Data Analyst
Learn to clean, query, analyze, and communicate data for confident business decisions.
Curriculum
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Phase 1Work with Everyday Data
Phase 2Query Relational Data
Phase 3Analyze with Sound Methods
Phase 4Communicate Decisions
Excel Formulas, Lookups and Pivot Tables
Typed structured tables
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Lookups and anchored formulas
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Pivot tables and charts
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Data Cleaning, Validation and Missing Values
Missing and duplicate profiles
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Valid ranges and keys
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Repeatable cleaning rules
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KPIs, Business Metrics and Requirements
Population and event definitions
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Numerator, denominator, and grain
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Baselines and guardrails
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SQL SELECT, WHERE, GROUP BY and Aggregates
Filters and nulls
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Grouping and conditional totals
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Dates and row checks
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SQL JOINs and Relational Modeling
Table grain and cardinality
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Join types and unmatched rows
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Fan-out reconciliation
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CTEs, Window Functions and Cohort Analysis
CTEs and intermediate grains
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Window partitions and frames
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Cohort retention calculations
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Descriptive Statistics, Sampling and Regression
Distributions and robust summaries
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Sampling and confidence intervals
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Regression and confounding
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pandas, NumPy and Jupyter
Explicit import types
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Merge and group checks
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Rerunnable analysis steps
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A/B Testing and Causal Reasoning
Randomization and sample size
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Primary metric and guardrails
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Effect size and uncertainty
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Matplotlib and Seaborn Data Visualization
Chart choice by question
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Honest scales and labels
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Uncertainty and accessible color
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Power BI or Tableau
KPI hierarchy and scope
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Consistent filters and definitions
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Freshness and drilldowns
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Analytics Portfolio Case Study
Decision and data scope
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Validated reproducible analysis
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Recommendation with limits
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