MASTERY CHECKLIST · STATISTICS

Statistics Mastery Checklist — Data Analyst Roadmap

The complete statistics journey for aspiring data analysts. 32 priority-ranked topics, key concepts, a 30-60-90 day plan, 11 portfolio projects, real-world scenarios, and interview prep — everything you need to be job-ready.

Beginner → Advanced ~120 hrs totalIndustry-focused · Interview-ready

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Priority-Based Topic Checklist

Ordered by recommended learning sequence. Expand each for context; check off as you master them.

01.Descriptive Statistics — Mean, Median, Mode, Variance, Standard Deviation
Most Critical
Easy 2–3 hrsOrder #1● Critical
02.Data Types & Scales — Qualitative/Quantitative, Nominal, Ordinal, Interval, Ratio
Must Know
Easy 2–3 hrsOrder #2● Critical
03.Population vs Sample — Sampling Basics
Must Know
Easy 1–2 hrsOrder #3● Critical
04.Frequency Distributions & Histograms
Must Know
Easy 2–3 hrsOrder #4● Critical
05.Percentiles, Quartiles & IQR (Interquartile Range)
Must Know
Easy 2–3 hrsOrder #5● Critical
06.Skewness & Kurtosis — Understanding Distributions
Must Know
Easy 2–3 hrsOrder #6● High
07.Probability Basics — Events, Outcomes, Probability Rules
Must Know
Easy 3–4 hrsOrder #7● Critical
08.Conditional Probability & Bayes' Theorem
Must Know
Medium 3–4 hrsOrder #8● High
09.Probability Distributions — Normal, Binomial, Poisson, Uniform
Most Critical
Medium 4–5 hrsOrder #9● Critical
10.Central Limit Theorem (CLT) — Why Samples Behave Normally
Must Know
Medium 3–4 hrsOrder #10● Critical
11.Law of Large Numbers & Sampling Distributions
Medium 2–3 hrsOrder #11● High
12.Sampling Techniques — Random, Stratified, Systematic, Cluster
Must Know
Medium 3–4 hrsOrder #12● Critical
13.Sampling Bias & Sample Size Determination
Must Know
Medium 2–3 hrsOrder #13● High
14.Inferential Statistics Overview — Making Predictions from Samples
Must Know
Medium 2–3 hrsOrder #14● Critical
15.Confidence Intervals — Estimation & Interpretation
Must Know
Medium 3–4 hrsOrder #15● Critical
16.Hypothesis Testing — Null (H₀) vs Alternative (H₁)
Most Critical
Medium 3–4 hrsOrder #16● Critical
17.p-Values & Significance Levels (Alpha)
Must Know
Medium 3–4 hrsOrder #17● Critical
18.Type I & Type II Errors — False Positives vs False Negatives
Must Know
Medium 2–3 hrsOrder #18● Critical
19.Statistical Power & Effect Size
Medium 2–3 hrsOrder #19● High
20.Z-Test — When Population Variance is Known
Must Know
Medium 3–4 hrsOrder #20● Critical
21.T-Test — When Population Variance is Unknown (1-Sample, 2-Sample, Paired)
Must Know
Medium 4–5 hrsOrder #21● Critical
22.ANOVA — Analysis of Variance (1-Way & 2-Way)
Must Know
Medium 4–5 hrsOrder #22● Critical
23.Chi-Square Test — For Categorical Data (Goodness of Fit & Independence)
Must Know
Medium 3–4 hrsOrder #23● Critical
24.Parametric vs Non-Parametric Tests — Wilcoxon, Mann-Whitney U, Kruskal-Wallis
Hard 4–5 hrsOrder #24● High
25.Correlation — Pearson & Spearman
Must Know
Medium 3–4 hrsOrder #25● Critical
26.Correlation vs Causation — Avoiding a Common Pitfall
Most Critical
Medium 2–3 hrsOrder #26● Critical
27.Linear Regression — Simple & Multiple
Must Know
Hard 5–6 hrsOrder #27● Critical
28.Regression Assumptions — Linearity, Homoscedasticity, Normality of Residuals
Hard 3–4 hrsOrder #28● High
29.Logistic Regression — Binary Classification
Hard 4–5 hrsOrder #29● High
30.Time Series Basics — Trends, Seasonality, Stationarity
Hard 4–5 hrsOrder #30● Medium
31.A/B Testing & Experimental Design
Must Know
Hard 4–5 hrsOrder #31● High
32.Bayesian Statistics Basics — Prior, Likelihood, Posterior
Hard 3–4 hrsOrder #32● Medium

Non-Negotiable

Must Know for Every Data Analyst

You will use all of these in the first week of any data analyst role.

Descriptive Statistics (Day 1)

›Mean — Average of dataset
›Median — Middle value (robust to outliers)
›Mode — Most frequent value
›Variance — Average squared deviation from mean
›Standard Deviation — Spread of data in original units
›Range & IQR — Spread of middle 50% of data

Probability & Distributions (Every Analysis)

›Normal Distribution — Bell curve, mean=median=mode
›Binomial Distribution — Success/failure experiments
›Poisson Distribution — Count events over time/space
›Central Limit Theorem — Sample means become normal
›Conditional Probability — P(A|B) formula
›Bayes' Theorem — Updating beliefs with new evidence

Inferential Statistics (Every Project)

›Hypothesis Testing — H₀ vs H₁ framework
›p-Value — Probability of observing data if H₀ is true
›Confidence Intervals — Range containing population parameter
›Significance Level (α) — Usually 0.05
›Type I Error — False positive (reject H₀ when true)
›Type II Error — False negative (fail to reject H₀ when false)

Regression & Correlation (Predictive Work)

›Pearson Correlation — Linear relationship strength
›Spearman Correlation — Monotonic relationship
›Correlation ≠ Causation — Always remember
›Linear Regression — Predict Y from X
›R² — Proportion of variance explained
›Residuals — Differences between predicted and actual

Statistical Tests (Comparing Groups)

›Z-Test — Large sample, known σ
›T-Test — Small sample, unknown σ
›ANOVA — Compare 3+ group means
›Chi-Square Test — Categorical data analysis
›Mann-Whitney U — Non-parametric alternative to t-test
›Wilcoxon Test — Paired non-parametric test

Sampling & Data Collection

›Random Sampling — Every unit has equal chance
›Stratified Sampling — Divide population into strata
›Systematic Sampling — Every k-th unit
›Cluster Sampling — Random clusters then sample all
›Sampling Bias — Non-representative samples
›Sample Size — Balance between precision and cost

Core

Probability & Distributions — Deep Dive

Understanding randomness is the foundation of inference and decision-making.

Basic Probability Rules

P(A) = Number of favorable outcomes / Total outcomes
P(A ∪ B) = P(A) + P(B) - P(A ∩ B) — Addition Rule
P(A ∩ B) = P(A) × P(B) — For independent events
P(A|B) = P(A ∩ B) / P(B) — Conditional Probability
P(B|A) = P(A|B) × P(B) / P(A) — Bayes' Theorem
Law of Total Probability: P(A) = Σ P(A|B_i) × P(B_i)

Key Probability Distributions

Normal: f(x) = (1/σ√(2π)) × e^(-(x-μ)²/(2σ²))
Standard Normal: Mean=0, Variance=1 (Z-score)
Binomial: P(X=k) = C(n,k) × p^k × (1-p)^(n-k)
Poisson: P(X=k) = (λ^k × e^(-λ)) / k!
Uniform: All outcomes equally likely
Exponential: Models time between Poisson events

Central Limit Theorem (CLT)

Sample mean distribution approaches normal as n increases
Independent of population distribution shape
Mean of sample means = population mean (μ)
Standard error = σ/√n — decreases with sample size
Enables hypothesis testing and confidence intervals
Critical for: Z-tests, t-tests, ANOVA, regression inference

Core

Hypothesis Testing & Statistical Tests

The tools for making data-driven decisions and comparing groups.

Hypothesis Testing Framework

›Step 1: State H₀ (null) and H₁ (alternative)
›Step 2: Choose significance level (α = 0.05)
›Step 3: Collect data and calculate test statistic
›Step 4: Compute p-value
›Step 5: Decision: Reject H₀ if p < α
›Step 6: Conclusion in plain language

Statistical Tests Comparison

›Z-Test: Large sample (n > 30), known σ
›T-Test: Small sample (n < 30), unknown σ
›Paired T-Test: Same subjects measured twice (before/after)
›1-Way ANOVA: Compare 3+ groups on 1 factor
›2-Way ANOVA: Compare 3+ groups on 2 factors
›Chi-Square: Categorical data (e.g., survey responses)

Test Assumptions & When to Use

›Parametric tests: Normal distribution, homogeneity of variance
›Non-parametric: Use when assumptions violated
›Mann-Whitney U: Alternative to independent t-test
›Wilcoxon: Alternative to paired t-test
›Kruskal-Wallis: Alternative to 1-way ANOVA
›Check assumptions before choosing test — always

Structured

30-60-90 Day Statistics Learning Plan

For a working analyst putting in 1–2 hrs/day. Weekends include 3–4 hr project sessions.

Days 1–30

Foundation & Descriptive Statistics

Descriptive Stats, Probability, Sampling, Distributions

Days 1–3

Descriptive Statistics Basics

Mean, median, mode, variance, standard deviation, range, IQR

Days 4–6

Data Types & Data Visualization

Qualitative vs quantitative, scales of measurement, histograms, box plots

Days 7–9

Probability Fundamentals

Basic probability rules, conditional probability, Bayes' Theorem

Days 10–12

Probability Distributions

Normal, Binomial, Poisson, Uniform distributions

Days 13–14

Weekend Project #1

Analyze a dataset: calculate all descriptive stats, create visualizations

Days 15–17

Sampling & CLT

Sampling techniques, Central Limit Theorem, Law of Large Numbers

Days 18–20

Introduction to Inference

Population vs sample, sampling distributions, standard error

Days 21–23

Confidence Intervals

CI for mean, proportion; interpretation in business context

Days 24–26

Hypothesis Testing Part 1

H₀ vs H₁, p-values, significance levels, Type I & II errors

Days 27–30

Weekend Project #2

Hypothesis testing project: formulate, test, and interpret results

Days 31–60

Inferential Statistics & Tests

Z-Test, T-Test, ANOVA, Chi-Square, Non-Parametric Tests

Days 31–33

Z-Test & One-Sample T-Test

When to use each, assumptions, manual calculation with real data

Days 34–36

Two-Sample & Paired T-Tests

Independent samples, paired designs, equal vs unequal variance

Days 37–38

T-Test Project

Compare two groups in a real dataset: formulate, test, interpret

Days 39–42

ANOVA (1-Way & 2-Way)

Comparing 3+ groups, post-hoc tests, interaction effects

Days 43–44

ANOVA Project

Compare multiple groups, interpret F-statistic and p-value

Days 45–48

Chi-Square Tests

Goodness of fit, test of independence, effect size

Days 49–50

Chi-Square Project

Analyze categorical data: survey responses, customer preferences

Days 51–54

Non-Parametric Tests

Mann-Whitney U, Wilcoxon, Kruskal-Wallis — when to use and how

Days 55–60

Capstone Project #1

Choose the right test for a business problem, run it, present results

Days 61–90

Regression, Correlation & Advanced Topics

Correlation, Linear/Logistic Regression, A/B Testing, Bayesian Stats

Days 61–64

Correlation Analysis

Pearson vs Spearman, scatter plots, correlation matrix

Days 65–68

Simple Linear Regression

Model building, interpretation, R², residuals analysis

Days 69–72

Multiple Linear Regression

Multiple predictors, multicollinearity, variable selection

Days 73–74

Regression Project

Build and interpret a regression model on real data

Days 75–78

Logistic Regression

Binary classification, odds ratio, confusion matrix

Days 79–80

A/B Testing

Design, sample size calculation, analysis, business decisions

Days 81–83

Time Series Basics

Trends, seasonality, stationarity, simple forecasts

Days 84–86

Bayesian Statistics Intro

Prior, likelihood, posterior; when to use Bayesian approach

Days 87–90

Capstone Project #2

End-to-end analysis: descriptive + inference + regression + AB testing

Interview Prep

4-Phase Interview Preparation Roadmap

Expect live stats questions — practice explaining concepts and calculations.

Foundation — Core Concepts Cold

Week 1 · Daily 1.5 hrs

Must explain without hesitation

›Mean vs Median vs Mode — when to use each
›Standard deviation vs variance
›Population vs sample — key differences
›Normal distribution properties
›Central Limit Theorem — explain in plain English

Practice method

›Explain each concept to a non-technical friend
›Calculate stats by hand from a small dataset
›Use Python/Excel to verify your manual work
›Draw normal distribution and mark key points
›Create flashcards for all key formulas

Hypothesis Testing Mastery

Week 2 · Daily 2 hrs

Master these scenarios

›Formulate H₀ and H₁ for any business problem
›Choose the right test (Z, T, ANOVA, Chi-Square)
›Calculate p-value and interpret correctly
›Explain Type I vs Type II errors with examples
›When to use 1-tailed vs 2-tailed tests

Interview tips

›Always mention assumptions before using a test
›Explain what alpha=0.05 means in plain English
›Connect p-value to business decision-making
›Discuss sample size and statistical power
›Note limitations and caveats of your test choice

Regression & Correlation

Week 3 · Daily 1.5 hrs

Regression essentials

›Explain R² — what it means and its limitations
›Correlation vs Causation — give a real example
›Simple vs Multiple Regression — when to use which
›Interpret regression coefficients
›Check regression assumptions

Portfolio talking points

›Real project where regression helped make a decision
›Quantify impact: 'R² of 0.85 explains customer churn'
›Mention handling multicollinearity and outliers
›Explain model validation (train/test split)
›Visualize residuals to check assumptions

A/B Testing & Business Application

Week 4 · 4–6 hrs per task

A/B testing framework

›Design an A/B test from business question to analysis
›Calculate required sample size before running test
›Analyze results using appropriate statistical test
›Interpret p-value and confidence intervals
›Make a business recommendation based on results

Standout elements

›Real A/B test example with all steps documented
›Power analysis to justify sample size
›Segmentation analysis (subgroups within test)
›Ethical considerations in experimentation
›Recommendation with clear next steps

Hands-On Projects & Portfolio Ideas

Build these to prove real-world statistics mastery. Each covers multiple topic areas.

P1Beginner 3–4hrs

Employee Salary Analysis

Calculate mean, median, mode, standard deviation; visualize salary distribution; identify outliers.

P2Beginner 4–5hrs

Customer Survey Analysis

Analyze survey responses: descriptive stats, frequency distributions, visualize with bar charts and histograms.

P3Beginner 3–4hrs

Sales Data Exploration

Calculate central tendency and spread; create box plots; identify and handle outliers.

P4Intermediate 6–8hrs

Hypothesis Testing — Product A vs Product B

Formulate H₀/H₁, choose t-test, run analysis, interpret p-value, make business recommendation.

P5Intermediate 6–7hrs

ANOVA — Regional Sales Comparison

Compare sales across 4+ regions, run 1-way ANOVA, post-hoc tests, interpret results.

P6Intermediate 7–9hrs

Chi-Square — Customer Preference by Category

Analyze categorical data: chi-square test of independence, measure association strength.

P7Intermediate 5–6hrs

Correlation Study — Marketing Spend vs Revenue

Calculate Pearson and Spearman correlation, interpret, visualize relationships.

P8Advanced 8–10hrs

Linear Regression — Revenue Prediction

Build multiple linear regression model, check assumptions, interpret coefficients, report R².

P9Advanced 8–10hrs

A/B Test Analysis

Design an A/B test, calculate sample size, analyze results, make data-driven recommendation.

P10Advanced 10–12hrs

Logistic Regression — Churn Prediction

Build a logistic regression model to predict customer churn, interpret odds ratios, evaluate performance.

P11Advanced 12–15hrs

End-to-End Business Analytics

Descriptive + inference + regression + AB testing on a single business problem.

Real-World

Business Scenarios You'll Actually Face

Common asks from actual analyst roles. Know how to solve each end-to-end.

Scenario 1 — 'Sales are down 15% this quarter - is this significant?'

Approach

›Collect sales data from current vs previous quarters
›Check if data meets normality assumptions
›Run appropriate hypothesis test (paired t-test)
›Calculate confidence interval for the difference

Statistical method

›H₀: No difference in sales
›H₁: Sales have decreased
›Paired t-test (same periods year-over-year)
›Interpret p-value, consider business context

Output

›Test statistic and p-value
›Confidence interval for % change
›Statistical significance + practical significance
›Recommendation to investigate further

Scenario 2 — 'Our A/B test shows 5% higher conversion — should we launch?'

A/B test analysis

›Calculate conversion rates for A and B
›Run 2-sample t-test for proportions
›Check if p-value < 0.05
›Calculate confidence interval for lift

Decision factors

›Statistical significance → p-value
›Practical significance → minimum detectable effect
›Sample size adequacy → power
›Cost/benefit analysis of launching

Recommendation

›Launch if statistically and practically significant
›Run for longer if under-powered
›Segment analysis for subgroups
›Monitoring plan after launch

Scenario 3 — 'Which marketing channel drives the most revenue?'

Approach

›Collect spend and revenue by channel
›Run ANOVA to compare revenue across channels
›If significant, run post-hoc tests
›Build regression model to understand drivers

Statistical method

›1-Way ANOVA (channel as factor)
›Tukey HSD for pair comparisons
›Multiple regression with channel variables
›Check for multicollinearity and interaction

Output

›Statistical significance of channel differences
›Revenue per dollar spent by channel
›Recommendation for budget allocation
›Attribution model for multi-touch

Scenario 4 — 'Our defect rate increased — is this just random variation?'

Investigation

›Collect defect rate data pre and post change
›Check if data meets assumptions
›Run appropriate test (chi-square or t-test)
›Calculate practical significance

Statistical method

›Chi-square test for proportion change
›Confidence interval for defect rate difference
›Control chart to monitor variation
›Determine if special cause or common cause

Output

›Statistical significance of increase
›Root cause investigation recommendation
›Process improvement recommendations
›Monitoring plan going forward

Standards

Statistics Best Practices Used at Companies

Standards followed by professional analysts at consulting firms, banks, tech companies, and research orgs.

Know Your Data

›Always start with EDA — understand your data
›Check for missing values and outliers
›Know your data types and scales
›Visualize before you analyze
›Document your assumptions
›Never trust data without validation

Choose the Right Test

›Know your data type (continuous vs categorical)
›Check assumptions before testing
›Consider sample size (power analysis)
›Match test to research question
›Parametric only if assumptions met
›Consider non-parametric alternatives

Interpret Results Responsibly

›Statistical significance ≠ practical significance
›Don't p-hack or cherry-pick results
›Report confidence intervals alongside p-values
›Always discuss limitations
›Connect results to business decisions
›Be honest about uncertainty

Document Everything

›Write down your hypotheses before testing
›Document data sources and cleaning steps
›Record all decisions made during analysis
›Include assumptions and caveats
›Use reproducible code (Jupyter, R Markdown)
›Share methodology with stakeholders

Validate & Replicate

›Run sensitivity analyses
›Cross-validate results on different data
›Test alternative models
›Peer review your analysis
›Validate with business domain experts
›Watch for overfitting and extrapolation

Communicate Clearly

›Lead with the business insight, not p-value
›Use visualizations to tell the story
›Avoid jargon with non-technical audiences
›Explain 'why this matters' for the business
›Be transparent about uncertainty
›Recommend clear next steps

Statistics Mastery Checklist — Data Analyst Roadmap

Priority-Based Topic Checklist

01.Descriptive Statistics — Mean, Median, Mode, Variance, Standard Deviation

02.Data Types & Scales — Qualitative/Quantitative, Nominal, Ordinal, Interval, Ratio

03.Population vs Sample — Sampling Basics

04.Frequency Distributions & Histograms

05.Percentiles, Quartiles & IQR (Interquartile Range)

06.Skewness & Kurtosis — Understanding Distributions

07.Probability Basics — Events, Outcomes, Probability Rules

08.Conditional Probability & Bayes' Theorem

09.Probability Distributions — Normal, Binomial, Poisson, Uniform

10.Central Limit Theorem (CLT) — Why Samples Behave Normally

11.Law of Large Numbers & Sampling Distributions

12.Sampling Techniques — Random, Stratified, Systematic, Cluster

13.Sampling Bias & Sample Size Determination

14.Inferential Statistics Overview — Making Predictions from Samples

15.Confidence Intervals — Estimation & Interpretation

16.Hypothesis Testing — Null (H₀) vs Alternative (H₁)

17.p-Values & Significance Levels (Alpha)

18.Type I & Type II Errors — False Positives vs False Negatives

19.Statistical Power & Effect Size

20.Z-Test — When Population Variance is Known

21.T-Test — When Population Variance is Unknown (1-Sample, 2-Sample, Paired)

22.ANOVA — Analysis of Variance (1-Way & 2-Way)

23.Chi-Square Test — For Categorical Data (Goodness of Fit & Independence)

24.Parametric vs Non-Parametric Tests — Wilcoxon, Mann-Whitney U, Kruskal-Wallis

25.Correlation — Pearson & Spearman

26.Correlation vs Causation — Avoiding a Common Pitfall

27.Linear Regression — Simple & Multiple

28.Regression Assumptions — Linearity, Homoscedasticity, Normality of Residuals

29.Logistic Regression — Binary Classification

30.Time Series Basics — Trends, Seasonality, Stationarity

31.A/B Testing & Experimental Design

32.Bayesian Statistics Basics — Prior, Likelihood, Posterior

Must Know for Every Data Analyst

Descriptive Statistics (Day 1)

Probability & Distributions (Every Analysis)

Inferential Statistics (Every Project)

Regression & Correlation (Predictive Work)

Statistical Tests (Comparing Groups)

Sampling & Data Collection

Probability & Distributions — Deep Dive

Basic Probability Rules

Key Probability Distributions

Central Limit Theorem (CLT)

Hypothesis Testing & Statistical Tests

Hypothesis Testing Framework

Statistical Tests Comparison

Test Assumptions & When to Use

30-60-90 Day Statistics Learning Plan

Foundation & Descriptive Statistics

Inferential Statistics & Tests

Regression, Correlation & Advanced Topics

4-Phase Interview Preparation Roadmap

Foundation — Core Concepts Cold

Hypothesis Testing Mastery

Regression & Correlation

A/B Testing & Business Application

Top Statistics Interview Questions

Descriptive Statistics

Probability & Distributions

Inferential Statistics

Regression & Correlation

Sampling & Experimental Design

Hands-On Projects & Portfolio Ideas

Employee Salary Analysis

Customer Survey Analysis

Sales Data Exploration

Hypothesis Testing — Product A vs Product B

ANOVA — Regional Sales Comparison

Chi-Square — Customer Preference by Category

Correlation Study — Marketing Spend vs Revenue

Linear Regression — Revenue Prediction

A/B Test Analysis

Logistic Regression — Churn Prediction

End-to-End Business Analytics

Business Scenarios You'll Actually Face

Scenario 1 — 'Sales are down 15% this quarter - is this significant?'

Scenario 2 — 'Our A/B test shows 5% higher conversion — should we launch?'

Scenario 3 — 'Which marketing channel drives the most revenue?'