DATA 320 at the University of Maryland Global Campus is a three-credit course that UMGC lists as formerly DATA 220, with STAT 200 as the prerequisite. Students keep the old code in older materials, so you may see both in forums and notes.
UMGC describes a practical introduction to the methods of data science, with a focus on applying foundational statistics: summary statistics, simple visualizations, statistical inference and predictive analytics.
The objective is to use data to draw conclusions about the patterns behind everyday problems through probability, hypothesis testing and linear model building.
Course at a Glance
| Item | Details |
|---|---|
| University | University of Maryland Global Campus (UMGC) |
| Course code | DATA 320 (formerly DATA 220) |
| Level and credits | Undergraduate, 3 credits |
| Prerequisite | STAT 200 |
| Subject area | Applied statistics for data analytics |
| Typical work | Statistical analyses of datasets, hypothesis tests, linear models, written interpretation |
What DATA 320 Covers
| Topic (from the UMGC description) | What you should be able to do |
|---|---|
| Summary statistics | Describe center and spread, and choose the right measure for the data |
| Simple visualizations | Show distributions and relationships clearly |
| Probability | Quantify uncertainty and chance variation |
| Hypothesis testing and inference | Judge whether a pattern is likely real or due to chance |
| Linear model building | Fit a line, interpret it and judge how well it predicts |
Key Concepts Explained
Hypothesis Testing in Plain Terms
You assume there is no effect (the null hypothesis), then ask how surprising your data would be if that were true. A small p-value says the data would be unusual under the null. It does not give the probability that the null is true, and it says nothing about practical importance.
Example: A company claims a new checkout page raises average order value from $50. A sample of 40 orders on the new page has a mean of $54. The test asks whether a difference that size could easily arise by chance given the spread of order values. Reporting the interval for the difference, not only the p-value, shows how large the improvement might be.
Interpreting a Linear Model
In a model such as sales = 12 + 3.5 x advertising spend, the slope says sales rise by 3.5 units on average for each extra unit of spend within the range of the data. The intercept is the predicted value when spend is zero, which is only meaningful if zero lies in the data.
Typical Assignments and How to Approach Them
UMGC does not publish individual assessments in the course description. Courses of this type typically include the following.
| Assignment type | What it tests | How to approach it |
|---|---|---|
| Descriptive analysis | Summaries and charts | Match the statistic to the data type and explain the choice |
| Hypothesis test | Inference | State hypotheses, check assumptions, then interpret in context |
| Regression task | Fitting and reading a linear model | Plot first, then interpret slope, fit and limits |
| Business summary | Communicating results | Answer the question in the first sentence |
Choosing the Right Statistical Tool
Much of the course is deciding which method fits. This table gives a starting point for typical questions.
| Question | Typical tool | Check first |
|---|---|---|
| What is typical, and how much does it vary? | Mean and standard deviation, or median and quartiles | Is the data skewed? |
| Is a sample mean different from a claimed value? | One-sample test | Sample size and shape |
| Do two groups differ? | Two-sample comparison | Independent or paired groups |
| Does X help predict Y? | Simple linear regression | A roughly linear scatter plot |
Where Students Get Stuck
- Misreading p-values. A p-value is not the chance that the claim is true.
- Skipping assumptions. State and check them before trusting a test.
- Cause versus correlation. A fitted line shows association only.
- Software output without interpretation. Always translate numbers into a sentence about the problem.
Study Tips for DATA 320
- Review STAT 200 notes on distributions and confidence intervals.
- Plot every dataset before calculating anything.
- Write the hypotheses in words before choosing a test.
- Keep a template for reporting results: method, result, meaning, limit.
A Worked Interpretation Paragraph
Marks often depend on how you write results. Compare a weak sentence with a stronger one.
Weak: "The p-value was 0.03 so it is significant." Stronger: "Average delivery time was 3.2 days for the new carrier and 3.9 for the old one. The difference of 0.7 days is unlikely to be due to chance alone (p = 0.03), and the interval for the difference runs from 0.1 to 1.3 days, so the improvement may be modest. The sample covers one month, which limits how far the result can be generalized."
The stronger version gives the numbers, the meaning, the uncertainty and a limit.
How We Help with DATA 320
Send the task, dataset, rubric and your attempt. A tutor can explain a method with a fresh example, check that you have chosen an appropriate test, or review your interpretation. For further support, see our regression analysis help guide.
GradeEssays is independent of the University of Maryland Global Campus. Our work is a study and reference aid: use it to understand the method, then complete and submit your own work under UMGC's academic integrity policy. Every order is written from scratch, with free revisions within the scope of your original request and a full refund if it is late.
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Frequently Asked Questions
Yes. UMGC's description says "Formerly DATA 220".
UMGC lists STAT 200.
Summary statistics, simple visualizations, statistical inference, predictive analytics, probability, hypothesis testing and linear model building.
As the average change in the outcome for a one-unit increase in the predictor, within the range of the data.
Say so, consider a more suitable method or transformation, and report the limitation honestly.
No. We provide tutoring, custom answers and feedback, and you must submit your own work under UMGC's policy.