- Data analytics, statistics, data analysis - Fundamental knowledge of statistics, data analysis (including correlation, regression and segmentation) and forecasting methods - Growth (decay), index calculations
Understanding and implementing
Target audience
- Internal auditors
- Bankers / Account managers
- Management controllers
- Financial directors and controllers
- Chartered accountants
- Treasurers
1 day
Prerequisites
No prior knowledge is required.
Objectives
◗ Integrating database fundamentals
◗ Design multiple relationships between databases to create a model suitable for decision-making
◗ Formulate requests for Data Warehouse organization
Training program
◗ Understanding data and its characteristics
◗ Understanding data and its characteristics
- Definition of data: nature, structure, typology
- Differences between structured and unstructured data
- Data sources in the enterprise
✔ UNDERSTAND | Illustration :: concrete examples of data used in management.
✔ APPLY | Case study: transforming a descriptive sentence into a formatted data line
✔ ASSESS | Quiz: what do you think... Can an image be considered as structured data?
structured data?
◗ Mastering databases and relational logic
- Database types: relational, hierarchical, object, dimensional
- Tables, attributes, records, relationships
- Primary and foreign keys, cardinalities and normalization rules
✔ UNDERSTAND | Case study: representing a relational model using Excel tables
✔ APPLY | Case study: building an entity-association schema
with relations (1,1/1,n/n,n)
✔ ASSESS | Quiz: what do you think... Why standardize a relational model?
◗ Design a dimensional model for analysis
- Differences between relational and star models
- Table of facts, dimensions, granularity, hierarchies
- Importance of cross-referencing and aggregation consistency
✔ UNDERSTAND | Illustration: building an OLAP cube with multi-dimensional views
✔ APPLY | Case study: identifying facts and dimensions in business reporting
✔ ASSESS | Quiz: what do you think... What is granularity in a dimensional model?
◗ Adopt a project modelling approach
- Key steps: expression of need, conceptual, logical and physical model
- Building the data dictionary
- Role of users, business and IT experts in the project
✔ UNDERSTAND | Illustration: project roadmap and RACI
✔ EXPERIMENT | Workshop: building a data dictionary for an HR model
✔ ASSESS | Quiz: what do you think... What's the difference between logical and physical models?
◗ Apply what you have learned to a practical case
- The case of the sports facilities census
- Identification of sources, dimensions and facts
- Construction of a usable data model for analysis
✔ APPLY | Case study: building a data model from public data (RES)
✔ EXPERIMENTALIZE | Case study: discuss with "requesters" (facilitator) to
frame the need
✔ EVALUATE | Quiz: what do you think... Can we create a model without clarifying uses upstream?
Why choose this course?
If your company (or department) is in the process of building or redesigning its data model, this module is for you! In particular, you'll learn the vocabulary and methods needed to become a credible contact with the IT department.
Teaching and assessment methods
Before: self-assessment quiz
During the session: alternating theoretical developments and illustrations drawn from real-life situations. Practical case studies and interactive quizzes ensure knowledge acquisition throughout the course.
Afterwards : the trainer is available to answer any questions relating to the training.
General training | FinHarmony Conseil & Formation
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Price
1 295 € EXCL. TAX
Testimonials
Big data training
Big data training
Big data training
Jonathan C.
Company
Training