Data Analysis with AI Assistants
AI assistants can total, compare, and chart a spreadsheet in seconds, but some of their confident answers are computed and some are guessed. This course teaches you to prepare data, ask questions with one meaning, and check the math before a decision rests on it. You'll learn to read trends with their caveats, triage outliers, draw honest charts, and keep a record anyone can reproduce. You'll finish with a five-step analysis habit and a checklist for your first dataset.
Course details
Duration
1h 03m 14s
Lectures
15
Video
1 hour
Quizzes
1
Level
Beginner
Course Description
Hand a spreadsheet to an AI assistant and you get answers, charts, and a confident summary in seconds. Some of it is calculated from your data; some is commentary that only sounds like analysis. This course shows you how to get the first kind, spot the second, and verify everything that matters.
What you'll learn
- The four tasks an assistant does reliably on a table, and how to prepare data for them
- Questions with one meaning, the calculation behind every number, and quick math checks
- The five common analysis failures and a prevention block that stops them
- Trends with caveats, consistent segments, and triaging outliers
- Honest charts, and separating correlation from causation
- A five-part summary, reproducible records, data governance, and when to call an analyst
Who it's for
Business professionals who work with spreadsheets and exports and want trustworthy answers from AI assistants, without a statistics background.
15 short video lessons in three modules: Getting Started with Data and Assistants; Real Analysis Tasks; and Delivering Analysis at Work.
This course is educational and is not legal, financial, or professional advice. Your employer's AI and data policies govern.
Module 1: Getting Started with Data and Assistants
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1.1 What "Analysis with AI" Actually MeansDescribing, comparing, trending, and flagging — what the assistant does with a table, and what it guesses.
In this lesson:
- Name the four analysis tasks an assistant does reliably on a table
- Distinguish computed answers from guessed ones
- Set realistic expectations for AI-assisted analysis
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1.2 Preparing the Data
Clean headers, one table per file, consistent units — and removing what shouldn't leave the building.
In this lesson:
- Prepare a table so an assistant can analyze it correctly
- Recognize the structural problems that cause wrong answers
- Remove sensitive columns before any data enters a tool
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1.3 Asking the First Questions
Totals, averages, top and bottom, changes over time — how to phrase them and how to read the answer.
In this lesson:
- Phrase descriptive questions so the answer is unambiguous
- Read an answer for the definition the tool used
- Ask for the calculation alongside the result
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1.4 Checking the Assistant's Math
Spot-checks, round-number sanity, and the two figures you always recompute yourself.
In this lesson:
- Run a spot-check on AI-computed figures
- Use round-number estimation to catch large errors instantly
- Identify the two figures in any analysis that must be recomputed independently
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1.5 Where Data Analysis Goes Wrong
Wrong column, wrong filter, wrong date range — the failure patterns and how each one looks in an answer.
In this lesson:
- Name the five most common AI analysis failures
- Recognize the symptom of each in an answer
- Prevent each with a line in the prompt
Module 2: Real Analysis Tasks
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2.1 Trends and Comparisons
Month over month, segment versus segment — and asking for the caveat behind every trend.
In this lesson:
- Ask for trend and comparison analyses with explicit definitions
- Request the caveats that accompany any trend
- Distinguish a real trend from noise and from a base-rate effect
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2.2 Segmenting and Grouping
By customer, region, product, or team — and keeping group definitions consistent across questions.
In this lesson:
- Define segments explicitly before asking for grouped analysis
- Keep segment definitions consistent across a session and a report
- Detect when a segment is too small to support a conclusion
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2.3 Outliers and Anomalies
Asking what looks unusual, then deciding whether it's a data error or a real signal.
In this lesson:
- Ask the assistant to surface outliers with a stated method
- Triage each outlier: error, one-off, or signal
- Decide how outliers are handled in the analysis and say so
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2.4 Charts That Tell the Truth
Choosing the chart, labeling it honestly, and the distortions to refuse.
In this lesson:
- Choose the chart type that matches the question
- Specify labels, axes, and sources so the chart is honest
- Reject AI-generated charts that distort the data
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2.5 Correlation, Causation, and Over-Reach
Reading "because" in an AI answer with suspicion — what the data can and cannot say.
In this lesson:
- Distinguish correlation from causation in AI-generated analysis
- Identify the over-reach patterns in AI commentary
- Rewrite causal claims as what the data supports
Module 3: Delivering Analysis at Work
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3.1 The Analysis Summary
Question, method, finding, confidence, caveats — five lines a decision-maker can trust.
In this lesson:
- Write a five-part analysis summary
- State confidence based on data quality and sample size
- Draft the summary with the assistant from verified results
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3.2 Reproducibility
Saving the prompts, the data version, and the steps so the analysis can be re-run and defended.
In this lesson:
- Keep an analysis record that allows exact re-running
- Version the data used so results can be traced
- Re-run an analysis on new data without redoing the thinking
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3.3 Data Privacy and Governance
Customer and employee data, aggregation before analysis, and approved tools only.
In this lesson:
- Apply aggregation and minimization before data enters a tool
- Recognize data that needs governance approval before analysis
- Use only approved tools and document data handling in the record
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3.4 When to Bring in an Analyst
The questions that need statistics, modeling, or a data professional — and how to hand off well.
In this lesson:
- Recognize the question types that exceed AI-assisted self-service analysis
- Hand off to an analyst with a package that saves them time
- Know what to keep doing yourself even when an analyst is involved
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3.5 The Analyst's Habit
Prepare, ask, check, chart, summarize — the checklist and your first dataset; the finale.
In this lesson:
- Recall the five-step analysis habit
- Apply the course checklist to a real dataset
- Complete Data Analysis with AI Assistants
Data Analysis with AI Assistants — Final Exam
Learn at your own pace
Study the materials at your convenience, you can always return to them to repeat
Certificate of completion
Complete every lesson and pass the final exam to earn a RiseCert certificate of completion you can download and share.
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Data Analysis with AI Assistants
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Got Questions? We've Got Answers!
Answers to the most common questions about this course.
What is the Data Analysis with AI Assistants course?
Data Analysis with AI Assistants is a RiseCert online course that teaches you to analyze business data with an AI assistant and check the results before a decision rests on them. It covers preparing tables, asking precise questions, verifying the math, honest charts, and a five-part analysis summary across 15 video lessons and a final exam.
Who should take a course on data analysis with AI?
The course is built for business professionals who work with spreadsheets and exports, such as sales, operations, support, marketing, and team leads, and who want reliable answers from an AI assistant. It suits anyone who is asked what's going on in the numbers but is not a statistician or data professional.
Do I need statistics, coding, or spreadsheet expertise?
No coding or statistics background is required. Data Analysis with AI Assistants assumes basic spreadsheet comfort, such as filtering a column and writing a sum, and explains everything else in plain language. The course also teaches you to recognize questions, like significance tests and forecasts, that belong with a trained analyst rather than self-service analysis.
What can an AI assistant reliably do with a spreadsheet?
The course names four reliable tasks: describe (totals, averages, counts), compare (segment versus segment, month versus month), trend (change over time and its rate), and flag (values that stand out). All four are calculations on data you supplied, so each is checkable. Explanations of why something happened are commentary, not computation.
How do I check whether an AI assistant's numbers are right?
Data Analysis with AI Assistants teaches three levels of check: a ten-second estimate of magnitude and direction, a spot-check of three random rows plus one group you recompute yourself, and independent recomputation of the headline number and the decision number every time. Asking for the row count with each calculation catches most filtering errors.
What are the most common mistakes in AI data analysis?
The course catalogs five failures: wrong column, wrong filter, wrong date range, double counting, and silent exclusion. Each produces a confident, precise, wrong number. A saved prevention block of six instructions, including exact column headers, explicit dates, and showing the row count and exclusions for every number, guards against all five.
Can AI tell me why my sales or metrics changed?
Not from the table alone. The course teaches that data shows what moved together, not what caused it; establishing cause needs a comparison group, a controlled change, or domain knowledge. Phrases like "driven by" or "likely due to" are hypotheses. You rewrite them as computed facts plus a labeled hypothesis with a way to test it.
Is it safe to upload company data to an AI assistant?
Only within your organization's rules. The course teaches aggregating and minimizing data before it enters a tool, removing names, identifiers, and free-text columns, and using approved tools only, never a personal account. Employee, individual customer, regulated, and partner data need approval from privacy, HR, legal, or compliance first.
How is this course different from a general AI productivity course?
General AI courses cover drafting and everyday tasks. Data Analysis with AI Assistants focuses on getting trustworthy numbers: table preparation, unambiguous questions, verification routines, trend caveats, consistent segments, outlier triage, honest chart specs, reproducible analysis records, data governance, and knowing when a question needs a professional analyst instead.
Which AI tools or spreadsheet programs does the course cover?
The course is vendor-neutral and teaches practices that work with any AI assistant your organization has approved and any spreadsheet program. No specific product is required, recommended, or endorsed. The emphasis is on prompts, checks, and records that carry over as tools change.
How long does the course take?
Data Analysis with AI Assistants has about 1 hour 5 minutes of video across 15 lessons, plus a final exam. Lessons run about four minutes each in three modules: Getting Started with Data and Assistants; Real Analysis Tasks; and Delivering Analysis at Work. Most learners finish in a few sittings.
Is the course online and self-paced?
Yes. The RiseCert course is 100% online and self-paced, and your access does not expire. You can start, pause, and resume at any time on a computer, tablet, or phone with a modern web browser, and there is nothing to install.
Is there a final exam?
Yes. The course ends with a 30-question multiple-choice final exam covering all three modules. You need 80% or higher (24 of 30) to pass, and you can retake the exam as many times as you need. Complete all 15 lessons and pass the exam to finish the course.
Will I get a certificate after completing the course?
Yes. After you complete all 15 lessons and pass the final exam, you can download a RiseCert certificate of completion. It is a course completion certificate, not a license, a professional certification, or a continuing education certificate, and it does not replace any credential your role requires.
Can I earn continuing education credit for this course?
No. Data Analysis with AI Assistants is not currently approved or accredited for continuing education credit, including CE, CPE, or CEU credit, by any state, licensing body, or professional organization. If you need credit hours for a license or designation, take a course approved by the body that governs it.
Is this course legal, financial, or professional advice?
No. The course is educational training, not legal, financial, statistical, or HR advice. Your employer's AI policy and data-governance rules govern which tools and data you may use. For decisions that need significance testing, forecasting, or legal review, involve a qualified analyst, privacy officer, or counsel.
How much does the course cost, and can I enroll my team?
The course costs $99 per learner as a one-time purchase, with no subscription or recurring fees. To enroll a team or department, contact RiseCert through the Contact Us page at risecert.org to discuss team access.
What is the refund policy?
All sales are final and no refunds are issued, as stated in the RiseCert Terms of Use. Please review the course description, lesson list, and these FAQs before purchasing, and contact RiseCert with any questions before you enroll.
What will I be able to do after finishing the course?
You will be able to prepare a clean table, ask questions with one meaning, verify the two numbers that matter, triage outliers, build an honest chart, and write a five-part summary of question, method, finding, confidence, and caveats. The final lesson includes a checklist and a five-day plan for your first dataset.
How do I make an AI-assisted analysis reproducible?
Keep an analysis record. The course's record holds four things: the data (a dated snapshot, row count, and columns removed), the prep steps, every prompt that produced a kept number, and the decisions on outliers and confidence. With it, a number can be traced months later and a monthly re-run takes an hour.