
| - Understand common packages to be conversant in the lingua franca | ||
| - Work through examples of each as a "hello world" of applied programming for data science | ||
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| ### What is Data Modelling? @kylebradbury | ||
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| - Overview! "Dimensionality reduction" | ||
| - representing data with high-fidelity | ||
| - Datasaurus / Anscombes Quartet | ||
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| ### Linear Regression @nickeubank | ||
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| - What Is Linear Regression? | ||
| - What Are Standard Errors? | ||
| - Modelling with `statsmodels` | ||
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| ### Machine Learning @kylebradbury | ||
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| - **What is it?** | ||
| - Unsupervised Machine Learning | ||
| - Supervised Machine Learning | ||
| - **scikit-learn** | ||
| - split, fit, predict | ||
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| ## WEEK FOUR [assignment TBD] | ||
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| - Theme: Getting Help in Learning More; Other Things | ||
| - Learning objective: | ||
| - Data Science is changing quickly, so you will have to keep learning constantly | ||
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| ### Workflows | ||
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| - Data analysis v. software engineering | ||
| - don't edit your source data / data pipelines | ||
| - Collaboration on github demonstration? | ||
| - CI/CD | ||
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| ### Forums | ||
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| - How to post on stackoverflow | ||
| - MWE, etiquette, problems of gender | ||
| - Posting on github | ||
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| ### Accessibility Tech | ||
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| - Data science resources for accessibility | ||
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| ## WEEK FIVE [assignment / content TBD] | ||
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| - Theme: Bringing it all together | ||
| - Learning objective: | ||
| - Complete a worked example that brings together content from all 5 of the specialization courses | ||
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| ### Comprehensive example | ||
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| - This example should include all of key practices from through out the 5 courses | ||
| - Conclude week 5 with a summary of what was learned | ||
| - Learning objectives: | ||
| - Make reproducible, customizable plots that use programming to reveal data insights | ||
| - Use programming skills to create software for common data science applications | ||
| - Conduct a full-scale data science analysis that makes use of the programming tools developed through the course and specialization | ||
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| ## WEEK 1: Visualizing Data with Code: Plotting with Matplotlib (Kyle) | ||
| - Why do we plot? Visual exploration, communication, and summarization | ||
| - Introduction to plotting with code: Matplotlib basics | ||
| - Procedural versus object oriented approaches to plotting | ||
| - Basic Plotting: coding a plot in 4 lines of code or less | ||
| - Using code to inspect your data: Datasaurus / Anscombes Quartet | ||
| - Common plot forms: scatter, line, bar, and heatmaps | ||
| - Histograms for data summarization | ||
| - Making highly customized and fully reproducible plots with code | ||
| - Effective plotting practices | ||
| - Other plotting tools in Python | ||
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| ## WEEK 2: Using Code to Make Predictions from Data: Classification and Regression (Kyle) | ||
| - Introduction to prediction (supervised learning): classification and regression | ||
| - Preparing your data: training and test data | ||
| - K Nearest Neighbors (KNN) classification | ||
| - Coding a KNN classifier | ||
| - Applying your KNN classifier to real world data | ||
| - How to convert your KNN classifier into a regression tool | ||
| - Common programming tools for prediction | ||
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| ## WEEK 3: Using Code to Draw Inferences from Data: Regression (Nick) | ||
| - From prediction to inference: understanding relationships between inputs and outputs | ||
| - Introducing linear regression | ||
| - Understanding standard errors | ||
| - Interpreting regression results | ||
| - Real-world example of applying regression for inference | ||
| - The importance of modeling assumptions | ||
| - Common programming tools for inference | ||
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| ## WEEK 4: Bringing it All Together: Final Coding Project (Kyle and Nick) | ||
| - Intro to the GapMinder project | ||
| - Motivation: background on the problem (and how to formulate data science question) | ||
| - Gathering and merging the data | ||
| - Drafting a plot of the data | ||
| - Refining a plot of the data | ||
| - Training a regression model on the data | ||
| - Determining the relationships between predictors | ||
| - Interpreting and sharing your findings |