Data Science, Machine Learning and Gen AI Certification Program

Name: Data Science, Machine Learning and Gen AI Certification Program
Price: 4499 GBP
Availability: InStock
Rating: 4.8 (31 reviews)

Build Python, statistics, SQL, machine learning, deep learning, GenAI, MLOps, and deployment skills through a 12-month portfolio-led programme.

Cohort: Starting Soon

Duration: 12 months

10,000+ Trained

85% Placed

Python, ML, deep learning, GenAI, MLOps, and capstone projects

Learner Reviews

4.8

Based on 31 first-party learner reviews

These ratings come from learner feedback published on this page and are mirrored in the course structured data for search engines.

Career Outcomes

Target Roles

Data Scientist
Machine Learning Engineer
AI Analyst
Data Analyst (Advanced)

Average Salary (UK)

£40,000 – £80,000+

Post-completion

Industry Demand

High demand in AI, fintech, healthcare, SaaS

Is This Right Fit For You?

Graduates targeting data science and machine learning roles

Developers moving into ML, GenAI, and applied AI product work

Analysts upgrading from dashboards to modelling, prediction, and deployment

Anyone searching for a data science course UK with portfolio and capstone focus

Curriculum

The full 48-week Data Science, Machine Learning and GenAI curriculum is expanded below, including phase-by-phase topics, weekly labs, ML projects, GenAI integration, and capstone outcomes.

View detailed curriculum

Overview

Duration12 months

FormatLive + project-based

LevelIntermediate

Course Investment

£4,499

EMI Available

Tools Covered

Python

JUJupyter

NUNumPy

pandas

SQL

Power BI

scikit-learn

TETensorFlow

OpenAI API

GitHub

STStreamlit / Flask

Data science, ML and GenAI certification plan

Data Science, Machine Learning and GenAI Certification

A twelve-month project-led programme covering Python, statistics, SQL, machine learning, deep learning, GenAI, deployment, MLOps, and portfolio presentation for UK data science roles.

48 weeks192 guided hours6 portfolio projectsML, GenAI, MLOps, capstone

Role readiness

Data scientist, ML engineer, advanced analyst, AI analyst, and modelling-focused roles

GenAI-enhanced workflow

Learners use LLMs for coding support, documentation, structured outputs, RAG, and reviewed insight generation

Responsible modelling

Model cards, fairness checks, leakage prevention, data ethics, and human review are part of every major project

Recommended curriculum balance

Build from Python foundations into ML, GenAI, deployment, and capstone proof.

The course spends enough time on statistics and SQL to support evidence-based modelling, then goes deeper into ML workflows, advanced modelling, GenAI, MLOps, and portfolio delivery.

Python and Data Foundations

6 weeks

24 live hours

Programming, notebooks, data handling, and analyst-grade coding habits

Statistics, SQL and EDA

8 weeks

32 live hours

Evidence, querying, exploration, visualisation, and business interpretation

Machine Learning Foundations

12 weeks

48 live hours

Supervised, unsupervised, preprocessing, pipelines, evaluation, and model selection

Advanced ML and Deep Learning

8 weeks

32 live hours

Trees, ensembles, feature engineering, NLP, neural networks, and explainability

GenAI, LLMs and MLOps

8 weeks

32 live hours

Prompting, embeddings, RAG, deployment, monitoring, and responsible AI workflows

Capstone and Career

6 weeks

24 live hours

End-to-end portfolio project, GitHub evidence, interview defence, and career positioning

Detailed syllabus

Five phases from Python to deployed data science products.

Learners progress through data foundations, evidence and analytics, core ML, advanced modelling, GenAI, MLOps, capstone delivery, and interview-ready portfolio storytelling.

Phase 1

Python, Data Foundations and Visualisation

Start with the practical coding and data handling skills needed for notebooks, analysis scripts, visualisation, and reproducible project work.

Python for Data Science

Weeks 1-6

Project: Exploratory Analysis Notebook

Python environment setup, Jupyter notebooks, variables, data types, control flow, functions, and error handling

Lists, dictionaries, tuples, sets, file handling, modules, virtual environments, and clean notebook structure

NumPy arrays, pandas DataFrames, CSV/Excel/JSON I/O, filtering, sorting, grouping, joins, reshaping, and date handling

Matplotlib, Seaborn, chart selection, visual storytelling, notebook commentary, and reproducible analysis habits

GenAI integration

Use GenAI as a coding coach for explanation and debugging while learners still test outputs, document assumptions, and explain code in their own words.

Data Cleaning and Feature Readiness

Embedded in Weeks 4-8

Project: Clean Data Asset

Missing values, duplicates, outliers, type conversion, categorical cleanup, text cleanup, and date/time standardisation

Data dictionaries, cleaning logs, data quality checks, validation rules, and analysis-ready datasets

Feature creation, aggregation levels, leakage awareness, train/test thinking, and reusable transformation functions

Project documentation with source notes, limitations, assumptions, and AI-use declarations

GenAI integration

Use AI to propose cleaning checks, draft data dictionaries, create QA checklists, and explain transformation logic for reviewers.

Phase 2

Statistics, SQL and Business Analytics

Build the evidence layer: query data, explore patterns, understand uncertainty, and explain results in business language.

Statistics and Probability for Data Science

Weeks 7-10

Project: Statistical Decision Report

Descriptive statistics, distributions, spread, percentiles, sampling, bias, confidence intervals, and uncertainty

Hypothesis testing, p-values, A/B tests, correlation vs causation, regression interpretation, and business significance

Probability basics, Bayes thinking, conditional probability, distributions, and practical risk communication

Experiment readouts, recommendation writing, charts, limitations, and stakeholder-facing explanation

GenAI integration

Use AI to translate statistical results into plain English, pressure-test wording, and generate alternative explanations after learners verify calculations.

SQL, EDA and Analytics Storytelling

Weeks 11-14

Project: Analytics Case Study Pack

SELECT, WHERE, GROUP BY, HAVING, CASE, joins, subqueries, CTEs, window functions, and date/string functions

EDA workflow, business questions, slicing and segmentation, cohort-style analysis, and metric validation

Power BI or Python dashboards, visual design principles, stakeholder requirements, and narrative insight writing

Readable SQL style, query documentation, output validation, and reproducible analysis handoff

GenAI integration

Use AI to convert business questions into query plans, explain SQL logic, draft insight summaries, and check analysis narratives for clarity.

Phase 3

Machine Learning Foundations

Learn the modelling workflow from problem framing through preprocessing, training, evaluation, comparison, and responsible recommendation.

Supervised Learning

Weeks 15-22

Project: Prediction Model Benchmark

Problem framing, target definition, baseline models, train/test split, cross-validation, leakage, and metric selection

Regression models, classification models, logistic regression, decision trees, random forests, gradient boosting, and model comparison

Preprocessing, encoding, scaling, imputation, pipelines, hyperparameter tuning, and reusable modelling workflows

MAE, RMSE, ROC-AUC, precision, recall, F1, confusion matrix, calibration, thresholding, and business trade-offs

GenAI integration

Use AI to compare algorithm choices, draft experiment plans, explain metrics, and generate model comparison summaries for non-technical audiences.

Unsupervised Learning and Feature Engineering

Weeks 23-26

Project: Customer or Behaviour Segmentation

Clustering, k-means, hierarchical clustering, dimensionality reduction, PCA, anomaly detection, and similarity thinking

Feature engineering for numeric, categorical, date/time, text, behavioural, and aggregated data

Segment profiling, validation, interpretability, stability checks, and actionable recommendation design

Ethics, bias, proxy variables, fairness concerns, and when not to use machine learning

GenAI integration

Use AI to draft segment personas, explain clusters, create stakeholder summaries, and flag possible bias or misuse scenarios.

Phase 4

Advanced ML, NLP and Deep Learning

Move beyond baseline models into more realistic data science work: ensembles, NLP, neural networks, explainability, and model risk.

Advanced Modelling and Explainability

Weeks 27-30

Project: Explainable ML Report

Advanced feature engineering, model tuning, nested validation, class imbalance, sampling strategies, and error analysis

Ensembles, boosting, model stacking concepts, time-aware validation, model robustness, and performance drift thinking

Feature importance, permutation importance, SHAP-style explanations, partial dependence concepts, and model cards

Communicating risk, limitations, fairness, interpretability, and readiness for pilot use

GenAI integration

Use AI to generate model cards, explain feature trade-offs, prepare stakeholder narratives, and create checklists for responsible model review.

NLP, Deep Learning and Modern AI

Weeks 31-34

Project: Text Classification or NLP Insight Prototype

Text preprocessing, tokenisation concepts, TF-IDF, embeddings, classification, similarity search, and sentiment-style use cases

Neural network foundations, tensors, training loops, overfitting, regularisation, optimisers, and evaluation

Deep learning use cases across tabular, text, images, and time series at a practical literacy level

Transformer concepts, attention, embeddings, foundation models, and where classical ML still beats deep learning

GenAI integration

Use GenAI to explain model behaviour, compare classical NLP with LLM workflows, and summarise text-model outcomes for stakeholders.

Phase 5

GenAI, MLOps, Deployment and Career Acceleration

Finish by building AI-enabled data science products, deploying models, documenting governance, and turning projects into hiring proof.

Applied GenAI for Data Science

Weeks 35-40

Project: LLM Data Science Assistant

Prompt design, structured outputs, function calling, embeddings, retrieval augmented generation, and evaluation

LLM-assisted EDA, feature explanation, SQL or Python helpers, model documentation, and insight generation workflows

Vector search, document Q&A, RAG quality checks, prompt injection risks, redaction, and human review loops

Responsible AI: transparency, data minimisation, security, fairness, accuracy, governance notes, and AI-use declarations

GenAI integration

Learners build an assistant that helps with a controlled data science task while logging data used, checks performed, and risks remaining.

MLOps, Deployment and Capstone

Weeks 41-48

Project: End-to-End Data Science Capstone

Model packaging, APIs, Streamlit or Flask demos, batch scoring, environment variables, dependency files, and reproducible repositories

Model monitoring concepts, drift, retraining triggers, experiment tracking, versioning, testing, and production-readiness checklists

Capstone scoping, data acquisition, cleaning, modelling, evaluation, deployment demo, model card, and executive summary

GitHub portfolio, README writing, architecture diagrams, CV positioning, interview defence, and stakeholder presentation

GenAI integration

Use AI to improve documentation, model cards, demo scripts, interview explanations, and executive summaries while keeping claims evidence-based.

Week-by-week teaching plan

Saturday concepts, Sunday labs, capstone progress every week.

The weekend rhythm keeps the programme manageable across twelve months while moving learners from foundations into a serious end-to-end data science portfolio.

Week

Saturday class

Sunday class

Project milestone

Week 1

Data science roles, workflow, tools, Python setup

Jupyter, variables, data types, notebook hygiene

Start Python notebook

Week 2

Control flow, functions, errors, modules

Lists, dictionaries, file handling, reusable code

Build Python practice notebook

Week 3

NumPy arrays and vectorised thinking

pandas DataFrames, CSV/Excel/JSON I/O

Load first analysis dataset

Week 4

Filtering, grouping, aggregation, joins

Missing values, duplicates, type conversion, dates

Create cleaning log

Week 5

Visualisation with Matplotlib and Seaborn

Chart selection, EDA commentary, insight writing

Build exploratory charts

Week 6

Notebook storytelling and reproducibility

Project review and AI-assisted documentation

Submit Exploratory Analysis Notebook

Week 7

Descriptive statistics, spread, distributions

Sampling, bias, probability, uncertainty

Start statistical report

Week 8

Confidence intervals and experiment thinking

Hypothesis testing, p-values, business significance

Draft test interpretation

Week 9

Correlation, causation, regression interpretation

A/B readout lab and stakeholder memo writing

Add statistical recommendation

Week 10

Probability, Bayes thinking, risk communication

Statistical report QA and presentation

Submit Statistical Decision Report

Week 11

SQL SELECT, WHERE, GROUP BY, HAVING, CASE

Joins, subqueries, CTEs, validation

Start analytics case schema

Week 12

Window functions, date and string functions

Business questions, cohorts, segmentation analysis

Write case-study queries

Week 13

EDA workflow and metric validation

Dashboard or visual story build lab

Create analytics narrative

Week 14

SQL style, documentation, insight presentation

Project review and AI-assisted executive summary

Submit Analytics Case Study Pack

Week 15

ML workflow, problem framing, target definition

Train/test split, leakage, baselines, metrics

Start modelling dataset

Week 16

Preprocessing, imputation, encoding, scaling

Pipelines and cross-validation

Build reusable ML pipeline

Week 17

Linear regression and regularisation concepts

Regression metrics and error analysis

Train first regression benchmark

Week 18

Classification and logistic regression

Confusion matrix, precision, recall, F1

Train first classifier

Week 19

Decision trees and random forests

Feature importance and business explanation

Compare tree-based model

Week 20

Gradient boosting and model tuning

Hyperparameter search and validation strategy

Improve benchmark model

Week 21

Thresholding, calibration, and trade-offs

Model comparison summary for stakeholders

Draft benchmark report

Week 22

Supervised learning project lab

Presentation, feedback, and model QA

Submit Prediction Model Benchmark

Week 23

Clustering, similarity, k-means

Cluster profiling and validation

Start segmentation project

Week 24

Hierarchical clustering and PCA concepts

Dimensionality reduction and visualisation

Add segment exploration

Week 25

Feature engineering for behavioural data

Anomaly detection and stability checks

Profile final segments

Week 26

Bias, proxy variables, ethics, when not to use ML

Recommendation writing and project review

Submit Segmentation Project

Week 27

Advanced feature engineering and imbalance

Sampling strategies and robust validation

Start explainable ML report

Week 28

Ensembles, boosting, stacking concepts

Time-aware validation and model drift thinking

Improve advanced model

Week 29

Explainability: permutation importance, SHAP concepts

Model cards and fairness review

Draft model card

Week 30

Model risk, pilot-readiness, stakeholder narrative

Explainable ML presentation and feedback

Submit Explainable ML Report

Week 31

NLP workflow and text preprocessing

TF-IDF, embeddings, text classification

Start NLP prototype

Week 32

Similarity search and text insight use cases

Evaluation and error analysis for text models

Train text model

Week 33

Neural network foundations and deep learning literacy

Training loops, overfitting, regularisation

Compare neural baseline

Week 34

Transformers, attention, foundation models

NLP prototype review and AI-assisted summary

Submit NLP Insight Prototype

Week 35

GenAI for data science workflows

Prompt design, structured outputs, safe data handling

Start LLM assistant

Week 36

Function calling and data science tool workflows

AI helpers for SQL, Python, metrics, and model docs

Add tool workflow

Week 37

Embeddings and vector search

RAG architecture and document Q&A

Add retrieval workflow

Week 38

RAG evaluation, citations, prompt injection risks

Human review, confidence, refusal, and escalation

Evaluate LLM assistant

Week 39

Responsible AI governance and AI-use declarations

Security, fairness, accuracy, transparency checklist

Draft governance note

Week 40

LLM assistant demo and project review

Presentation, feedback, and risk review

Submit LLM Data Science Assistant

Week 41

MLOps foundations, packaging, dependency files

APIs, batch scoring, Streamlit or Flask demos

Start capstone repository

Week 42

Model versioning and experiment tracking concepts

Testing, validation, and reproducibility checks

Build capstone pipeline

Week 43

Monitoring concepts, drift, retraining triggers

Production-readiness checklist

Add monitoring plan

Week 44

Capstone scoping and data acquisition review

Cleaning, EDA, feature engineering lab

Prepare capstone dataset

Week 45

Capstone modelling and benchmark comparison

Model tuning, explainability, and risk review

Choose final capstone model

Week 46

Deployment demo build lab

Model card, executive summary, and GitHub README

Package capstone demo

Week 47

Portfolio storytelling and interview defence

CV, LinkedIn, GitHub optimisation

Prepare final presentation

Week 48

Capstone demo day

Mock interview, feedback, and next-step plan

Submit End-to-End Data Science Capstone

Portfolio outcomes

Six projects that show complete data science delivery.

The programme produces evidence across EDA, statistics, supervised ML, unsupervised ML, explainability, GenAI, deployment, and a final capstone.

Exploratory Analysis Notebook

Analyse raw data clearly and communicate early insights

Clean notebook, data dictionary, charts, findings, assumptions, and AI-use notes

Prediction Model Benchmark

Frame and evaluate supervised ML problems responsibly

Pipeline, baseline models, metrics table, error analysis, and stakeholder summary

Customer or Behaviour Segmentation

Use unsupervised learning to create actionable segments

Feature table, clustering notebook, segment profiles, validation notes, and recommendations

Explainable ML Report

Explain model behaviour, risks, and limitations to stakeholders

Advanced model, feature explanations, model card, fairness notes, and pilot-readiness view

LLM Data Science Assistant

Use GenAI inside data science workflows with controls

Notebook or app, prompt templates, structured outputs, RAG or tool workflow, and governance note

End-to-End Data Science Capstone

Show full data science delivery from problem to model to demo

GitHub repo, README, deployed demo or walkthrough, model card, slides, and interview defence

Required submission pattern

Business problem

Data source and ethics note

Cleaning and EDA log

Feature strategy

Model experiments

Evaluation and trade-offs

Deployment or demo

Model card and AI-use declaration

Model from evidence

Learners start every model with a business question, data quality review, baseline, metric choice, and clear trade-off explanation.

Explain, not just predict

Every major ML project includes model interpretation, error analysis, fairness notes, limitations, and stakeholder-ready language.

Ship portfolio proof

Capstone work is packaged with GitHub documentation, model cards, demo artefacts, and an interview-style project defence.

Simple course promise

Take learners from Python and statistics into real machine learning, GenAI-enabled workflows, deployment, and a defensible capstone that proves they can think, build, explain, and improve models responsibly.

Career guides

Read before choosing this programme

These Brit Institute guides explain the UK roles, salaries, tools, and learning path connected to this course.

How to Become

How to Start a Career in Data Science in the UK

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Read guide →Career Comparisons

Data Analyst vs Data Scientist: Which Career is Right in the UK?

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Hands-On Projects

Exploratory Analysis Notebook
Prediction Model Benchmark
Customer or Behaviour Segmentation
Explainable ML Report
LLM Data Science Assistant
End-to-End Data Science Capstone

Career Support

Data science CV and LinkedIn positioning
ML, statistics, and project interview prep
GitHub portfolio and capstone review

Learner Reviews

4.8 / 5 average from 31 published learner reviews

Sophie Williams

Published 5 Mar 2026

4.9 / 5

The machine learning modules were challenging in the right way. I appreciated that every concept was tied back to a business problem instead of staying theoretical.

Daniel Mensah

Published 18 Jan 2026

4.8 / 5

My capstone project became the strongest part of my portfolio. The team also pushed me to explain model choices clearly, which helped during interviews.

Ravi Patel

Published 27 Nov 2025

4.7 / 5

The programme gave me structure across Python, statistics, and deployment. It felt like a serious path for moving from analytics into data science work.

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