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pages/_teachings/data-science-fundamentals.md
Jiahao Zhang 0fe3c84636
Add course schedule feature to teaching page (#2258) (#3147) 
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>
2026-01-17 18:43:47 -03:00

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layout title description instructor year term location time course_id schedule
course Data Science Fundamentals 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. Prof. Data 2024 Spring Science Building, Room 202 Mondays and Wednesdays, 2:00-3:30 PM data-science-fundamentals
week date topic description materials
1 Feb 5 Introduction to Data Science Overview of the data science workflow and key concepts.
name url
Syllabus /assets/pdf/example_pdf.pdf
name url
Slides /assets/pdf/example_pdf.pdf
week date topic description materials
2 Feb 12 Data Collection and APIs Methods for collecting data through APIs, web scraping, and databases.
name url
Lecture Notes /assets/pdf/example_pdf.pdf
name url
Assignment 1 /assets/pdf/example_pdf.pdf
week date topic description materials
3 Feb 19 Data Cleaning and Preprocessing Techniques for handling missing values, outliers, and data transformation.
name url
Lecture Notes /assets/pdf/example_pdf.pdf
name url
Coding Lab https://github.com/
week date topic description materials
4 Feb 26 Exploratory Data Analysis Descriptive statistics, visualization, and pattern discovery.
name url
Lecture Notes /assets/pdf/example_pdf.pdf
name url
Assignment 2 /assets/pdf/example_pdf.pdf
week date topic description materials
5 Mar 4 Statistical Analysis Hypothesis testing, confidence intervals, and statistical inference.
name url
Lecture Notes /assets/pdf/example_pdf.pdf
name url
Review Materials /assets/pdf/example_pdf.pdf
week date topic description materials
6 Mar 11 Data Visualization Principles and tools for effective data visualization.
name url
Lecture Notes /assets/pdf/example_pdf.pdf
name url
Assignment 3 /assets/pdf/example_pdf.pdf

Course Overview

This course provides a comprehensive introduction to data science principles and practices. Students will:

  • Learn the end-to-end data science workflow
  • Gain practical experience with data manipulation tools
  • Develop skills in data visualization and communication
  • Apply statistical methods to derive insights from data

Prerequisites

  • Basic programming knowledge (preferably in Python)
  • Introductory statistics
  • Comfort with basic algebra

Textbooks

  • "Python for Data Analysis" by Wes McKinney
  • "Data Science from Scratch" by Joel Grus

Grading

  • Assignments: 50%
  • Project: 40%
  • Participation: 10%