Data and Analytics Learner Education Pathway (Level 2)
In this Education Pathway Level, learners further their Data Literacy journey with brief examinations of cutting-edge topics like AI and Machine Learning. The pathway also delves into crucial organizational subjects such as Data Strategy and Data Architecture, alongside brief explorations of business glossaries and data catalogs. Moreover, it moves deeper into the Data Security conversation from the previous level, focusing this time on Data Security strategies. The pathway culminates with a primer on Data Science (as a continuation of the initial AI/ML talks) and a more in-depth exploration of the business benefits of metadata.
The Data and Analytics Learner Education Pathway is structured to furnish individuals with fundamental knowledge in data and analytics. Divided into three distinct levels, each pathway offers a carefully selected series of courses that facilitate a progressive understanding of topics commonly encountered within organizations and integral to career advancement.
These Education Pathways cater to anyone seeking a conceptual grasp of data and analytics across diverse domains and disciplines, with the aim of assisting (and possibly challenging) current perspectives and enhancing the effective utilization of organizational information resources.
Each pathway consists of 3.5 – 4.5 hours of course content, complemented by practice exams after each course. Upon completion of the courses within a Pathway, both the individual courses and the entire Education Pathway confer certificates of completion.
**This is Subscription-Only Content, It is NOT purchasable as a separate product**
Data and Analytics Learner Education Pathway (Level 2):
In this Education Pathway Level, learners further their Data Literacy journey with brief examinations of cutting-edge topics like AI and Machine Learning. The pathway also delves into crucial organizational subjects such as Data Strategy and Data Architecture, alongside brief explorations of business glossaries and data catalogs. Moreover, it moves deeper into the Data Security conversation from the previous level, focusing this time on Data Security strategies. The pathway culminates with a primer on Data Science (as a continuation of the initial AI/ML talks) and a more in-depth exploration of the business benefits of metadata.
Electives include an overview of deep learning to go along with similar topics in this Pathway, and a Data Governance and Information Quality Conference session that looks into how to get people on-board with Data Governance.
Each Course Includes:
- A 15- to 48-minute educational training video
- Total time: 3:34 hours
- A 6- to 13-question review exam
- “Check for Understanding” quizzes after each course section
- Self-paced and on-demand e-learning
- Unlimited course access
- Pathway CEUs: 7.0 hours (with electives)
Required Courses within this Education Pathway:
- What is Artificial Intelligence? (Anthony Algmin)
- What is Machine Learning? (Anthony Algmin)
- What is Business Glossary? (Anthony Algmin)
- What is a Data Catalog? (Anthony Algmin)
- What is Data Strategy? (Anthony Algmin)
- What is Data Architecture? (Anthony Algmin)
- DYBP2: Data Security Strategies (Jeremy Taylor)
- DSBP1: Introduction to Data Science (Jeremy Taylor)
- MM2: The Business Value of Metadata (Donna Burbank)
Elective Courses within this Pathway:
- What is Deep Learning? (Anthony Algmin)
- DGIQ-E22: How to Get Everyone Excited about Data Governance (Annemieke de Groot)
Anticipated Data Literacy Proficiency Level: Foundational
At this level, most individuals possess minimal experience in working with data and may lack fundamental comprehension of basic data principles. They might not be acquainted with data types, data concepts, tools for visualizing data, pertinent technologies, or other common data practices, necessitating assistance and training to proficiently leverage data within their responsibilities.
- This level of training (within specified Education Pathways) may also be relevant for business professionals, non-technical people, and leadership who want to gain a basic understanding of a given data topic or an overall understanding of a range of data concepts. (e.g. What is Data Strategy and Why Does Our Organization Need One?)
The Purpose of this Level is for Learners to Gain:
- A fundamental grasp of concepts facilitating effective communication among stakeholders while understanding their role data and analytics usage.
- An ability to pose relevant inquiries at the onset of a project, fostering avenues for brainstorming and creativity.
Prerequisites: No course prerequisites or previous expertise necessary.
Technical Coverage: Presents any technical topics around how they affect the business and with clear explanations prior to going into more depth.
Applicable Job Roles/Functions: Foundational learners seeking baseline data and analytics understanding, new staff on-boarding, career development/career change learners, operational employees, data entry staff, front and back-office administrators, contact center teams, and other similar roles.
Foundations Unit - Complete All Four Courses
1. What is Artificial Intelligence?
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Artificial intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information collected. Commonly known as machine intelligence, AI analyzes its environment using either predetermined rules and search algorithms, or pattern-recognizing machine learning models, and then makes decisions based on those analyses. Not intended to replace human contributions, this technology improves enterprise productivity by automating processes or tasks that once required human power.
View Details2. What is Machine Learning?
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Machine learning (ML) is about building programs with adaptable parameters. Through innovative construction, queries automatically adjust based on changing data sets the programs receive. By adapting to previously seen data, programs can improve their behavior. At its most basic, machine learning is the practice of using algorithms to parse data, learn from it, and then decide or predict a beneficial outcome of artificial intelligence (AI) for digital transformation.
View Details3. What is a Business Glossary?
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A business glossary is a means of sharing internal vocabulary within an organization. Most business glossaries share certain characteristics, such as standard data definitions or documentation of them. Along with maximizing search capabilities within an organization, a business glossary is also used as a framework to create, nurture, and promote a common vocabulary. Business glossaries ensure trust in a company’s data and reduce risks that data will be misused due to an inconsistent understanding of the business concepts.
View Details4. What is a Data Catalog?
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A data catalog is intended to inform customers about the available data sets and metadata around a topic. Along with assisting users in locating data quickly, a data catalog differs from a data dictionary in its ability for searching and retrieving information. Instead, it is about establishing a directory that allows users to gain easy access to business definitions. Additionally, their self-service capabilities make them a valuable piece of the business intelligence structure.
View DetailsProgressions Unit - Complete All Three Courses
1. What is Data Strategy?
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Data Strategy concerns how an organization supports Business Strategy with data. This includes creating a strategy that effectively deals with how data systems and architectures will be built, and how data will be consumed and used to create a measurable improvement in business outcomes. How does an organization use data to make the business better? That is the central question when building a Data Strategy.
View Details2. What is Data Architecture?
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Data Architecture is what an organization “builds” to create data value, such as systems, platforms, databases, rules, and processes that direct data efforts. Data Architecture is guided by and also influences business processes and strategies that are essential to success. This course examines the core concepts of Data Architecture, including: definitions, why Data Architecture is important, uses of Data Architecture, getting started, and more.
View Details3. DYBP2: Data Security Strategies
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In this course, instructor Jeremy Taylor discusses some fundamental Data Security roles, measures, and types of controls. He also discusses various responsibilities, reporting and responding to threats, data breach case studies, and the basics of zero trust security.
View Details4. What is Deep Learning?
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Deep learning is a type of machine learning (ML) that utilizes neural networks to disseminate complex patterns. This type of technology allows artificial intelligence (AI) systems to perform human-like tasks, such as recognizing real-life objects or understanding speech. While the operation of these brain-inspired networks remains inscrutable, their interconnected layers’ algorithms give machines the ability to be trained and carry out specific tasks. Through algorithms that drive processes, organizations rely on the account of abstract variables to produce meaningful results.
View DetailsCompletions Unit - Complete Both Courses
1. DSBP1: Introduction to Data Science
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This Introduction to Data Science course, with instructor Jeremy Taylor, is designed to provide students with a comprehensive introduction to the field of Data Science. Students will learn primary concepts, look at case studies featuring different types of analytics, and gain an understanding of the importance of Data Science in business, the role of data warehousing, main trends, and more.
View Details2. MM2: The Business Value of Metadata
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This course delves into the importance of metadata to overall business success. It looks at why effective Metadata Management is essential to organizations of all sizes through a discussion around wasted costs, brand damage, financial reporting and audits, Big Data Analytics, efficiency and reuse, change management, business agility, value to Data Governance, and more.
View Details3. DGIQ-E22: How to Get Everyone Excited About Data Governance
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