---
layout: course
title: Data Science Fundamentals
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.
instructor: Prof. Data
year: 2024
term: Spring
location: Science Building, Room 202
time: Mondays and Wednesdays, 2:00-3:30 PM
course_id: data-science-fundamentals
schedule:
  - week: 1
    date: Feb 5
    topic: Introduction to Data Science
    description: Overview of the data science workflow and key concepts.
    materials:
      - name: Syllabus
        url: /assets/pdf/example_pdf.pdf
      - name: Slides
        url: /assets/pdf/example_pdf.pdf

  - week: 2
    date: Feb 12
    topic: Data Collection and APIs
    description: Methods for collecting data through APIs, web scraping, and databases.
    materials:
      - name: Lecture Notes
        url: /assets/pdf/example_pdf.pdf
      - name: Assignment 1
        url: /assets/pdf/example_pdf.pdf

  - week: 3
    date: Feb 19
    topic: Data Cleaning and Preprocessing
    description: Techniques for handling missing values, outliers, and data transformation.
    materials:
      - name: Lecture Notes
        url: /assets/pdf/example_pdf.pdf
      - name: Coding Lab
        url: https://github.com/

  - week: 4
    date: Feb 26
    topic: Exploratory Data Analysis
    description: Descriptive statistics, visualization, and pattern discovery.
    materials:
      - name: Lecture Notes
        url: /assets/pdf/example_pdf.pdf
      - name: Assignment 2
        url: /assets/pdf/example_pdf.pdf

  - week: 5
    date: Mar 4
    topic: Statistical Analysis
    description: Hypothesis testing, confidence intervals, and statistical inference.
    materials:
      - name: Lecture Notes
        url: /assets/pdf/example_pdf.pdf
      - name: Review Materials
        url: /assets/pdf/example_pdf.pdf

  - week: 6
    date: Mar 11
    topic: Data Visualization
    description: Principles and tools for effective data visualization.
    materials:
      - name: Lecture Notes
        url: /assets/pdf/example_pdf.pdf
      - name: Assignment 3
        url: /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%