Mission
Develop a practical foundation in programming, statistics, data analysis, and machine learning for a pivot into higher-paying technical roles. The sequence moves from core tools to applied analytical and AI capabilities.
Career pivoting into higher-paying fields.
~26 hours · 8 courses
Course 1 · ~180 min
Write, debug, and explain Python programs that process files, transform data, and automate routine tasks.
Python is a core tool across data analysis, automation, software engineering, and AI roles.
Course 2 · ~180 min
Retrieve, combine, filter, and summarize data from multiple relational tables using accurate SQL queries.
SQL is expected in many well-paid data, analytics, operations, and engineering positions.
Course 3 · ~210 min
Summarize datasets, quantify uncertainty, evaluate claims with statistical tests, and distinguish correlation from causation.
Statistical reasoning helps you produce trustworthy analysis and avoid misleading conclusions in technical and business settings.
Course 4 · ~210 min
Clean a messy dataset, calculate meaningful measures, create clear visualizations, and communicate evidence-based findings.
The ability to turn raw data into clear decisions is valuable in analytics, marketing, finance, operations, and product roles.
Course 5 · ~210 min
Train and evaluate basic predictive models, select appropriate metrics, and explain model limitations.
Predictive modeling expands your opportunities beyond reporting into applied analytics and machine learning work.
Course 6 · ~210 min
Build a repeatable machine learning workflow that prepares data, compares models, tracks evaluation results, and documents assumptions.
Employers value reliable modeling processes, not just isolated algorithms or notebook experiments.
Course 7 · ~180 min
Explain how language models work, design structured prompts, use embeddings conceptually, and evaluate AI-generated outputs.
Generative AI literacy is increasingly useful across software, analytics, operations, and emerging AI-enabled roles.
Course 8 · ~150 min
Compare target technical roles, map existing experience to job requirements, identify skill gaps, and explain technical work clearly in interviews.
A career pivot requires more than acquiring skills; you must present your capabilities in language employers recognize.