0fe3c84636
Implements the course schedule feature requested in issue #[2258](https://github.com/alshedivat/al-folio/issues/2258). This PR adds a new course schedule feature to the al-folio theme, allowing academics to easily create and display structured course information. **Changes:** - Added a `courses` collection to organize and display academic courses - Created course layout and display templates with responsive design - Implemented organization by year and term with automatic sorting - Added support for weekly schedule with topics and course materials - Simplified documentation with a README for course creation This feature makes it easier for academics to showcase their teaching materials with a consistent, organized display of course schedules, helping users create professional teaching pages without custom implementation. --------- Signed-off-by: George Araújo <george.gcac@gmail.com> Co-authored-by: George Araújo <george.gcac@gmail.com>
98 lines
2.8 KiB
Markdown
98 lines
2.8 KiB
Markdown
---
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layout: course
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title: Data Science Fundamentals
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description: This course covers the foundational aspects of data science, including data collection, cleaning, analysis, and visualization. Students will learn practical skills for working with real-world datasets.
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instructor: Prof. Data
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year: 2024
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term: Spring
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location: Science Building, Room 202
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time: Mondays and Wednesdays, 2:00-3:30 PM
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course_id: data-science-fundamentals
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schedule:
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- week: 1
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date: Feb 5
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topic: Introduction to Data Science
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description: Overview of the data science workflow and key concepts.
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materials:
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- name: Syllabus
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url: /assets/pdf/example_pdf.pdf
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- name: Slides
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url: /assets/pdf/example_pdf.pdf
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- week: 2
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date: Feb 12
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topic: Data Collection and APIs
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description: Methods for collecting data through APIs, web scraping, and databases.
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materials:
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- name: Lecture Notes
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url: /assets/pdf/example_pdf.pdf
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- name: Assignment 1
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url: /assets/pdf/example_pdf.pdf
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- week: 3
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date: Feb 19
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topic: Data Cleaning and Preprocessing
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description: Techniques for handling missing values, outliers, and data transformation.
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materials:
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- name: Lecture Notes
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url: /assets/pdf/example_pdf.pdf
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- name: Coding Lab
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url: https://github.com/
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- week: 4
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date: Feb 26
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topic: Exploratory Data Analysis
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description: Descriptive statistics, visualization, and pattern discovery.
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materials:
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- name: Lecture Notes
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url: /assets/pdf/example_pdf.pdf
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- name: Assignment 2
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url: /assets/pdf/example_pdf.pdf
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- week: 5
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date: Mar 4
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topic: Statistical Analysis
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description: Hypothesis testing, confidence intervals, and statistical inference.
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materials:
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- name: Lecture Notes
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url: /assets/pdf/example_pdf.pdf
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- name: Review Materials
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url: /assets/pdf/example_pdf.pdf
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- week: 6
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date: Mar 11
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topic: Data Visualization
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description: Principles and tools for effective data visualization.
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materials:
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- name: Lecture Notes
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url: /assets/pdf/example_pdf.pdf
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- name: Assignment 3
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url: /assets/pdf/example_pdf.pdf
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---
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## Course Overview
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This course provides a comprehensive introduction to data science principles and practices. Students will:
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- Learn the end-to-end data science workflow
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- Gain practical experience with data manipulation tools
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- Develop skills in data visualization and communication
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- Apply statistical methods to derive insights from data
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## Prerequisites
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- Basic programming knowledge (preferably in Python)
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- Introductory statistics
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- Comfort with basic algebra
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## Textbooks
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- "Python for Data Analysis" by Wes McKinney
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- "Data Science from Scratch" by Joel Grus
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## Grading
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- Assignments: 50%
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- Project: 40%
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- Participation: 10%
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