Build practical programming and technology skills

Statistics for Data Science

Summarize data, reason about probability and sampling, interpret confidence intervals and tests, and communicate statistical uncertainty responsibly.

View curriculum

FoundationDifficulty

30 hoursEstimated time

8 modules32 lessons

5 guided labsKnowledge check

Plan your learning

Path Brief

Skills you will practice

Data Ai · Statistics Data Science
Data Ai · Data Variables And Measurement
Data Ai · Distributions And Descriptive Statistics
Data Ai · Probability Foundations
Data Ai · Sampling And Sampling Variation
Data Ai · Confidence Intervals

Prerequisites

No prior experience is required; basic computer and file-management skills are helpful.

Tools

A current web browser
A code editor or terminal appropriate to the path
A safe local or virtual lab environment

Before you begin

What you need

No prior experience is required; basic computer and file-management skills are helpful.

Step-by-step curriculum

Learning modules

Complete lessons in order or open the module that matches your current goal. Your lesson progress is saved on this device.

01Data, Variables, and MeasurementBuild practical skill in data, variables, and measurement and connect it to the outcomes of Statistics for Data Science.4 lessons · 1 lab

Lesson 1

Data, Variables, and Measurement: Core Concepts for Statistics for Data Science

Understand the concepts, decisions, and vocabulary you need before applying Data, Variables, and Measurement in Statistics for Data Science.

25 min

Lesson 2

Set Up and Explore Data, Variables, and Measurement in Statistics for Data Science

Prepare the tools, data, project state, or test environment needed to explore Data, Variables, and Measurement safely and repeatably.

25 min

Lesson 3

Build a Practical Data, Variables, and Measurement Example in Statistics for Data Science

Create a small chart or dashboard from a tidy dataset, explain why the encoding matches the question, then revise one misleading scale, aggregation, color, or labeling choice.

30 min

Lesson 4

Debug Common Data, Variables, and Measurement Problems in Statistics for Data Science

Recognize common failure modes in Data, Variables, and Measurement, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Safe

Guided Lab: Data, Variables, and Measurement in Statistics for Data Science

Statistics for Data Science — Data, Variables, and Measurement lab: Create a small chart or dashboard from a tidy dataset, explain why the encoding matches the question, then revise one misleading scale, aggregation, color, or labeling choice.

Not started

02Distributions and Descriptive StatisticsBuild practical skill in distributions and descriptive statistics and connect it to the outcomes of Statistics for Data Science.4 lessons · 1 lab

Lesson 1

Distributions and Descriptive Statistics: Core Concepts for Statistics for Data Science

Understand the concepts, decisions, and vocabulary you need before applying Distributions and Descriptive Statistics in Statistics for Data Science.

25 min

Lesson 2

Set Up and Explore Distributions and Descriptive Statistics in Statistics for Data Science

Prepare the tools, data, project state, or test environment needed to explore Distributions and Descriptive Statistics safely and repeatably.

25 min

Lesson 3

Build a Practical Distributions and Descriptive Statistics Example in Statistics for Data Science

Analyze a small numeric dataset, report appropriate summaries, and explain one limitation of the conclusion.

30 min

Lesson 4

Debug Common Distributions and Descriptive Statistics Problems in Statistics for Data Science

Recognize common failure modes in Distributions and Descriptive Statistics, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Distributions and Descriptive Statistics in Statistics for Data Science

Statistics for Data Science — Distributions and Descriptive Statistics lab: Analyze a small numeric dataset, report appropriate summaries, and explain one limitation of the conclusion.

Not started

03Probability FoundationsBuild practical skill in probability foundations and connect it to the outcomes of Statistics for Data Science.4 lessons · 1 lab

Lesson 1

Probability Foundations: Core Concepts for Statistics for Data Science

Understand the concepts, decisions, and vocabulary you need before applying Probability Foundations in Statistics for Data Science.

25 min

Lesson 2

Set Up and Explore Probability Foundations in Statistics for Data Science

Prepare the tools, data, project state, or test environment needed to explore Probability Foundations safely and repeatably.

25 min

Lesson 3

Build a Practical Probability Foundations Example in Statistics for Data Science

Calculate probabilities for a small scenario and compare an analytic result with a simple simulation.

30 min

Lesson 4

Debug Common Probability Foundations Problems in Statistics for Data Science

Recognize common failure modes in Probability Foundations, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Probability Foundations in Statistics for Data Science

Statistics for Data Science — Probability Foundations lab: Calculate probabilities for a small scenario and compare an analytic result with a simple simulation.

Not started

04Sampling and Sampling VariationBuild practical skill in sampling and sampling variation and connect it to the outcomes of Statistics for Data Science.4 lessons · 1 lab

Lesson 1

Sampling and Sampling Variation: Core Concepts for Statistics for Data Science

Understand the concepts, decisions, and vocabulary you need before applying Sampling and Sampling Variation in Statistics for Data Science.

25 min

Lesson 2

Set Up and Explore Sampling and Sampling Variation in Statistics for Data Science

Prepare the tools, data, project state, or test environment needed to explore Sampling and Sampling Variation safely and repeatably.

25 min

Lesson 3

Build a Practical Sampling and Sampling Variation Example in Statistics for Data Science

Analyze a small numeric dataset, report appropriate summaries, and explain one limitation of the conclusion.

30 min

Lesson 4

Debug Common Sampling and Sampling Variation Problems in Statistics for Data Science

Recognize common failure modes in Sampling and Sampling Variation, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Sampling and Sampling Variation in Statistics for Data Science

Statistics for Data Science — Sampling and Sampling Variation lab: Analyze a small numeric dataset, report appropriate summaries, and explain one limitation of the conclusion.

Not started

05Confidence IntervalsBuild practical skill in confidence intervals and connect it to the outcomes of Statistics for Data Science.4 lessons · 1 lab

Lesson 1

Confidence Intervals: Core Concepts for Statistics for Data Science

Understand the concepts, decisions, and vocabulary you need before applying Confidence Intervals in Statistics for Data Science.

25 min

Lesson 2

Set Up and Explore Confidence Intervals in Statistics for Data Science

Prepare the tools, data, project state, or test environment needed to explore Confidence Intervals safely and repeatably.

25 min

Lesson 3

Build a Practical Confidence Intervals Example in Statistics for Data Science

Analyze a small numeric dataset, report appropriate summaries, and explain one limitation of the conclusion.

30 min

Lesson 4

Debug Common Confidence Intervals Problems in Statistics for Data Science

Recognize common failure modes in Confidence Intervals, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Confidence Intervals in Statistics for Data Science

Statistics for Data Science — Confidence Intervals lab: Analyze a small numeric dataset, report appropriate summaries, and explain one limitation of the conclusion.

Not started

06Hypothesis TestingBuild practical skill in hypothesis testing and connect it to the outcomes of Statistics for Data Science.4 lessons

Lesson 1

Hypothesis Testing: Core Concepts for Statistics for Data Science

Understand the concepts, decisions, and vocabulary you need before applying Hypothesis Testing in Statistics for Data Science.

25 min

Lesson 2

Set Up and Explore Hypothesis Testing in Statistics for Data Science

Prepare the tools, data, project state, or test environment needed to explore Hypothesis Testing safely and repeatably.

25 min

Lesson 3

Build a Practical Hypothesis Testing Example in Statistics for Data Science

Test whether two independent sample means differ, report the null hypothesis, test statistic, p-value, significance decision, confidence interval, and a short interpretation in context.

30 min

Lesson 4

Debug Common Hypothesis Testing Problems in Statistics for Data Science

Recognize common failure modes in Hypothesis Testing, use the relevant diagnostics, and verify the correction.

25 min

07Correlation and RegressionBuild practical skill in correlation and regression and connect it to the outcomes of Statistics for Data Science.4 lessons

Lesson 1

Correlation and Regression: Core Concepts for Statistics for Data Science

Understand the concepts, decisions, and vocabulary you need before applying Correlation and Regression in Statistics for Data Science.

25 min

Lesson 2

Set Up and Explore Correlation and Regression in Statistics for Data Science

Prepare the tools, data, project state, or test environment needed to explore Correlation and Regression safely and repeatably.

25 min

Lesson 3

Build a Practical Correlation and Regression Example in Statistics for Data Science

Analyze a small numeric dataset, report appropriate summaries, and explain one limitation of the conclusion.

30 min

Lesson 4

Debug Common Correlation and Regression Problems in Statistics for Data Science

Recognize common failure modes in Correlation and Regression, use the relevant diagnostics, and verify the correction.

25 min

08Practical Statistical ReasoningBuild practical skill in practical statistical reasoning and connect it to the outcomes of Statistics for Data Science.4 lessons

Lesson 1

Practical Statistical Reasoning: Core Concepts for Statistics for Data Science

Understand the concepts, decisions, and vocabulary you need before applying Practical Statistical Reasoning in Statistics for Data Science.

25 min

Lesson 2

Set Up and Explore Practical Statistical Reasoning in Statistics for Data Science

Prepare the tools, data, project state, or test environment needed to explore Practical Statistical Reasoning safely and repeatably.

25 min

Lesson 3

Build a Practical Practical Statistical Reasoning Example in Statistics for Data Science

Analyze a small numeric dataset, report appropriate summaries, and explain one limitation of the conclusion.

30 min

Lesson 4

Debug Common Practical Statistical Reasoning Problems in Statistics for Data Science

Recognize common failure modes in Practical Statistical Reasoning, use the relevant diagnostics, and verify the correction.

25 min

Portfolio integration

Capstone Project

Produce a Statistical Analysis of an Observational Dataset

Combine the path skills in a portfolio-ready project with clear functional requirements, tests, verification evidence, and operating notes.

GuidedIndependentPortfolio

Acceptance criteria

The core workflow works from start to finish.
Invalid input or failure states are handled clearly.
The result is tested and documented.
A reviewer can reproduce the setup and verification.

Continue in Programming

Related Programming

Skill assessment

Statistics for Data Science

Path Brief

Skills you will practice

Prerequisites

Tools

What you need

Learning modules

Data, Variables, and Measurement: Core Concepts for Statistics for Data Science

Set Up and Explore Data, Variables, and Measurement in Statistics for Data Science

Build a Practical Data, Variables, and Measurement Example in Statistics for Data Science

Debug Common Data, Variables, and Measurement Problems in Statistics for Data Science

Guided Lab: Data, Variables, and Measurement in Statistics for Data Science

Distributions and Descriptive Statistics: Core Concepts for Statistics for Data Science

Set Up and Explore Distributions and Descriptive Statistics in Statistics for Data Science

Build a Practical Distributions and Descriptive Statistics Example in Statistics for Data Science

Debug Common Distributions and Descriptive Statistics Problems in Statistics for Data Science

Guided Lab: Distributions and Descriptive Statistics in Statistics for Data Science

Probability Foundations: Core Concepts for Statistics for Data Science

Set Up and Explore Probability Foundations in Statistics for Data Science

Build a Practical Probability Foundations Example in Statistics for Data Science

Debug Common Probability Foundations Problems in Statistics for Data Science

Guided Lab: Probability Foundations in Statistics for Data Science

Sampling and Sampling Variation: Core Concepts for Statistics for Data Science

Set Up and Explore Sampling and Sampling Variation in Statistics for Data Science

Build a Practical Sampling and Sampling Variation Example in Statistics for Data Science

Debug Common Sampling and Sampling Variation Problems in Statistics for Data Science

Guided Lab: Sampling and Sampling Variation in Statistics for Data Science

Confidence Intervals: Core Concepts for Statistics for Data Science

Set Up and Explore Confidence Intervals in Statistics for Data Science

Build a Practical Confidence Intervals Example in Statistics for Data Science

Debug Common Confidence Intervals Problems in Statistics for Data Science

Guided Lab: Confidence Intervals in Statistics for Data Science

Hypothesis Testing: Core Concepts for Statistics for Data Science

Set Up and Explore Hypothesis Testing in Statistics for Data Science

Build a Practical Hypothesis Testing Example in Statistics for Data Science

Debug Common Hypothesis Testing Problems in Statistics for Data Science

Correlation and Regression: Core Concepts for Statistics for Data Science

Set Up and Explore Correlation and Regression in Statistics for Data Science

Build a Practical Correlation and Regression Example in Statistics for Data Science

Debug Common Correlation and Regression Problems in Statistics for Data Science

Practical Statistical Reasoning: Core Concepts for Statistics for Data Science

Set Up and Explore Practical Statistical Reasoning in Statistics for Data Science

Build a Practical Practical Statistical Reasoning Example in Statistics for Data Science

Debug Common Practical Statistical Reasoning Problems in Statistics for Data Science

Capstone Project

Produce a Statistical Analysis of an Observational Dataset

Acceptance criteria

Test your understanding

Related Programming

Check your starting point

Plan the capstone

Explore related paths