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)

Author and instructor

Yulia Kosarenko

Yulia Kosarenko is a consultant, author, speaker, and coach. Her career spans more than 25 years of bringing IT and business together through business analysis, architecture, analytics, communications, and change management. She is the author of Business Analyst: a Profession and a Mindset and Business Analysis Mindset Digital Toolkit and part-time professor at the Humber College in Toronto, Business Insights and Analytics program. Yulia is active in business analysis and analytics communities, leading webinars, participating in podcasts, coaching, and sharing her thoughts with LinkedIn communities. You can always find her at why-change.com.

Social proof: testimonials

A well structured course with many exercises

Lina, Sr. Business Analyst

A well-structured course with many exercises, and I enjoyed the chapters a lot. Recommend it for business analysts - we must have an understanding of how to use, transform and govern data structures.

Useful tools for my BA toolkit

James, Business Analyst

Highly recommended, the course packs in a lot of detail. It's full of great insights and useful tools for my BA toolkit.

Rich, in-depth and practical

Tokunbo, Business Analyst

I just concluded this insightful course. It was rich, in-depth and practical, showcased various ways to use analytics, and covered data lifecycle requirements and requirements for AI projects.

Useful templates for Data Dictionary and Data Mapping

Lisha N., Data Analyst

The templates for Data Dictionary and Data Mapping with examples were useful to me; knowing how to do it right is important on a project.

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