DASC 2113 - Principles and Techniques in Data Science

Description
(F) Principles and Techniques in Data Science (PTDS) (DASC 2113) is an intermediate semesterlong
data science course that follows an overview of data science in today’s world. This class bridges
between introduction to data science and upper division data science courses as well as methods
courses in other concentrations. This class equips students with essential basic elements of data
science, ranging from database systems, data acquisition, storage and query, data cleansing, data
wrangling, basic data summarization and visualization, and data estimation and modeling. Students
will gain hands-on experience using Python and various packages in Python.

Pre-Requisite
DASC 1222 - The Role of Data Science in Today’s World

3 Credit Hour(s)

Contact Hours
45 lecture/lab

3 Faculty Load Hour(s)

Semesters Offered
Fall

ACTS Equivalent
None.

Grade Mode
A-F

Learning Outcomes
Students completing DASC 2113 should be able to:
• describe the necessary foundation and context to prepare for more advanced data science
topics;
• describe relational database management systems and their use in data acquisition, data
storage and data query.
• query, combine and cleanse the data to identify potential issues and resolve inconsistencies,
errors and/or issues in the data;
• summarize, visualize, and transform the data to understand it more deeply as well discover
data patterns that may inform further analyses;
• employ various mathematical and statistical tools for modeling and estimation of the data;
• use principles and techniques in data science to communicate conclusions and patterns in
the data to diverse audiences.

General Education Outcomes Supported
• Students will demonstrate technological fluency.
• Students demonstrate information literacy.

Standard Practices
Topics list

1. Foundations of Data
2. Data tables, indexes, and Pandas
3. Data design, data collection, data query and data transformation
4. Exploratory data analysis and data cleansing
5. Informative data visualization and data wrangling
6. Statistical concepts and inference
7. Working with text and web technologies
8. Introduction to data centric computing and scalable data processing

Learning activities
Assignments and Projects.
This course requires some in class, hands-on work and also additional hands-on work in the
virtual or on-campus computer lab.

Assessments
Homework
Projects
Quizzes
Exams

Grading guidelines
A = 90 - 100%
B = 80 - 89%
C = 70 - 79%
D = 60 - 69%
F = 0 - 59%