Area Learning Outcomes

The Applications in Data Science Certificate’s curricular requirements consist of the following areas:

The following provides for each of these areas, the corresponding program outcome(s) and a listing of representative learning outcomes:

Foundation in Statistical Methods

Develop a foundation in statistical, mathematical, and algorithmic techniques or tools for the analysis of large-scale datasets.

Explain basic terminology, set theory, and counting rules related to probability.
Identify various discrete and continuous probability models/distributions and apply them to answer probability questions.
Apply basic terms, graphs, and symbols.
Calculate and use confidence intervals and significance test for means and proportions.
Design and conduct statistical experiment.
Perform simple and multiple regression procedures.
Employ correlation in multivariate analysis.

Develop a foundation in statistical, mathematical, and algorithmic techniques or tools for the analysis of large-scale datasets.

Demonstrate the ability to analyze a problem, design an algorithm to address the problem, and code the solution.
Evaluate time complexity and space complexity of algorithms.
Evaluate algorithmic options to implement solutions that make efficient use of computational resources.
Create computational tools to quantify and evaluate numerical models.
Apply appropriate sorting and searching algorithms.
Apply graph theoretic techniques, data structures, and algorithms for problem solving.
Examine information technology architectures inclusive of operating systems, networking, distributed systems architectures, and distributed application architectures.

Describe the stages of the data life cycle (data acquisition, organization, curation, analysis, preservation, and communication) and create an effective data management and data analysis plan.
Apply statistical, mathematical, and algorithmic techniques or tools to extract knowledge and insights from large-scale datasets
Interpret results from large-scale data analysis and communicate findings

Use selected tools to scrape, clean, process, and visualize data.
Apply data management techniques to prepare, parse, manipulate, and store data.
Use statistical methods to summarize data and identify relationships.
Formulate effective strategies for curation and preservation.
Explain new hypotheses, draw accurate conclusions, and make predictions based on data.
Effectively communicate the outcome of data analysis.

Identify ethical, legal, social issues and policy implications for, and responsibilities of, different stakeholders in data-science-driven decision making.
Develop skills related to ethical data reasoning and decision-making that refer to ethical principles such as such as trust, fairness, beneficence, autonomy, or non-maleficence.

Identify ethical, legal, social issues and policy implications associated with applications of data science in a variety of professional settings.
Demonstrate the ability to use ethical principles in context, such as trust, fairness, beneficence, autonomy, or non-maleficence
Apply general ethical principles and frameworks to the specific, concrete actions of individuals, corporations, governments, and other organizations.
Construct sound, well-reasoned arguments, and communicate them clearly to interdisciplinary scholarly and public audiences.
Develop and apply ethical, legal, social frameworks to case studies and/or scenarios directly related to data science.
Assess the impact of data science solutions in different cultural environments.
Develop awareness of how data science is implicated in the distribution of economic, political, and social rights and opportunities.

Interrogate who is included and how in the design, implementation, and/or evaluation of data science.

Apply statistical, mathematical, and algorithmic techniques or tools to extract knowledge and insights from large-scale datasets.
Interpret results from large-scale data analysis and communicate findings.

Apply data science techniques and tools on large-scale datasets in a specific domain.
Interpret results in the context of domain-specific knowledge.
Communicate findings in domain-specific language