愛荷華大學

Learning Outcomes

Data Curation Skills

Graduates will be able to:

• understand issues associated with data collection, management, provenance, storage, merging, sharing, and preparation;
• work with multiple-source, multiple-format data;
• investigate the quality of the data; and
• have a basic understanding of ethical and confidentiality issues associated with data collection, storage, merging, and sharing.

Computational Skills

Graduates will be able to:

• use critical thinking skills to translate substantive questions into well-defined computational problems and choose appropriate computational techniques for a given problem;
• understand the foundational software skills and associated algorithmic and computational problem-solving methods used in computer science;
• be proficient in computational methods for collecting, managing, storing, preparing, sharing, and describing data numerically and graphically from a variety of sources to design and carry out basic simulation studies; and
• use professional statistical software and understand the principles of programming and algorithmic problem solving that underlie these packages.

Statistical/Probablilistic Skills

Graduates will be able to:

• use critical thinking skills to translate substantive questions into well-defined statistical or probability problems and choose the appropriate graphical or numerical descriptive and/or inferential statistical techniques for a given problem;
• understand the importance of, and issues related to, the choice of the study design, such as designed experiment versus probability sample versus convenience sample, used to produce data;
• understand that uncertainty, variability, and randomness play significant roles in data-driven decision making;
• understand how to measure and display uncertainty, the effect of randomness, confidence/credibility, and the likelihood of incorrect inferences;
• understand and be able to explain common misperceptions, paradoxes, and fallacies of probability and statistics; and
• understand basic regression, prediction, simulation, and visualization methods.

The Bachelor of Science with a major in data science requires a minimum of 120 s.h., including at least 59 s.h. of work for the major. Students must maintain a g.p.a. of at least 2.00 in all courses for the major and in all UI courses for the major. They also must complete the College of Liberal Arts and Sciences GE CLAS Core.

Data science majors may not earn a major or minor in computer science or statistics, a major in computer science and engineering, or the Certificate in Social Science Analytics.

Today, nearly every business, government, social media platform and educational institution collects and analyzes data about its users, logistics and operations, and media presence in the hope of extracting valuable insights and utilizing the resulting efficiencies.

As an example, Amazon is the company most closely identified with a data-driven business model. Starting just over 20 years ago as an online book seller with a relatively crude crowdsourced book review platform and simple recommender system technology, it was subsequently augmented with extensive tracking of customer page views, advertising hits, data about prior purchases, and an aggressive emphasis on data-driven operational efficiencies. Amazon has become the major player in U.S. retail and a prime example of the strategic value of big data.

Data science graduates may pursue careers as data scientists. This position allows them to apply their understanding of statistics, as well as algorithm and software design, to create and develop the next generation of data analysis tools.