144 focused days
A 144-day professional data analytics path: investigate data from Day 1, build strong Python foundations, master tabular analysis, spreadsheets, SQL and BI, reason about uncertainty and experiments, analyze time without predictive modeling, govern metrics, and communicate decision-ready work.
EDA from Day 1, then reliable programs and reproducible workflows
Ask a data question on Day 1 while learning the Python needed to answer it
Run a reproducible Python analysis, inspect a small dataset, and distinguish a useful pattern from a quality problem.
Build reliable analytical utilities instead of notebook fragments
Use collections, functions, modules, and tests to turn repeated analysis into understandable programs.
Make analytical programs survive imperfect inputs and handoffs
Design file workflows with explicit errors, configuration, domain objects, logs, and reproducible environments.
NumPy, Pandas, cleaning, visualization, and auditable Excel
Move from Python records to vectorized, auditable tables
Use arrays and DataFrames with deliberate shapes, dtypes, indexes, selections, transformations, and grouped summaries.
Turn messy tables into trustworthy patterns without hiding uncertainty
Profile, clean, reshape, visualize, and document real data while protecting meaning and grain.
Use spreadsheets as controlled analytical systems
Build reviewable Excel models with tables, formulas, pivots, controls, charts, and reconciliation checks.
SQL, analytical models, statistics, sampling, and experiments
Ask precise questions of relational data and prove result grain
Write readable SQL that filters, aggregates, joins, and reconciles without changing the intended unit.
Express sequences, cohorts, windows, and governed metric-ready tables
Build reusable SQL layers using windows, dimensional models, cohorts, and performance-aware design.
Quantify variation before making population claims
Describe distributions, reason with probability, design samples, and communicate representativeness limits.
Estimate effects without turning thresholds into truth machines
Use intervals, tests, power, and A/B design with assumptions, practical significance, and guardrails.
Tableau, BI, time analysis, governed metrics, and professional communication
Deliver governed decision products and analyze time without ML forecasting
Create trustworthy dashboards, define metrics, and perform decomposition and baseline time-series analysis.
Frame ambiguity, influence decisions, and prove readiness
Own analysis from stakeholder question through quality review, communication, portfolio evidence, and interview defense.