Data Science & ML
Data scientists bridge technical research with business impact. This path builds the English for reading papers, presenting model results, discussing data quality issues, and communicating uncertainty to stakeholders.
Topics covered
- ML paper English
- Model training vocabulary
- Data pipeline language
- Statistical terms
- Presenting results
Vocabulary spotlight
4 terms every Data Science & ML should know in English:
When a model learns training data too well and fails to generalise to new data
"Validation loss rising while training loss falls is a clear sign of overfitting."
A simple reference model used to benchmark more complex approaches
"Our model outperforms the baseline by 8% on the held-out test set."
When information from outside the training set is used to build the model
"The suspiciously high accuracy was caused by data leakage in the feature pipeline."
An experiment removing individual components to measure their contribution
"The ablation study shows that attention is not contributing to accuracy."
📚 Vocabulary Reference
Key terms organised by category for Data Science & MLs:
Data Fundamentals
ML Concepts
Model Types
Evaluation
Tools & Ecosystem
Recommended exercises
Real-world scenarios you'll practise
- Explaining model performance metrics to a non-technical PM
- Writing the findings section of an ML experiment report
- Presenting a data pipeline architecture to engineering
- Discussing dataset bias with stakeholders
Recommended reading
Frequently Asked Questions
What English skills do Data Science & MLs most need to improve?+
Data Science & MLs most commonly need to improve: technical vocabulary (the correct English terms for domain concepts), collocation accuracy (using the right verb for each action), written communication (bug reports, PR descriptions, technical docs), and spoken communication for standups, code reviews, and stakeholder meetings.
How long does the Data Science & ML learning path take?+
The Data Science & ML learning path contains 20–40 hours of material studied comprehensively. Most learners focus on the highest-priority modules first and return to the rest over time. Spending 30 minutes per day for 4–6 weeks produces noticeable improvement in workplace English.
What vocabulary should a Data Science & ML prioritise first?+
Start with the vocabulary that appears most in your daily work — terms you read in documentation, use in commit messages, and hear in meetings. The Data Science & ML path begins with the most frequent vocabulary clusters before moving to advanced communication patterns.
Are there interview exercises for Data Science & ML roles?+
Yes. The Data Science & ML path includes role-specific interview question modules with model answers and key phrases — the actual questions interviewers ask and the vocabulary needed to answer them fluently. There is also a dedicated Interview Practice hub for general interview skills.
Does this path include pronunciation help?+
Yes. The path links to pronunciation exercises for the technical terms most commonly mispronounced in this domain. The Pronunciation hub includes drills for acronyms, silent letters, word stress, and minimal pairs — all in IT context.
What are the most common English mistakes Data Science & MLs make?+
The most common mistakes: incorrect collocations (using the wrong verb with a technical noun), false friends from L1, tense errors when narrating past incidents or walkthroughs, and using overly formal or overly casual register in written communication.
How do I improve my English for code reviews?+
Learn the standard code review collocations: approve a PR, request changes, leave a nit, address feedback, block a merge, resolve a conversation. Use hedging language for suggestions: "This might be cleaner as…", "Have you considered…?". The Collocations section includes a dedicated Code Review set.
Can I use this path alongside my daily work?+
Yes — the path is designed for working professionals. Each exercise set takes 10–15 minutes. The most effective approach is to study a vocabulary module before a meeting or task where you'll use that vocabulary, then practise immediately after. Context-linked practice produces much faster retention.
Is the content free?+
Yes, completely free. No registration required, no payment, no time limit. All vocabulary modules, exercises, glossary entries, and learning path guides are open access.
How do I track my progress through this path?+
Progress is tracked in your browser's local storage — completed exercise sets are marked with a checkmark when you return. No account is needed. You can bookmark specific modules and use the exercises overview to see which sets you've completed.