WGU C797: Data Science and Analytics

What C797 Data Science and Analytics Actually Is

WGU's C797, Data Science and Analytics, is a course built to give you working exposure to the tools and thinking behind modern data-driven decision making. Rather than a single sit-down test, the course is organized around a set of short, self-paced microcourses that walk you through practical skills, and you demonstrate your learning by completing those microcourses and reflecting on what you took away from each one. It shows up in graduate-level informatics and analytics tracks, so many of the students who take it come to it not as career data scientists but as professionals who need to read, interpret, and communicate data confidently in their own field.

Direct answer: To pass C797, complete each assigned microcourse in full, save the certificate of completion for every one, and write a thoughtful, specific reflection that ties what you learned to real analytical work. This is a completion-and-reflection performance assessment, not a memorization exam, so following the task template exactly and doing genuine, original writing is what gets you through.

Because there is no multiple-choice exam here, the mindset that works is different from a heavy theory course. You are not being asked to derive formulas from scratch or write production code under pressure. You are being asked to move through curated lessons on data visualization, forecasting, and statistics, then articulate in your own words how those concepts apply. Treat the reflection as the real deliverable and the microcourses as the raw material you are reflecting on.

Topics and Tools the Course Covers

The microcourse lineup emphasizes practical, industry-standard analytics tools rather than a single programming language. Based on publicly described course materials, you can expect to work through content such as:

• Data visualization in Power BI — building dashboards and using visualization techniques to surface insights from a dataset.
• Interactive dashboards in Tableau — creating dashboards that let a viewer explore the data rather than read a static chart.
• Business forecasting — using methods such as exponential smoothing to project near-term values from past observations.
• Statistics in Excel — essential statistical training that grounds the descriptive and inferential concepts you will reference in your reflection.

The unifying theme is applied analytics: taking data, choosing an appropriate technique, and presenting the result so a decision maker can act on it. You are learning the vocabulary and the workflow of analysis more than any one narrow algorithm.

How Hard Is C797 and How Long Does It Take?

Difficulty is genuinely personal here, but the structure works in your favor. Because the assessment rewards completing lessons and reflecting rather than passing a timed, high-stakes exam, many students report that C797 feels lighter than theory-heavy quantitative courses, provided they set aside focused time to actually watch the microcourses instead of skimming them. The friction most people hit is not conceptual difficulty; it is the discipline to finish every microcourse and to write reflections that are specific rather than vague.

Realistic prep time varies with your comfort around data tools. Students who already use spreadsheets and dashboards often move quickly, while those newer to analytics should budget more time to absorb the material properly. Rather than chasing someone else's "I finished it in a weekend" story, plan around your own weekly study hours and the number of microcourses assigned in your term, then front-load the viewing so you are reflecting on fresh material.

A Study Plan That Fits This Course

Standard learning-science tactics still help, but they need to be adapted to a completion-and-reflection format instead of a question bank.

• Sequence the microcourses deliberately. Work through them in order and do not multitask through the videos. Take short notes as you go so that when you sit down to write the reflection, you already have concrete examples to draw on.
• Use active recall on the concepts, not the trivia. After each microcourse, close your notes and explain the core idea aloud — what exponential smoothing does, why a Tableau dashboard beats a static chart, what a particular statistic tells you. If you can teach it, you can reflect on it.
• Space your reflections. Instead of binging every microcourse and writing all reflections at the end from a blur of memory, reflect soon after each one while the details are sharp. Spacing the writing also spaces the learning.
• Practice by doing, lightly. Where you can, open the tool yourself and reproduce a small piece of what was demonstrated. Even a quick Excel chart or a simple Power BI visual turns passive watching into practice testing and gives your reflection authentic detail.
• Read the rubric before you write. Map each reflection prompt in the task template to what the rubric expects, and make sure every required element is visibly addressed.

If your quantitative footing feels shaky, brushing up on core statistics first pays off. Our guide to C955 Applied Probability and Statistics is a good place to shore up the descriptive and inferential basics, and D467 Exploring Data covers the data-exploration mindset that C797 assumes you already carry.

Common Mistakes to Avoid

• Skimming the microcourses. The reflection reveals whether you actually watched. Vague, generic writing that could apply to any lesson is the fastest way to get a task sent back.
• Forgetting the certificates. Each microcourse completion certificate is part of the evidence. Save or screenshot every one as you finish, so you are not hunting for them at submission time.
• Reusing outside language. Performance assessment submissions are checked for similarity, and reflections must be your own original work. Never paste text from study-site "templates," classmates, or the microcourse transcript.
• Ignoring the task template. The reflection has a required structure. Answer every prompt in the order and format the template specifies rather than free-writing an essay.
• Waiting to reflect. Batching all the writing at the end drains the specific detail that makes a reflection strong. Write while each lesson is fresh.

C797 Readiness Checklist

Before you submit, run through these self-checks. If you can answer yes to each, you are in good shape.

• Can you confirm every assigned microcourse is fully completed, not just started?
• Can you produce a certificate of completion for each microcourse you are reflecting on?
• Can you explain, in plain language, the main takeaway of each microcourse?
• Can you point to a specific concept or technique from each lesson in your reflection, rather than a generic summary?
• Can you match every reflection prompt in the task template to a paragraph you actually wrote?
• Can you connect at least one microcourse concept to a real analytical or professional situation?
• Can you confirm the writing is entirely your own and free of pasted outside text?
• Can you verify your submission follows the required template format from top to bottom?

C797 FAQ

Is C797 an OA or a PA?

C797 is assessed by a performance assessment. There is no objective (multiple-choice) exam. You complete microcourses and submit reflection tasks together with certificates of completion.

Does C797 require coding in R or Python?

No. Publicly described course materials center on applied analytics tools such as Power BI, Tableau, and Excel, along with forecasting and statistics concepts, rather than writing code in R or Python. Confirm the exact microcourse list in your own course portal, since program requirements can change.

How many microcourses are there?

The number can vary by program and term, so rely on your official course of study for the current list rather than any outside figure. Your course instructor can confirm exactly which microcourses your term requires.

How long does it take to finish?

It depends heavily on your prior comfort with data tools and how many hours you can study each week. Many students find the completion-and-reflection format lets them move at a steady, predictable pace once they commit focused time to the microcourses.

What is the fastest way to fail the task?

Submitting vague reflections that do not show you engaged with the lessons, omitting certificates of completion, or including any text that is not your own original writing. Genuine engagement with each microcourse is what the assessment is checking for.

Where can I get official course information?

Your WGU course of study and course instructor are the authoritative sources for current requirements. You can also review WGU's public program pages, such as the Data Analytics program overview, for context on how analytics fits into WGU's degrees.

Keep Going

C797 rewards steady, honest effort more than last-minute cramming. Work the microcourses in order, reflect while the material is fresh, and follow the template exactly. For related preparation, browse the School of Technology guides or the full library of WGU course guides to line up your next course.