# Area Learning Outcomes

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

- Foundation in Statistical Methods
- Foundation in Computation
- Foundation Data Literacy, Management, and Analytics
- Foundation in Data Ethics and Digital Citizenship
- Application Focus

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

## Foundation in Statistical Methods

### Applicable Program Outcomes:

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

### Representative Learning Outcomes:

- 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.

## Foundation in Computation

### Applicable Program Outcomes:

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

### Representative Learning Outcomes:

- 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.

## Foundation Data Literacy, Management, and Analytics

### Applicable Program Outcomes:

- 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

### Representative Learning Outcomes:

- 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.

## Foundation in Data Ethics and Digital Citizenship

### Applicable Program Outcomes:

- 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.

### Representative Learning Outcomes:

- 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.

## Application Focus

### Applicable Program Outcomes:

- 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.

### Representative Learning Outcomes:

- 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