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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.

Instructor: Prof. Data

Term: Spring

Location: Science Building, Room 202

Time: Mondays and Wednesdays, 2:00–3:30 PM

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%

Schedule

Week Date Topic Materials
1 Feb 5 Introduction to Data Science

Overview of the data science workflow and key concepts.
2 Feb 12 Data Collection and APIs

Methods for collecting data through APIs, web scraping, and databases.
3 Feb 19 Data Cleaning and Preprocessing

Techniques for handling missing values, outliers, and data transformation.
4 Feb 26 Exploratory Data Analysis

Descriptive statistics, visualization, and pattern discovery.
5 Mar 4 Statistical Analysis

Hypothesis testing, confidence intervals, and statistical inference.
6 Mar 11 Data Visualization

Principles and tools for effective data visualization.