Introduction

Jake Ryland Williams
Assistant Professor
Department of Information Science
College of Computing and Informatics
Drexel University
Introduction to data science

Course intent

  • This is a first course in data science.
  • It will provide an overview of most aspects of the discipline.
  • This is a self-contained survey course with no prerequisites,
  • with content organized through readings and class discussions.

    • Who should take this course?

  • Anyone. Data science is becoming pervasive.
  • Any discipline where data is prevalent can benefit,
  • and data is becoming prevalent in more and more places.
  • As mentioned, there are no prerequisites.
  • It is required for DS, IS, and CST majors, and DS minors.

    • What will you get out of this course?

  • An overview of the skills that go into data science,
  • and an understanding of the tasks undertaken in data science.
    • Among other topics, this includes:
      • what data is and where to find it,
      • the differing work that gets done across the discipline,
      • typical components of a data science project, and
      • characterizations of "Big data."

    If you want to see more of anything...
    ...take the major!

    • Programming and development
      • Web Systems and Services I: INFO 151
      • Computer Programming I: CS 171
      • Data Science Programming I & II: INFO 212 & 213
      • Cloud Computing and Big Data: INFO 323


    • Analysis and exploration
      • Social Media Data Analysis: INFO 440
      • Exploratory Data Analytics: INFO 311
      • Advanced Data Analytics: INFO 411
      • Data Mining Applications: INFO 371

    If you want to see more of anything...
    ...take the major!

    • Curation, management, and access
      • Data Curation: INFO 202
      • Applied Data Management: INFO 153
      • Database Management Systems: INFO 210
      • Information Retrieval Systems: INFO 300
      • Introduction to Information Security: INFO 333

    If you want to see more of anything...
    ...take the major!

    • Design and visualization
      • Information Visualization: INFO 250
      • Human-Computer Interaction II: INFO 310
      • Visual Analytics: INFO 350


    • Social aspects and collaboration
      • Social Aspects of Information Systems: INFO 215
      • Issues in Information Policy: INFO 216
      • Software project management: INFO 420

    Course structure

    • Overview:
      • Eight quizzes (20%)
      • Two homework assignments (30%)
      • One mid-term exam (20%)
      • One group project description (15%)
      • One group project presentation (15%)

    Quizzes

    • For full credit:
      • Show up every day.
      • Complete all required readings.

    Homework

    • For full credit:
      • Complete your assignments in a timely fashion.
      • Make sure your responses are cogent and concise.
      • Think creatively about your responses.
      • Relate your experiences to the questions asked.
      • Complete all required readings.
      • Participate in class discussions.
      • Ask questions and affirm your understanding.

    Mid-term exam

    • For full credit:
      • Show up for/take your exams!
      • Make sure your responses are cogent and concise.
      • Complete all required readings.
      • Participate in class discussions.
      • Ask questions and affirm your understanding.

    Project description

    • For full credit:
      • Start thinking about a topic early.
      • Communicate and coordinate work with your group.
      • Get specific about one product/dataset/technology.
      • Consider both limitiations and advancements.
      • Make sure your information is clearly presented!

    Project presentation

    • For full credit:
      • Integrate feedback to update description material.
      • Use images, graphs, and charts.
      • Don't just read from slides!
      • Pause for understanding and ask questions.

    Recap

  • Data science reaches into a lot of fields.
  • This survey course is intended for everyone.

    • Up next, an overview:
      • What is data science?
      • Who is a data scientist?
      • What is data?