1TEACH ME
Explain [topic] in Statistics for Data Analysis to me step by step at a [beginner / intermediate / advanced] level. Use a simple example first, then a harder one, and ask me a question to check I understood.
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Statistics for Data Analysis: From Descriptive Stats to Inference – AI Tutor-Enabled Course Book
Original price was: $52.00.$30.00Current price is: $30.00.
- AI Tutor-enabled
- Self-paced
- Beginner → Intermediate
- Modules
- 10
- Study time
- About 35 hours
- Format
- Digital course book (PDF)
- Projects
- 4 + capstone
Turn data into conclusions you can explain and defend.
TypeNotes & books
DeliveryInstant download after payment
- Instant download after payment
- Lifetime access in My Courses
- Secure checkout
AI Tutor-Enabled Self-Paced Professional Course Book
Turn data into conclusions you can explain and defend.
Statistics for Data Analysis is a self-paced course book covering descriptive statistics, probability, sampling, confidence intervals, hypothesis tests, ANOVA and regression. Each idea is linked to a practical question, so you learn which method to choose and how to explain the result.
Is this you?
- You can calculate a statistic but not explain what it means
- Choosing the right test feels like guessing
- p-values and confidence intervals are confusing
- Your project data needs analysis and you are not sure where to start
- Statistics software output is overwhelming
- You worry about misinterpreting results
This course book is built to close exactly that gap: clear explanations, worked practice and an AI tutor that guides you instead of just giving answers.
- Learn
- Practise
- Check with your AI Tutor
- Build projects
- Get assessed
What you’ll learn in 10 practical modules
01 Data and VariablesUnderstand the types of data you will analyse.
Key concepts
- Types of variables
- Sampling and study design
- Data quality
Sample practice
- Classify ten variables by type
- Spot the bias in a survey design
02 Descriptive StatisticsSummarise data clearly.
Key concepts
- Mean, median and mode
- Spread: range, IQR, standard deviation
- Outliers
Sample practice
- Find the median and IQR of a dataset
- Explain when the median beats the mean
03 Data VisualisationChoose the right chart.
Key concepts
- Histograms and box plots
- Scatter plots
- Misleading graphs
Sample practice
- Pick a chart for each of four questions
- Redesign a misleading chart
04 Probability BasicsReason about uncertainty.
Key concepts
- Rules of probability
- Conditional probability
- Independence
Sample practice
- P(A)=0.3, P(B)=0.5, independent: find P(A and B)
- Use a two-way table to find a conditional probability
05 Probability DistributionsModel random outcomes.
Key concepts
- Binomial distribution
- Normal distribution
- z-scores
Sample practice
- Find P(X≤2) for Binomial(10, 0.2)
- Find the proportion above z=1.5
06 Sampling DistributionsSee why samples vary.
Key concepts
- Central Limit Theorem
- Standard error
- Law of large numbers
Sample practice
- Find the standard error for n=64, σ=16
- Explain the CLT in plain words
07 Confidence IntervalsEstimate with a margin of error.
Key concepts
- Intervals for means
- Intervals for proportions
- Choosing sample size
Sample practice
- Construct a 95% CI for a mean
- Find n for a margin of error of 3%
08 Hypothesis TestingTest claims with data.
Key concepts
- Null and alternative hypotheses
- Type I and II errors
- t-tests and z-tests
Sample practice
- Run a one-sample t-test
- Explain a p-value of 0.03
09 Comparing GroupsTest differences between groups.
Key concepts
- Two-sample t-test
- Chi-square test
- One-way ANOVA
Sample practice
- Compare two teaching methods
- Test independence in a 2×3 table
10 Correlation and RegressionModel relationships between variables.
Key concepts
- Correlation
- Simple linear regression
- Residuals and assumptions
Sample practice
- Interpret r=−0.7
- Interpret a regression slope in context
Use AI as your personal tutor, not an answer machine
The course book includes ready-to-use prompts for ChatGPT, Copilot, Gemini or any capable AI assistant. Each mode makes the AI teach, guide, check or challenge you, so you build real understanding.
2GUIDE ME
I am working on this problem: [paste problem]. Do not give me the answer. Give me one hint at a time and wait for my next step.
3CHECK MY WORK
Here is my solution: [paste working]. Check each step, tell me exactly where any mistake is and why, but let me correct it myself.
4CHALLENGE ME
Give me three exam-style Statistics for Data Analysis questions on [topic], getting harder each time. Wait for my answers, then mark them and explain what I missed.
5HELP ME APPLY IT
Show me how [topic] from Statistics for Data Analysis is used in a real situation, then give me a small task to try it myself.
Verify before you trust
- Ask a clear, specific question
- Solve it yourself first
- Test the answer by substituting or estimating
- Check it against the worked examples in the book
- Validate with your teacher if the AI and the book disagree
Don’t just study it. Build evidence you can show.
Project 1
Survey Analysis
Clean and summarise a survey dataset with charts and descriptive statistics.
Project 2
A/B Test Report
Test whether a change made a difference and explain the result.
Project 3
Regression Mini-Study
Model a relationship and check the assumptions.
Project 4
Data Story
Turn an analysis into a one-page report for a non-technical reader.
Final capstone
Full Data Analysis Project
Pose a question, analyse a dataset end to end and present findings with appropriate tests and visuals.
Who this course book is for
- University students in any major
- Nursing and health science students
- Business and psychology students
- Researchers and dissertation writers
- Junior data analysts
- Professionals who work with data
Where these skills lead
- Data analysis
- Health research
- Market research
- Public policy
- Business intelligence
- Quality management
The course builds skills and evidence; it does not guarantee employment or exam results.
What you receive
- 10 progressive learning modules
- Learning objectives, key concepts and practice problems
- Ready-to-use AI Tutor prompts for five learning modes
- Practical projects and a capstone
- Portfolio guide, assessment rubric and completion checklist
- Instant download and lifetime access in My Courses
| Format | Digital course book (PDF) |
|---|---|
| Learning mode | Self-paced + AI Tutor |
| Level | Beginner → Intermediate |
| Study time | About 35 hours |
| Created by | NoteStore Academy |
| Certificate | Optional project and portfolio assessment |
| Licence | Personal learning use |
| Updated | November 2, 2026 |
Certificate pathway
Want a certificate? Submit your work for optional assessment by NoteStore Academy. Certificates reflect demonstrated work, so they are not issued automatically with purchase.
Module practice work20%
Projects 1–440%
Capstone project30%
Portfolio and reflection10%
Frequently asked questions
Which software do I need?
None is required. Examples work by hand or in Excel, Google Sheets, R, SPSS or Python.
Is it suitable for nursing and psychology students?
Yes. Examples include health and behavioural data, and the tests covered are the ones these courses use most.
Do I need a maths background?
Basic algebra is enough. Formulas are always explained in words first.
Can it help with my dissertation analysis?
Yes. Modules 7–10 cover the methods most dissertations need, and the capstone follows a research-report structure.
How do I get the course book after paying?
Your download link appears on the order confirmation page straight away, is emailed to you, and stays in My Courses in your account.
Which AI assistant can I use?
Any capable assistant works, such as ChatGPT, Microsoft Copilot or Google Gemini. The prompts are written to work with all of them.
Start today
Statistics for Data Analysis: From Descriptive Stats to Inference
Self-paced, AI Tutor-enabled and project-based. Download instantly after payment and keep it in My Courses.
Original price was: $52.00.$30.00Current price is: $30.00.
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