C797 Data Science and Analytics introduces Western Governors University's nursing informatics students to the interdisciplinary field of data science in healthcare.
WGU's MSN Nursing Informatics (BSN to MSN) program guide lists it at two competency units in term three. The guide describes combining statistics, computer science, data visualization and the social sciences to solve problems with data, covering data analysis, database management, descriptive and inferential statistics, statistical inference and process improvement.
Nurses often worry about the statistics. In practice the course rewards choosing sensible measures, presenting them clearly and explaining what they mean for care.
Course at a Glance
| Item | Details |
|---|---|
| University | Western Governors University (WGU) |
| Course code | C797 |
| Level | Graduate, competency-based |
| Program | MSN Nursing Informatics (BSN to MSN) |
| Competency units | 2 (term 3 of the standard path) |
| Typical work | Data analysis, interpretation, dashboards, trend-based planning |
What C797 Covers
The program guide lists three competencies:
| Competency (summarized) | In practice |
|---|---|
| Apply data, statistics, databases, modeling and data management to nursing informatics | Choosing the right measures and data structures |
| Analyze data from databases, EHRs, dashboards, remote monitoring and telemedicine | Using varied clinical data sources to support high-quality, efficient care |
| Predict clinical events from current or historical trend data | Forecasting needs to plan and resource care |
Key Concepts Explained
Descriptive and Inferential Statistics
Descriptive statistics summarize data you have: means, medians, rates and spread. Inferential statistics use a sample to draw conclusions about a wider population, with tests and confidence intervals that express uncertainty.
Rates, Not Raw Counts
Comparing units by raw counts misleads when their sizes differ. Healthcare quality data usually use rates per patient days or per admissions.
Example: Unit A had 12 falls in 8,000 patient days; Unit B had 9 falls in 4,500 patient days. Falls per 1,000 patient days are 12 / 8,000 x 1,000 = 1.5 for Unit A and 9 / 4,500 x 1,000 = 2.0 for Unit B. Unit B, with fewer falls, actually has the higher rate.
Trend-Based Prediction
The third competency asks you to predict clinical events or likelihoods from data. At this level that often means reading trends, seasonality and risk scores, for example using past admissions to plan winter staffing, or early warning scores to flag patients likely to deteriorate.
Visualization
Run charts and control charts show change over time and help separate real improvement from normal variation. Dashboards should show a few meaningful indicators, each with a target and a clear trend.
Typical Assignments and How to Approach Them
WGU assesses competency through performance assessments or objective assessments; check your course page for the current format. The informatics specialty also uses a clinical practice experience record and e-portfolio. Typical work includes:
| Task type | What it tests | How to approach it |
|---|---|---|
| Dataset analysis | Method choice | Match the method to the question and data type |
| Dashboard design | Clear indicators | Choose few measures with targets |
| Trend prediction | Planning from data | State assumptions and limits |
| Improvement recommendation | Data to action | Link each finding to a change |
Clinical Data Sources and Their Limits
The second competency names several sources, each with strengths and weaknesses worth discussing:
- EHR data: rich and current, but entered for care rather than research, so missing or inconsistent fields are common.
- Dashboards: quick to read, but only as good as the definitions behind each indicator.
- Remote monitoring devices: continuous readings, but prone to artifacts and alert overload.
- Telemedicine records: extend reach, but may capture less physical assessment data.
Where Students Get Stuck
- Using the wrong test. Identify whether variables are categorical or continuous first.
- Correlation as causation. An association between two measures does not prove one causes the other.
- Raw counts. Convert to rates before comparing groups of different sizes.
- Results without meaning. Every figure should lead to a clinical or operational implication.
Study Tips for C797
- Revise basic measures: mean, median, standard deviation, rates and percentages.
- Practice with your own unit's quality indicators, anonymized and aggregated.
- Learn to read a control chart and spot special-cause variation.
- Write a "so what" sentence under every table or chart.
How We Help with C797
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Frequently Asked Questions
WGU's MSN Nursing Informatics (BSN to MSN) program guide lists Data Science and Analytics at two competency units, in term three.
The course covers descriptive and inferential statistics and statistical inference, applied to healthcare data rather than abstract theory.
Databases, electronic health records, dashboards, remote monitoring devices and telemedicine are all named in the competencies.
Rates adjust for the size of a unit or population, so comparisons between groups are fair.
Yes. One competency asks you to predict clinical events or likelihoods from current or historical trend data to plan care.
No. We are an independent tutoring and writing service and are not connected with Western Governors University.