Microsoft Team Data Science Process Implementation Training Course

Course overview

Data science projects often stall between a promising model and an operational decision tool. Teams may begin with untested business assumptions, work from inconsistent data extracts, lose track of experiments, or hand over notebooks that cannot be reproduced or deployed. Microsoft Team Data Science Process (TDSP) provides a structured, collaborative lifecycle for turning a business problem into a governed data product, with defined artefacts, decision points and responsibilities at every stage.

This five-day course teaches participants to implement TDSP across business understanding, data acquisition and understanding, modelling, deployment, and customer acceptance. Participants learn to frame analytical objectives as measurable success criteria; create project charters and data dictionaries; profile and prepare data; manage feature engineering and model experiments; evaluate models against business and technical measures; and plan production deployment, monitoring and acceptance. The course connects these practices to Azure Machine Learning, Git, Azure DevOps and Power BI so that work remains traceable from source data to stakeholder-facing output.

Delivery combines concise instructor demonstrations with a running business case, guided team workshops and practical tool-based exercises. Each participant works through the lifecycle of a predictive analytics initiative, documenting decisions and producing the artefacts needed for a repeatable delivery process. They leave with a completed TDSP project pack: a business problem definition, data acquisition plan, data dictionary, experiment record, model evaluation summary, deployment plan, acceptance criteria and implementation roadmap that can be adapted for a live organisational project.

The course is designed for analysts, data scientists, technical leads, product owners and managers who need a common operating model for delivering data science work. It is particularly valuable where teams are moving from exploratory analysis to reliable, reviewable and deployable analytics solutions.

Course objectives

By the end of this course, participants will be able to:

• Define a TDSP project charter with business objectives, hypotheses, stakeholders and measurable success criteria
• Map the TDSP lifecycle stages, gates, roles and required project artefacts for a data science initiative
• Create a data acquisition plan and data dictionary that document sources, ownership, quality and access constraints
• Profile and prepare a dataset using repeatable data-quality checks, transformations and feature-engineering steps
• Track model experiments in Azure Machine Learning with parameters, metrics, versions and reproducible runs
• Evaluate candidate models using technical performance measures, business thresholds and error-analysis evidence
• Design a deployment and monitoring plan covering model registration, release controls, drift indicators and rollback actions
• Produce a TDSP implementation roadmap and customer-acceptance pack for an organisational use case

Benefits of attending

Target competencies

Who should attend

• Data Scientists — who need a repeatable lifecycle for moving models from exploration into production
• Data Analysts — who are expanding from reporting into predictive analytics and structured data science delivery
• Machine Learning Engineers — who must make experimentation, model registration and deployment traceable
• Analytics Managers — who need consistent governance, stage gates and evidence for data science investment decisions
• Product Owners — who translate operational needs into measurable data product requirements
• Data and Digital Transformation Leads — who are establishing cross-functional standards for analytics delivery

Requirements and prerequisites

Participants should be comfortable working with tabular data and understand basic descriptive statistics, such as distributions, averages, missing values and correlations. Familiarity with Python or R is helpful for interpreting data preparation and modelling examples, but advanced programming is not required. Participants should also understand the purpose of machine learning models and be able to discuss a business process in measurable terms. Access to an organisational Azure environment is not required for attendance; guided examples can be followed in a training environment. Complete beginners should expect an intensive introduction to data science delivery rather than a course on coding algorithms from first principles.

Training methodology

The course uses a single end-to-end business case so participants see how each TDSP stage produces inputs for the next. Instructor-led sessions introduce lifecycle decisions, templates and Azure-based practices; guided labs apply them to data profiling, experiment tracking and model evaluation. Small-group workshops test assumptions, define acceptance criteria and review deployment risks from business, technical and operational perspectives. Daily outputs are added to a project pack, and the final session converts that pack into a practical implementation plan for a participant’s own team or use case.

Course outline

Day 1: TDSP foundations and business understanding

• Microsoft Team Data Science Process lifecycle and stage-gate structure
• Data science roles, responsibilities and collaboration model
• Business problem framing versus solution-led model selection
• Project charter components and decision-rights definition
• Analytical hypotheses and measurable business outcomes
• Success criteria, baseline measures and value assumptions
• Stakeholder mapping and customer acceptance expectations

Workshop: Participants turn a loosely defined operational problem into a TDSP project charter with stakeholders, hypotheses, scope boundaries and measurable success criteria.

Day 2: Data acquisition, understanding and preparation

• Data acquisition planning and source-system assessment
• Data ownership, access approvals and privacy constraints
• Data dictionaries, lineage records and metadata capture
• Exploratory data analysis for distributions, outliers and missingness
• Data-quality rules and remediation decisions
• Repeatable transformation pipelines and feature definitions
• Training, validation and test dataset partitioning

Workshop: Using a case-study dataset, participants produce a data acquisition plan, data dictionary, quality assessment and initial feature preparation specification.

Day 3: Modelling and experiment management

• Model-selection strategy aligned to problem type and constraints
• Baseline models and benchmark performance measures
• Feature engineering hypotheses and leakage prevention
• Azure Machine Learning workspace and experiment concepts
• Run tracking for parameters, datasets, metrics and outputs
• Git branching and version control for notebooks and code
• Reproducibility controls for environments and model artefacts

Workshop: Participants configure an experiment record for competing model approaches and create a reproducibility checklist covering code, data, parameters and environment.

Day 4: Evaluation, deployment and operational control

• Technical evaluation metrics for classification and regression models
• Business threshold setting and cost-of-error analysis
• Error analysis across segments and edge cases
• Model registration, versioning and approval checkpoints
• Batch, real-time and embedded deployment patterns
• Monitoring plans for performance, drift and data-quality change
• Release controls, rollback procedures and incident ownership

Workshop: Participants evaluate model results against business thresholds and produce a deployment, monitoring and rollback plan for the case-study solution.

Day 5: Customer acceptance and TDSP implementation

• Customer acceptance criteria and evidence requirements
• User acceptance testing for data products
• Power BI reporting for model outcomes and operational monitoring
• Azure DevOps work items, backlogs and delivery traceability
• Governance reviews, risk registers and project stage gates
• Operating-model choices for cross-functional data science teams
• TDSP adoption roadmap and template tailoring

Workshop: Participants present their completed TDSP project pack to a simulated steering group and create a 90-day implementation roadmap for applying TDSP in their organisation.

Tools & standards covered

Azure Machine Learning, Azure DevOps, Git, Power BI

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions