MLA 009 Charting and Visualization Tools for Data Science
Machine Learning Guide
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D Three
D three is intended for writing the front end code of an ap that is going to be used by an end user. Some sophoware packages will let you actually generate a machine learning model without having right code. The word power b i, microsots, powerb i, sophor package, implies that these programs are more than just looking at data and generating grafts. Power b i as in business intelligence.
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Python charting libraries - Matplotlib, Seaborn, and Bokeh - explaining, their strengths from quick EDA to interactive, HTML-exported visualizations, and clarifies where D3.js fits as a JavaScript alternative for end-user applications. It also evaluates major software solutions like Tableau, Power BI, QlikView, and Excel, detailing how modern BI tools now integrate drag-and-drop analytics with embedded machine learning, potentially allowing business users to automate entire workflows without coding.
Links- Notes and resources at ocdevel.com/mlg/mla-9
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- Exploratory Data Analysis (EDA):
- EDA occupies an early stage in the Business Intelligence (BI) pipeline, positioned just before or sometimes merged with the data cleaning (“munging”) phase.
- The outputs of EDA (e.g., correlation matrices, histograms) often serve as inputs to subsequent machine learning steps.
- The foundational plotting library in Python, supporting static, basic chart types.
- Requires substantial boilerplate code for custom visualizations.
- Serves as the core engine for many higher-level visualization tools.
- Common EDA tasks (like plotting via .corr(), .hist(), and .scatter() methods on pandas DataFrames) depend on Matplotlib under the hood.
- Pandas integrates tightly with Matplotlib and exposes simple, one-line commands for common plots (e.g., df.corr(), df.hist()).
- Designed to make quick EDA accessible without requiring detailed knowledge of Matplotlib’s verbose syntax.
- A high-level wrapper around Matplotlib, analogous to how Keras wraps TensorFlow.
- Sets sensible defaults for chart styles, fonts, colors, and sizes, improving aesthetics with minimal effort.
- Importing Seaborn can globally enhance the appearance of all Matplotlib plots, even without direct usage of Seaborn’s plotting functions.
- A powerful library for creating interactive, web-ready plots from Python.
- Enables user interactions such as hovering, zooming, and panning within rendered plots.
- Exports visualizations as standalone HTML files or can operate as a server-linked app for live data exploration.
- Supports advanced features like cross-filtering, allowing dynamic slicing and dicing of data across multiple axes or columns.
- More suited for creating reusable, interactive dashboards rather than quick, one-off EDA visuals.
- Unlike previous libraries, D3.js is a JavaScript framework for creating complex, highly customized data visualizations for web and mobile apps.
- Used predominantly on the client-side to build interactive front-end graphics for end users, not as an EDA tool for analysts.
- Common in production-grade web apps, but not typically part of a Python-based data science workflow.
- Leading commercial drag-and-drop BI tool for data visualization and dashboarding.
- Connects to diverse data sources (CSV, Excel, databases), auto-detects column types, and suggests default chart types.
- Users can interactively build visualizations, cross-filter data, and switch chart types without coding.
- Microsoft’s BI suite, similar to Tableau, supporting end-to-end data analysis and visualization.
- Integrates data preparation, visualization, and increasingly, built-in machine learning workflows.
- Focused on empowering business users or analysts to run the BI pipeline without programming.
- Another major BI offering is QlikView, emphasizing interactive dashboards and data exploration.
- Still widely used for basic EDA and visualizations directly on spreadsheets.
- Offers limited but accessible charting tools for histograms, scatter plots, and simple summary statistics.
- Data often originates from Excel/CSV files before being ingested for further analysis in Python/pandas.
- Workflow Integration: Modern BI tools are converging, adding both classic EDA capabilities and basic machine learning modeling, often through a code-free interface.
- Automation Risks and Opportunities: As drag-and-drop BI tools increase in capabilities (including model training and selection), some data science coding work traditionally required for BI pipelines may become accessible to non-programmers.
- Distinctions in Use:
- Python libraries (Matplotlib, Seaborn, Bokeh) excel in automating and scripting EDA, report generation, and static analysis as part of data pipelines.
- BI software (Tableau, Power BI, QlikView) shines for interactive exploration and democratized analytics, integrated from ingestion to reporting.
- D3.js stands out for tailored, production-level, end-user app visualizations, rarely leveraged by data scientists for EDA.
Key Takeaways
- For quick, code-based EDA: Use Pandas’ built-in plotters (wrapping Matplotlib).
- For pre-styled, pretty plots: Use Seaborn (with or without direct API calls).
- For interactive, shareable dashboards: Use Bokeh for Python or BI tools for no-code operation.
- For enterprise, end-user-facing dashboards: Choose BI software like Tableau or build custom apps using D3.js for total control.
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