MTH 225 - Introduction to Data Science

Credits: 3
Instructional Contact Hours: 3

Introduces the main tools and ideas in the data scientist's toolbox. Provides an overview of the data, questions, techniques, and tools that data analysts and data scientists use. Provides a conceptual introduction to the ideas behind turning data into actionable knowledge and tools that will be used to analyze this data. Examines collecting, cleaning, and sharing data. Demonstrates how to communicate results through visualizations.

Prerequisite(s): MTH 208W or MTH 208AW or MTH 209W or MTH 209AW may be taken previously or concurrently.
Corequisite(s): None

Lecture Hours: 45 Lab Hours: 0
Meets MTA Requirement: None
Pass/NoCredit: Yes

Outcomes and Objectives

Demonstrate relevant programming abilities.
1. Code simple algorithms in a high-level programming language.
2. Formulate simple algorithms to solve problems and code them in a high-level language appropriate for data science work (e.g., Python, SQL, R, Java).
3. Create algorithms of moderate complexity and implement them in a data science programming language appropriate for data science work.
Demonstrate proficiency with statistical analysis of data.
1. Perform standard data visualization and formal inference procedures and interpret the results.
2. Choose appropriately from a wider range of descriptive and inferential methods for analyzing data and interpret the results contextually.
3. Construct statistical models, assess the fit of such models to the data, and apply the models in real-world contexts.
Demonstrate the ability to build and assess data-based models.
1. Demonstrate an understanding of what a model is and how to use a given model.
2. Demonstrate use of more complex models and begin to construct models of their own.
3. Recognize that different models fit and perform better than others and measure fit and performance appropriately.
Execute statistical analyses with professional statistical software.
1. Generate simple statistical summaries using on-line tools or software not designed for statistical analyses (e.g., Excel).
2. Create a wide range of visual and numerical data summaries and perform basic inferential procedures (confidence intervals and significance tests) using menu-driven statistical software.
3. Apply complex models using dedicated statistical software (e.g., R, Minitab, SAS).
Demonstrate skill in data management
1. Organize data after the data have been collected and cleaned and use data in the form in which the data are given.
2. Perform basic data cleaning and transform variables to facilitate analysis.
3. Acquire and clean data and move information in and out of relational databases.
4. Demonstrate skills in acquisition of data, combining data from multiple sources, and data wrangling.
Describe the fundamental elements of relational database management systems.
1. Explain the basic concepts of relational data model, entity-relationship (ER) model, relational database design, relational algebra, and SQL.
2. Design ER models to represent simple database application scenarios.
3. Convert the ER model to relational tables, populate relational database and formulate SQL queries on data.
4. Improve the database design by normalization.
5. Become familiar with basic database storage structures and access techniques: file and page organizations, indexing methods (e.g., B tree, hashing).
Apply data science concepts or methods to solve problems in real-world contexts.
1. Choose appropriate data management strategies, perform relevant analyses, interpret and apply the results to inform understanding, and solve specific problems in context.
Communicate solutions to problem effectively.
1. Communicate to a technical audience.