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.

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%

授業計画

  1. 第回
    Introduction to Data Science Feb 5

    Overview of the data science workflow and key concepts.

  2. 第回
    Data Collection and APIs Feb 12

    Methods for collecting data through APIs, web scraping, and databases.

  3. 第回
    Data Cleaning and Preprocessing Feb 19

    Techniques for handling missing values, outliers, and data transformation.

  4. 第回
    Exploratory Data Analysis Feb 26

    Descriptive statistics, visualization, and pattern discovery.

  5. 第回
    Statistical Analysis Mar 4

    Hypothesis testing, confidence intervals, and statistical inference.

  6. 第回
    Data Visualization Mar 11

    Principles and tools for effective data visualization.