Unclear business framing
Translate a broad request into a measurable question and decision context.
Course 05 · 7 days
Applied Data Scientist
Turn messy business questions into defensible analysis, predictive models, explainable insight, and a production-minded data science capstone.
1 · Learner problem
A model is not useful when the question, data, and decision are disconnected.
Analysts and emerging data scientists need a practical way to explore data, choose methods, explain findings, and carry work toward production.
Analysis without a reliable story
Use EDA, statistics, and feature engineering to understand what the data can support.
Accuracy without explanation
Compare models, interpret results, and communicate limitations with confidence.
End-to-end applied practice
Connect analysis, modeling, GenAI, MLOps, and a capstone deliverable.
2 · Course outcome
Leave with an applied data science workflow you can defend.
Use Python and EDA
Inspect data quality, distributions, relationships, and useful signals.
Apply machine learning
Choose, train, compare, and explain models for a defined question.
Use GenAI responsibly
Explore practical GenAI support for data science while keeping validation human-led.
Plan production data science
Document MLOps, monitoring, limitations, and the capstone decision story.
For data scientists and analysts who want applied depth.
- Data scientists and business analysts
- Python practitioners moving into ML
- Professionals who need to explain model output
- Builders with a business question and a dataset
Bring a question worth investigating.
Python and basic statistics are useful. The course is project-first and expects learners to question data and communicate uncertainty.
4 · Curriculum
Eight modules from Python foundations to capstone.
- Python for Applied Data ScienceUse Python tools and patterns for practical analysis.
- EDA, Statistics & Feature EngineeringUnderstand data quality, patterns, variables, and useful features.
- Applied Machine LearningBuild and compare models for a defined decision problem.
- Advanced Applied ML & Explainable AIInterpret model behavior and communicate limits.
- Generative AI for Data ScientistsUse GenAI for support tasks with validation and responsible boundaries.
- Production Data Science & MLOpsPlan deployment, monitoring, reproducibility, and maintenance.
- End-to-End Applied Data Science CapstonePresent the question, data, analysis, model, and next action.
5 · Live project
Build an end-to-end applied data science capstone.
Use a business dataset to frame a question, explore signals, build a model, explain results, and recommend a next decision.
Decision framing
Define the user, business question, target, measures, and constraints.
Analysis and explanation
Show EDA, features, model comparison, interpretation, and limitations.
Production-minded recommendation
Connect findings to a decision and outline the next MLOps step.
6 · Trainer credentials
View trainer profileApplied AI and data workflows taught together.
MHGrad’s practice covers data science, GenAI, software delivery, analytics, and responsible AI workflows. Verified trainer credentials and references are available during enrolment conversations.
Seven-day live intensive
- Daily guided instruction and analysis time
- Model review and explainability checkpoints
- End-to-end capstone presentation
- Exact dates, timezone, and delivery format confirmed before enrolment
8–10 · Fees and enrolment
Current fee shared on consultation.
Ask about the next intensive, availability, payment steps, or private team delivery. Refund terms are published before payment.
Enquire for current fee
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FAQ and refund terms
Before you enrol.
Do I need advanced statistics?
Basic statistics and Python familiarity help. The course focuses on applied reasoning, model choices, explanation, and a practical capstone.
Can I bring my own data?
Only use data you are authorised to share. A guided non-confidential dataset is available when needed.
Can an analyst take this course?
Yes. The target job includes data scientists and analysts who want a stronger applied ML workflow.
What is the refund policy?
Eligibility, cut-off dates, transfers, and cancellations are stated in the refund policy and enrolment confirmation.
Turn a business question into a defensible data story.
Ask for dates, fee, prerequisites, or a team-training conversation.