14-Day English Crash Course for Data Scientists & ML Engineers
Intensive Sprint

This crash course is for data scientists and ML engineers who need focused, fast improvement in technical English — before a new role, a technical interview, or when presenting findings to English-speaking stakeholders. It covers the 14 highest-priority vocabulary and communication areas for data science and ML engineering work.

The course is designed for B1–B2 English learners (intermediate). You should be able to hold basic conversations in English. The course improves your professional and technical English, not general English from scratch. If you are unsure of your level, try Day 1 — if the vocabulary feels completely unfamiliar, build general English skills first.

Each day is designed for 20–30 minutes: roughly 10 minutes on vocabulary and 15 minutes on the exercise. The intensive format keeps sessions focused — every day is tied directly to vocabulary and scenarios you encounter in data science and ML work.

The course covers core ML vocabulary (model, feature, training, inference), neural networks and deep learning terms, statistics and mathematics vocabulary in English, probability and distribution language, data pipeline terminology, ETL and data quality language, experiment design vocabulary, hypothesis testing language, model evaluation terms, bias/variance/overfitting language, stakeholder communication phrases, and interview and salary negotiation language.

Yes. Days 3 and 4 focus on statistics and mathematics vocabulary in English — including distribution, variance, standard deviation, hypothesis, p-value, confidence interval, and the natural phrases used when discussing statistical results with colleagues and stakeholders who may not have a statistics background.

Yes. Days 7 and 8 cover experiment design and hypothesis testing vocabulary — the language used when designing A/B tests, setting up treatment and control groups, discussing statistical significance, and presenting experiment results to product and business stakeholders.

Yes. Days 11 and 12 focus on stakeholder communication and presenting data findings in English — the phrases used when explaining model performance to non-technical audiences, communicating data limitations, and presenting charts and dashboards in English during business reviews and sprint demos.

The 14-day crash course covers the 14 highest-priority areas in a condensed format. The 30-day path goes deeper — adding MLOps vocabulary, feature engineering language, model deployment terminology, data governance vocabulary, advanced presentation skills, and a full week of career preparation including technical writing and leadership communication.

Yes. Days 13 and 14 focus on ML and data science technical interview speaking and salary negotiation phrases — covering model explanation language, case study discussion vocabulary, and the phrases used when discussing experience and compensation at data science job interviews. See /exercises/speaking/technical-interview-speaking/.

After the crash course, move to the 30-day data science path for deeper coverage of MLOps, feature engineering, data governance, and advanced communication. You can also browse the full exercise library at /exercises/ or explore the DevOps 14-day path if your role involves model deployment infrastructure.