Why should you take this course

Immediate value, practical templates, experiential learning

  • 25 exercises, templates & downloads

    Practice creating a data dictionary, data model, data mapping or a scenario matrix as you learn. Templates and handouts included with course materials.

  • 7 hours of video lessons

    Main concepts are explained with visuals, checklists, and examples from real-life projects in every video lesson. Your lessons will feel like a one-on-one coaching session.

  • Apply what you learn

    The course encourages you to practice what you learn right away: define data management requirements, engage in data governance activities, ask analytics questions and define useful metrics.

Course curriculum

  • 1

    What’s the big deal about data?

  • 2

    Types of data

    • Data, information, and knowledge

    • Structured, unstructured, and semi-structured data

    • Quantitative and qualitative data

    • Data types and formats: examples and exercises

    • Big and small data. Why should we care about types of data?

  • 3

    Managing data in enterprise initiatives

    • Data in enterprise initiatives

    • Sources of data. Operational and analytical systems

    • Data governance and the role of business analyst

  • 4

    Data life cycle requirements

    • Data life cycle

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    • Sourcing requirements: data definitions & data collection

    • Data dictionary

    • Data mapping

    • Data modelling

    • Data protection, usage, and sharing requirements

  • 5

    Using data for business analysis

    • How to use data analysis to support business analysis

    • Using data analysis on the projects and a summary of analysis techniques

    • Data analysis techniques and when to use them

    • Scenario analysis and scenario matrix

    • Querying data and SQL

    • Ad-hoc and diagnostic analysis

  • 6

    Types and methods of business analytics

    • Using analytics to solve business problems; types of analytics

    • Descriptive analytics

    • Diagnostic analytics

    • Predictive and prescriptive analytics

  • 7

    Requirements for analytics and AI projects

    • Defining analytics requirements: the process

    • Identifying the data required for analytics

    • Analytics personas, styles, and capturing analytics requirements

    • Conclusion: how to mitigate analytics project failures

Instructor(s)

Social proof: testimonials

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