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Professional Data Analyst — From raw data to decision-ready insights

A complete professional pathway for turning raw data into reliable analysis, useful metrics and recommendations that business teams can act on.

A Data Analyst turns data from multiple sources into reliable information that helps an organization understand what is happening, measure performance and make better decisions. The role involves framing business questions, checking data quality, building analyses and metrics, and communicating results clearly to decision-makers.

This pathway teaches the full workflow: decision framing, data quality and preparation, Excel, SQL, Power Query, analytical modeling, Power BI, decision-oriented statistics, Python/pandas, APIs and automation. You will also learn to test and reconcile results, document analyses, govern metrics and defend professional recommendations with evidence.

metiers_prof_data_analyst_outcome

15 courses • 150 interactive lessons • 45 to 75 hours of conversational learning

5 levels: Level 1 — 24,90 € excl. VAT Level 2 — 29,90 € excl. VAT Level 3 — 34,90 € excl. VAT Level 4 — 39,90 € excl. VAT Level 5 — 45,00 € excl. VAT

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Best offer

Pack Complet — Data Analyst

🎓 Certificate : "Data Analyst professionnel" 523,80 €

📚 15 courses 📖 150 lessons ⏱ 45 to 75 h ⚡ 3000 credits included

399,00 € excl. VAT

Savings : 124,80 €

Analytical Foundations Pack 3 courses Level 1

🎓 Certificate : Data Analyst — Analytical Foundations

📚 3 courses 📖 30 lessons ⏱ 9 to 15 h ⚡ 480 credits included

74,70 €

64,90 excl. VAT

Savings : 9,80 €

01 Level 1

Understand the Data Analyst role and frame a decision

Adopt a professional Data Analyst mindset and turn a business request into a measurable analytical question.

Understand the Data Analyst role in the organization and how it works with business teams Define the respective responsibilities of business teams, the Data Analyst, Data Engineer, Data Scientist, and data governance Run a business scoping interview: decision to support, users, metrics, scope, constraints, and a measurable analytical question Distinguish the stated request, the underlying problem, and the decision that needs support Define the population, time period, scope, and explicit exclusions Turn a broad business question into an observable, testable analytical question Select relevant indicators and distinguish metrics, KPIs, targets, and alert thresholds Build an analysis plan linking the question, data, method, controls, and deliverable Write an analytical brief and define its acceptance criteria Professional workshop: scope an analysis end to end and obtain validation of the analytical question

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 160 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

No advanced prerequisite. Everyday computer skills and an interest in data analysis or problem solving are sufficient to start.

Learning objectives

Understand the Data Analyst role in the organization and how it works with business teams
Distinguish the stated request, the underlying problem, and the decision that needs support
Select relevant indicators and distinguish metrics, KPIs, targets, and alert thresholds
Professional workshop: scope an analysis end to end and obtain validation of the analytical question

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: scope an analysis end to end and obtain validation of the analytical question
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

24,90 € excl. VAT

02 Level 1

Understand, structure and assess data

Read a dataset’s structure, understand its origin and assess its quality before starting any analysis.

Understand the data lifecycle: collection, storage, transformation, use, and retention Identify dimensions, measures, identifiers, dates, and the grain of a dataset Distinguish business identifiers, technical keys, and composite keys Determine a table’s grain and detect changes in granularity Build a data dictionary that business and data teams can use Assess data quality: completeness, accuracy, consistency, freshness, and uniqueness Profile a dataset and prioritize anomalies Clean a dataset: duplicates, missing values, formats, and outliers Document an analysis: sources, assumptions, transformations, limitations, and definitions Professional workshop: audit a deliberately inconsistent customer-and-orders file

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 160 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

No advanced prerequisite. Everyday computer skills and an interest in data analysis or problem solving are sufficient to start.

Learning objectives

Understand the data lifecycle: collection, storage, transformation, use, and retention
Determine a table’s grain and detect changes in granularity
Profile a dataset and prioritize anomalies
Professional workshop: audit a deliberately inconsistent customer-and-orders file

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: audit a deliberately inconsistent customer-and-orders file
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

24,90 € excl. VAT

03 Level 1

Analyze with Excel, descriptive statistics and visualization

Explore a dataset, validate calculations and communicate an initial conclusion using Excel and appropriate visualizations.

Turn an Excel range into an analysis-ready table and validate data types Use core Excel calculations to check and explore data Use logical, conditional, and error-handling functions Use lookup functions to match and reconcile data Build PivotTables and validate the resulting aggregates Use descriptive statistics: mean, median, dispersion, quantiles, and distributions Choose and design the right visualization: key message, visual hierarchy, comparisons, scales, and chart bias Detect a misleading visualization and correct its scale or encoding Write a concise analysis note: finding, evidence, impact, limitation, and recommendation Professional workshop: produce an initial analysis note from a raw file

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 160 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

No advanced prerequisite. Everyday computer skills and an interest in data analysis or problem solving are sufficient to start.

Learning objectives

Turn an Excel range into an analysis-ready table and validate data types
Use lookup functions to match and reconcile data
Choose and design the right visualization: key message, visual hierarchy, comparisons, scales, and chart bias
Professional workshop: produce an initial analysis note from a raw file

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: produce an initial analysis note from a raw file
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

24,90 € excl. VAT

Operational Analytics Pack 3 courses Level 2

🎓 Certificate : Data Analyst — Operational Analytics

📚 3 courses 📖 30 lessons ⏱ 9 to 15 h ⚡ 540 credits included

89,70 €

76,90 excl. VAT

Savings : 12,80 €

04 Level 2

Query a relational database with SQL

Understand the relational model and write reliable SQL queries to filter, transform and aggregate data.

Understand tables, rows, columns, keys, and relationships in a relational database Read a relational schema and identify the tables needed to answer a business question Write your first SQL queries with SELECT, WHERE, ORDER BY, and LIMIT Combine filters, logical operators, lists, and ranges Build aggregates with COUNT, SUM, AVG, MIN, MAX, and GROUP BY Use GROUP BY and HAVING to filter grouped results Handle NULL correctly and distinguish missing values, zero, and empty strings Use CASE WHEN, text and date functions, and type conversions in SQL Validate a query with row counts, test cases, and reasonableness checks Professional workshop: build and validate a SQL sales report

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 180 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

A working knowledge of the foundations introduced in the first courses, or equivalent practical experience, is recommended.

Learning objectives

Understand tables, rows, columns, keys, and relationships in a relational database
Combine filters, logical operators, lists, and ranges
Handle NULL correctly and distinguish missing values, zero, and empty strings
Professional workshop: build and validate a SQL sales report

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: build and validate a SQL sales report
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

29,90 € excl. VAT

05 Level 2

Master joins and build maintainable SQL queries

Combine multiple tables without double counting and structure complex SQL logic so it remains readable and maintainable.

Understand what joins do and choose the right starting table Join multiple tables with INNER JOIN and LEFT JOIN while managing duplication risks Validate key integrity before joining two tables Interpret one-to-one, one-to-many, and many-to-many cardinalities Check granularity before and after a join Detect double counting caused by a join Use subqueries and CTEs to make complex SQL readable and maintainable Use SQL window functions for rankings, running totals, comparisons, and change over time Reconcile join results with source totals Professional workshop: build a multi-table customer-product analysis without duplication

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 180 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

A working knowledge of the foundations introduced in the first courses, or equivalent practical experience, is recommended.

Learning objectives

Understand what joins do and choose the right starting table
Interpret one-to-one, one-to-many, and many-to-many cardinalities
Use subqueries and CTEs to make complex SQL readable and maintainable
Professional workshop: build a multi-table customer-product analysis without duplication

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: build a multi-table customer-product analysis without duplication
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

29,90 € excl. VAT

06 Level 2

Prepare multi-source data with Power Query

Build a reproducible preparation workflow from files, databases and analytical data warehouses.

Import and profile data from files, relational databases, and cloud data warehouses such as BigQuery or Snowflake using Power Query or another suitable connector Choose a connector and manage access parameters without exposing secrets Use Power Query profiling to detect errors, blanks, and distributions Build repeatable data-cleaning rules instead of relying on manual fixes Set data types and locale settings without corrupting dates or numbers Handle conversion errors and isolate rejected rows Merge and append queries while validating matches Trace transformation steps and their dependencies Build a recurring analysis with data-quality controls before publication Professional workshop: consolidate omnichannel sales from three different sources

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 180 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

A working knowledge of the foundations introduced in the first courses, or equivalent practical experience, is recommended.

Learning objectives

Import and profile data from files, relational databases, and cloud data warehouses such as BigQuery or Snowflake using Power Query or another suitable connector
Build repeatable data-cleaning rules instead of relying on manual fixes
Merge and append queries while validating matches
Professional workshop: consolidate omnichannel sales from three different sources

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: consolidate omnichannel sales from three different sources
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

29,90 € excl. VAT

Modeling & Exploration Pack 3 courses Level 3

🎓 Certificate : Data Analyst — Modeling & Exploration

📚 3 courses 📖 30 lessons ⏱ 9 to 15 h ⚡ 600 credits included

104,70 €

89,90 excl. VAT

Savings : 14,80 €

07 Level 3

Build an analytical model and DAX measures

Organize data in a star schema and build consistent business measures in Power BI.

Build a star schema with fact tables, dimensions, and a calendar Define the grain and additive measures of a fact table Build a calendar table aligned with reporting periods Validate the granularity of each table before creating relationships Configure relationships and filter directions with care Design a consistent semantic layer and share metric definitions Create business measures in Power BI and understand calculated columns versus measures Use core DAX concepts for ratios, variances, shares, and time-based indicators Prevent double counting by managing cardinality, granularity, and complex relationships Professional workshop: model sales, targets, and calendar data, then build the key measures

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 200 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

A working knowledge of the foundations introduced in the first courses, or equivalent practical experience, is recommended.

Learning objectives

Build a star schema with fact tables, dimensions, and a calendar
Validate the granularity of each table before creating relationships
Create business measures in Power BI and understand calculated columns versus measures
Professional workshop: model sales, targets, and calendar data, then build the key measures

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: model sales, targets, and calendar data, then build the key measures
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

34,90 € excl. VAT

08 Level 3

Design and validate a decision-oriented Power BI dashboard

Create a clear, interactive and reconciled Power BI report that business users can genuinely use to make decisions.

Identify a dashboard’s audience, decision, and frequency of use Design a decision-oriented dashboard page: visual hierarchy, emphasis on the key message, and controlled information density Build a layout grid and guide users from overview to detail Choose visuals for comparison, change over time, distribution, or composition Implement useful filters, slicers, drill-down, and interactions Make a report accessible through color, contrast, labels, and reading order Validate a dashboard by reconciling it with source data Design high-performing dashboards for large data volumes Present findings to a business stakeholder and address objections Professional workshop: build a complete operational performance dashboard

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 200 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

You should already master the pathway foundations or have equivalent experience. Advanced courses build on methods, tools and controls introduced earlier.

Learning objectives

Identify a dashboard’s audience, decision, and frequency of use
Choose visuals for comparison, change over time, distribution, or composition
Validate a dashboard by reconciling it with source data
Professional workshop: build a complete operational performance dashboard

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: build a complete operational performance dashboard
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

34,90 € excl. VAT

09 Level 3

Analyze time, funnels, cohorts and segments

Explore behavior and performance changes by combining time analysis, conversion, retention and segmentation.

Prepare a time dimension and distinguish event date, processing date, and reference date Analyze performance over time using calendars, comparable periods, and seasonality Identify trend, seasonality, and structural breaks without confusing variation with a lasting signal Build a funnel and measure conversion rates between stages Run a cohort analysis to understand retention and behavior over time Measure retention, repeat purchase, and churn by customer tenure Segment a population using relevant business criteria Detect anomalies, breaks, and outliers in a series of indicators Build a hypothesis tree to guide an investigation Professional workshop: analyze a conversion drop and defend several competing hypotheses

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 200 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

You should already master the pathway foundations or have equivalent experience. Advanced courses build on methods, tools and controls introduced earlier.

Learning objectives

Prepare a time dimension and distinguish event date, processing date, and reference date
Build a funnel and measure conversion rates between stages
Segment a population using relevant business criteria
Professional workshop: analyze a conversion drop and defend several competing hypotheses

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: analyze a conversion drop and defend several competing hypotheses
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

34,90 € excl. VAT

Advanced Analytics & Automation Pack 3 courses Level 4

🎓 Certificate : Data Analyst — Advanced Analytics & Automation

📚 3 courses 📖 30 lessons ⏱ 9 to 15 h ⚡ 660 credits included

119,70 €

101,90 excl. VAT

Savings : 17,80 €

10 Level 4

Master decision-oriented statistics and experimentation

Quantify uncertainty, test hypotheses and interpret experiments without overstating what the evidence supports.

Connect a statistical question to a business decision and the cost of being wrong Measure correlation, association, and dependence without confusing correlation with causation Identify confounding variables, selection bias, and reverse causality Build and interpret a confidence interval Understand the principles of hypothesis testing and decision errors Distinguish statistical significance from practical importance Analyze an A/B test correctly: metric, sample size, duration, significance, and bias Estimate sample size and minimum detectable effect Present a statistical conclusion with assumptions, uncertainty, and limitations Professional workshop: design, audit, and defend an experimentation protocol

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 220 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

You should already master the pathway foundations or have equivalent experience. Advanced courses build on methods, tools and controls introduced earlier.

Learning objectives

Connect a statistical question to a business decision and the cost of being wrong
Build and interpret a confidence interval
Analyze an A/B test correctly: metric, sample size, duration, significance, and bias
Professional workshop: design, audit, and defend an experimentation protocol

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: design, audit, and defend an experimentation protocol
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

39,90 € excl. VAT

11 Level 4

Build reproducible analyses with Python and pandas

Use Python and pandas to prepare, transform and validate data within a reproducible analytics project.

Set up an isolated Python environment and document its dependencies Structure a reproducible analysis with Python and pandas: environment, notebook or script, loading, checks, and transformations Load and profile CSV, Excel, Parquet, or JSON files with pandas Clean missing values, duplicates, and formatting issues with pandas Work with strings, dates, categories, and numeric types Join, aggregate, pivot, transform, and validate data with pandas Use groupby and aggregation functions without losing the intended granularity Add assertions and consistency checks to a data-processing workflow Write reusable functions and separate parameters, logic, and outputs Professional workshop: build a controlled pandas pipeline from raw data

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 220 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

You should already master the pathway foundations or have equivalent experience. Advanced courses build on methods, tools and controls introduced earlier.

Learning objectives

Set up an isolated Python environment and document its dependencies
Clean missing values, duplicates, and formatting issues with pandas
Use groupby and aggregation functions without losing the intended granularity
Professional workshop: build a controlled pandas pipeline from raw data

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: build a controlled pandas pipeline from raw data
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

39,90 € excl. VAT

12 Level 4

Automate data flows, APIs and analytical monitoring

Automate a robust and observable analytics workflow that handles failures without publishing invalid results.

Automate data imports from files and APIs, consistency checks, and result exports with Python Consume an HTTP API and interpret JSON, pagination, and response codes Manage authentication, environment variables, and secrets without hard-coding them Handle timeouts, outages, quotas, and retry logic Schedule a run and make the process idempotent Log processing steps, volumes, durations, anomalies, and final status Build blocking controls and warning controls Quarantine rejected data and organize correction and reprocessing Build an analytical story: context, finding, evidence, impact, and recommendation Professional workshop: automate a daily report with controls, alerts, and recovery

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 220 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

You should already master the pathway foundations or have equivalent experience. Advanced courses build on methods, tools and controls introduced earlier.

Learning objectives

Automate data imports from files and APIs, consistency checks, and result exports with Python
Handle timeouts, outages, quotas, and retry logic
Build blocking controls and warning controls
Professional workshop: automate a daily report with controls, alerts, and recovery

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: automate a daily report with controls, alerts, and recovery
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

39,90 € excl. VAT

Expertise & Leadership Pack 3 courses Level 5

🎓 Certificate : Data Analyst — Expertise & Leadership

📚 3 courses 📖 30 lessons ⏱ 9 to 15 h ⚡ 720 credits included

135,00 €

114,90 excl. VAT

Savings : 20,10 €

13 Level 5

Industrialize, test and maintain an analytics solution

Make analytical work versioned, tested, performant, observable and maintainable in an enterprise environment.

Structure and version an analytics project with Git: sources, transformations, models, reporting, branches, and change history Diagnose a slow SQL query using data volume, filters, joins, scans, and indexes without moving into advanced Data Engineer tuning Implement data tests for uniqueness, non-null values, referential integrity, thresholds, and reconciliations Automate quality controls and rejected-data handling with SQL and Python Document lineage, provenance, ownership, and transformation rules Monitor dataset freshness, availability, and quality, then alert on critical metrics Use generative AI in a controlled way to assist with SQL, Python, documentation, and anomaly investigation, with systematic verification of outputs Organize peer review for an analysis or model: versioned changes, comments, validation, and traceability Assess and reduce analytics debt: parallel files, duplicate calculations, and conflicting KPIs Professional workshop: turn a fragile reporting process into a governed analytics product

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 240 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

You should already master the pathway foundations or have equivalent experience. Advanced courses build on methods, tools and controls introduced earlier.

Learning objectives

Structure and version an analytics project with Git: sources, transformations, models, reporting, branches, and change history
Automate quality controls and rejected-data handling with SQL and Python
Use generative AI in a controlled way to assist with SQL, Python, documentation, and anomaly investigation, with systematic verification of outputs
Professional workshop: turn a fragile reporting process into a governed analytics product

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: turn a fragile reporting process into a governed analytics product
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

45,00 € excl. VAT

14 Level 5

Govern, secure and drive adoption of an analytics product

Define responsibilities, protect data and embed an analytics product in sustainable, compliant business use.

Understand data-governance roles and decision-making bodies Build a business glossary and a process for resolving definition disputes Facilitate a KPI-definition workshop and resolve conflicting definitions Implement access rights and distribution rules appropriate for sensitive data Understand GDPR requirements for analytics and data minimization Define responsibilities across data owners, analysts, business teams, IT, and governance Organize maintenance, refresh frequency, and service commitments Measure dashboard adoption and distinguish viewing, usage, and decision-making Assess the ethical risks of a metric, segmentation, or automated decision Professional workshop: build the governance dossier for a sensitive analytics product

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 240 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

You should already master the pathway foundations or have equivalent experience. Advanced courses build on methods, tools and controls introduced earlier.

Learning objectives

Understand data-governance roles and decision-making bodies
Implement access rights and distribution rules appropriate for sensitive data
Organize maintenance, refresh frequency, and service commitments
Professional workshop: build the governance dossier for a sensitive analytics product

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: build the governance dossier for a sensitive analytics product
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

45,00 € excl. VAT

15 Level 5

Lead an analytics engagement and defend a decision

Own an end-to-end analytics engagement, prioritize value, validate the solution and defend an executive recommendation.

Qualify a strategic request and identify the decision that actually needs support Build a metric tree linking objectives, drivers, indicators, and actions Define the scope of an analytics product, its users, and its success criteria Prioritize an analytics backlog by value, effort, risk, and frequency of use Design a validation strategy with control data and acceptance criteria Design and present a visual briefing for an executive committee: information hierarchy, key message, comparisons, scenarios, uncertainty, and limitations Build a quantified recommendation with scenarios, impacts, and assumptions Handle an urgent request without sacrificing quality or traceability Build a professional portfolio: context, method, results, limitations, and value created Final project: deliver a complete decision analysis, dashboard, controls, versioned work, and executive presentation

📚 10 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 240 credits included

Course information

Target audience

Data, reporting, finance, operations and performance professionals who want to perform or strengthen the practical skills of a Data Analyst.

Prerequisites

You should already master the pathway foundations or have equivalent experience. Advanced courses build on methods, tools and controls introduced earlier.

Learning objectives

Qualify a strategic request and identify the decision that actually needs support
Prioritize an analytics backlog by value, effort, risk, and frequency of use
Build a quantified recommendation with scenarios, impacts, and assumptions
Final project: deliver a complete decision analysis, dashboard, controls, versioned work, and executive presentation

Target skills

Apply the course methods and tools to a representative professional situation.
Produce a usable and verifiable outcome by combining the concepts covered: deliver a complete decision analysis, dashboard, controls, versioned work, and executive presentation
Check the quality of data, results, evidence or decisions before communication or implementation.
Document the approach, explain limitations and defend the choices made to a professional stakeholder.

Estimated duration

3 to 5 h

Price

45,00 € excl. VAT