IST 387
Introduction to Applied Data Science
(3 credits)
Class Size: 10-25
Faculty: Preeti Jagadev, Assistant Teaching Professor, Syracuse University
Administrative Contact: Tavish Van Skoik, Assistant Director, Project Advance
Course Catalog Description
The course introduces students to fundamentals about data and the standards, technologies, and methods for organizing, managing, curating, preserving, and using data. It discusses broader issues relating to data management, ethics, quality control and publication of data.
Applied examples of data collection, processing, transformation, management, and analysis as well as a hands-on introduction to the emerging field of data science are provided.
Students will explore key concepts related to data science, including applied statistics, information visualization, text mining and machine learning. R, the open-source statistical analysis and visualization system, will be used throughout the course. R is reckoned by many to be the most popular choice among data analysts worldwide; having knowledge and skill with using it is considered a valuable and marketable job skill for most data scientists.
Course Overview
The course will consist of one larger class plus one smaller lab section each week. During the larger class lectures, we will explore key concepts and examine the use of those concepts within R. Prior to attending lecture, it is expected that you’ve completed the reading. The lecture and lab will be easier, faster, more fun, and more useful if you get as much as you can out of the lecture. Preparation and participation are key.
The weekly lab sessions will review key concepts, let you practice R coding and will also review any student questions that have arisen during the week. During the lab and the homework, you will have a chance to practice and apply your knowledge.
There will also be an exam that will include questions about the lecture material as well as R-coding questions.
Note that there will also be a final project (group) which you will complete during the semester. This project will allow you to apply what you have learnt within the class to a real-world data problem, where your task is to understand the domain and the data available to determine how to best provide insight and wisdom from all the data that might be available.
Pre- / Co-requisites
N/A
Course Objectives
After taking this course, the students will be expected to understand:
- Essential concepts and characteristics of data
- The purpose of scripting for data management using R and R Studio
- Principles and practices in data screening, cleaning, linking, and visualizations
- The importance of clear communication of results to decision-makers
- The key ethical challenges associated with applications of data science in a variety of contexts
After taking this course, the students will be able to:
- Identify a problem and the data needed for addressing the problem
- Perform basic computational scripting using R and other optional tools
- Transform data through processing, linking, aggregation, summarization, and searching
- Organize and manage data at various stages of a project lifecycle
- Determine appropriate techniques for analyzing data
Laboratory
N/A
Required Materials
- You must own a functioning laptop with R and RStudio installed or create an account on posit.cloud (formerly rstudio.cloud).
- You must bring your laptop to every class meeting, both lecture and lab.
There is no required textbook for this course – all necessary information will be provided through lecture content and supplemental notes and readings posted on Blackboard as electronic documents for downloading and printing.
Texts / Supplies – Optional
Data Science for Business
With R (2021) by Jeffrey S. Saltz and Jeffrey M. Stanton (ISBN: 9781544370453). The book is available in paperback and electronic versions on
Amazon: https://www.amazon.com/Data–Science–Business–Jeffrey–
Instructor Recommendations
N/A