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Data Analyst
103 topics across 7 chapters
Chapter 1
Foundations
1
Spreadsheets for analysis (Excel/Google Sheets)
3 subtopics
2
Spreadsheet formulas & functions (core set)
3
Pivot tables and summarization
4
Cleaning data in spreadsheets
5
Programming mindset
2 subtopics
6
Core scripting concepts (variables, loops, functions)
7
Debugging & problem decomposition
8
Data literacy & ethics
2 subtopics
9
Data types, granularity, and measurement levels
10
Privacy, bias, and responsible data use
Chapter 2
Tools & Workflow
11
SQL tools & environments
2 subtopics
12
Set up and use a SQL client (connect, run queries, export results)
13
SQL style: formatting, aliasing, commenting, and readability
14
SQL querying (core)
5 subtopics
15
SELECT, filtering, and aggregation
2 subtopics
16
GROUP BY, HAVING, and common aggregations (count/distinct, avg, etc.)
17
Date/time functions and time-based grouping (daily/weekly/monthly)
18
Joins
1 subtopics
19
Join types practice (inner/left/full) + handling duplicates
20
Window functions
21
CTEs & subqueries
22
Query performance basics (indexes, explain plans, avoiding pitfalls)
23
Python analysis stack
3 subtopics
24
Python fundamentals
2 subtopics
25
Notebooks and environments (Jupyter, venv/conda, requirements)
26
Core Python syntax & data structures (strings, lists, dicts, functions)
27
pandas for data wrangling
2 subtopics
28
DataFrame basics (load, inspect, select, filter, groupby)
29
Cleaning with pandas (missing values, types, duplicates, merges)
30
Python visualization basics
1 subtopics
31
Make basic charts with matplotlib/seaborn (bar/line/hist/box)
32
Version control & reproducible workflow
2 subtopics
33
Git basics (commit, branch, merge, PRs)
34
Project structure & README writing (reproducible deliverables)
35
BI tools (Tableau/Power BI) basics
2 subtopics
36
Navigate the BI tool (fields, measures, visuals, filters)
37
Connect to data sources (CSV, database, published datasets) and refresh
Chapter 3
Data Management & SQL
38
Relational data modeling
2 subtopics
39
Primary keys, foreign keys, and relationship types
40
Dimensional modeling: facts, dimensions, and star schema basics
↗ SQL querying (core) (see Chapter 2)
41
Data quality & documentation
2 subtopics
42
Data validation checks (nulls, uniqueness, ranges, freshness)
43
Data dictionaries, definitions, and lineage (what does this metric mean?)
44
Warehouses & pipelines (concepts)
2 subtopics
45
ETL vs ELT and where transformations should live
46
Analytics engineering basics (models, tests, and documentation concepts)
Chapter 4
Statistics & Analytics
47
Descriptive statistics
2 subtopics
48
Central tendency and dispersion (mean/median/variance/std)
49
Distributions, percentiles, and outliers (IQR, z-score intuition)
50
Probability & inference
2 subtopics
51
Hypothesis tests and p-values (how to interpret, not just compute)
52
Confidence intervals and uncertainty communication
53
Experimentation & A/B testing
2 subtopics
54
Sample size, power basics, and effect sizes (conceptual)
55
Experiment pitfalls (seasonality, novelty effects, peeking, Simpson’s paradox)
56
Time series & forecasting (intro)
1 subtopics
57
Trend/seasonality basics (moving averages, decomposition intuition)
58
KPI design & metrics
1 subtopics
59
Metric trees and North Star metric thinking
Chapter 5
Visualization & Storytelling
60
Data visualization principles
2 subtopics
61
Color, accessibility, and preattentive attributes
62
Avoid misleading visuals (scales, truncation, dual axes, cherry-picking)
63
Dashboard design
2 subtopics
64
Dashboard layout patterns (KPI strip, drill-down, filters, annotations)
65
Dashboard performance & refresh (extracts, aggregations, limits)
66
Chart selection practice (choose the right chart and justify it)
67
Storytelling with data
2 subtopics
68
Write executive summaries (context → insight → impact)
69
Recommendations and next steps (what should change and why)
Chapter 6
Business & Communication
70
Business/domain basics
1 subtopics
71
Pick a domain and learn its core KPIs (e.g., SaaS, e-commerce, marketing)
72
Requirements & scoping
1 subtopics
73
Write problem statements & success criteria (what decision will this support?)
74
Communication & influence
2 subtopics
75
Stakeholder mapping and expectation setting
76
Concise writing (status updates, analysis docs, decision memos)
77
Decision-making & ROI
1 subtopics
78
Impact estimation (cost/benefit, opportunity sizing, confidence levels)
Chapter 7
Portfolio & Career
79
Portfolio projects
4 subtopics
80
Project: SQL analysis case study (question → query → insights → write-up)
81
Project: dashboard + narrative (KPI dashboard + 1-page readout)
82
Project: Python/pandas EDA report (cleaning, visuals, findings)
83
Capstone: end-to-end analytics (brief → data → analysis → recommendations deck)
84
Interview preparation
3 subtopics
85
SQL interview drills (joins, windows, CTEs, edge cases)
86
Analytics case interviews (frameworks, assumptions, metric tradeoffs)
87
Take-home assignments workflow (scoping, cleaning, QA, narrative)
88
Resume & online presence
2 subtopics
89
Resume bullets with metrics (action + method + impact)
90
LinkedIn + portfolio site checklist (projects, visuals, links, credibility)
91
Job search strategy
1 subtopics
92
Applications strategy (target roles, tracking, referrals, follow-ups)
93
On-the-job growth
2 subtopics
94
Networking and community (meetups, online groups, informational interviews)
95
Mentorship and feedback loops (code reviews, analysis reviews, retrospectives)
↗ Git basics (commit, branch, merge, PRs) (see Chapter 2)

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