ASS_SDAM BCS Data Analysis Course with Online Exam

COURSE OBJECTIVE:

 The Data Analysis course offers a deep dive into two key approaches to analysing and modelling data – analysis class modelling and data normalisation.

The course has recently been updated to include a module on data analytics, the interrogating and interpreting of data for the purpose of business decision making. The data analytics component looks at how data can be analysed with a business focus, offering critical insights which can drive decision making and pinpoint why some projects succeed and others fail. Techniques used to validate data against stated requirements are also explored.

Presented to you by one of the expert training consultants pictured below. Each member of our Data Analysis training team brings substantial data analysis, data modelling and data analytics experience to the programme.

TARGET AUDIENCE:

 Business analysts, project managers and solution developers who require a practical approach to analysing and modelling data. Data Analysis is also an Analytical Skills module on the BCS (ISEB) Advanced Diploma in Business Analysis.

COURSE PREREQUISITES:

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COURSE CONTENT:

During this course, you will cover:Introduction to Business Information and Data • Initial concepts and terminology- Information versus data- Data analysis versus data analytics • Data modelling and data models- Conceptual, logical and physical data models- Static and dynamic views of data • Structured and unstructured data • The Data LifecycleModelling Data Using Class Diagrams • Classifying elements of substance and their attributes • Classes and objects • Attributes • Associations and multiplicity- Types of relationships (one-to-one, one-to-many, many-to-many)- Resolving many-to-many relationships- Showing multiple roles • Aggregation and composition • Generalisation • Naming conventions • Class diagramsDefining Data Requirements • Defining data • Metadata (structural, descriptive and statistical metadata) • Data definitions • Domain definitions • Relational data theory- Two-dimensional structures- Using keys to identify data (primary, foreign, concatenated, compound and hierarchic keys) • Normalisation – The normalisation process- Un-normalised form, first normal form, second normal form, third normal form- Relations- TNF (Third Normal Form) model • Aspects of data qualityObtaining and Recording Data • Identifying sources of data • Validating data models using a CRUD matrix • Data navigation paths and Data Navigation DiagramsAnalysis for Decision Making • A process for data analytics • Sourcing datasets- Data lineage • Validating and cleansing datasets- Confirmation bias- Sampling- Outliers- Consistency • Dataset calculations- Counting- Totalling- Averaging (mean, median, mode)- Maximum and minimum- Probability- NULL values • Identifying meaningful relationships- Regression analysis- Correlation and causation- Time-series analysis and forecasting • Interpreting resultsProtecting Data • The imperative for protecting data • CIA (Confidentiality, Integrity and Availability) • Data protection principles • Data ethics – Data ethics principles • Online data

FOLLOW ON COURSES:

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