Digital Generalist Credential


FSU Digital Generalist BadgeThe Digital Generalist Credential is one of the Digital Tech Credential promoted by the Capital CoLAB. It is a free program for students attending Universities in the Capital Region, and the goal is for the students to gain digital skills that are needed for entry-level jobs. The Generalist credential exposes students to the understanding of data analysis, data visualization, and data security. These three concepts are applied in every organization now in one way or the other. Equipping students with the knowledge, skills, and abilities in these concepts will prepare them better for the job market.

Some Opportunities that come with the Credential

  • Students will have access to a portal from CoLAB employers
  • Students can participate in annual internship fairs with CoLAB employers
  • Students will have access to professional development webinars and networking opportunities
  • Students can connect to a network of CoLAB students from CoLAB academic institutions
  • Upon completion of required courses, students will earn a Digital Badge that can be displayed on their LinkedIn profile and their resume

Earning the Digital Generalist Credential

If you already have a bachelor's degree, you can enroll in one of our Graduate Program Options that grant the Digital Generalist Credential. You can earn the credential while earning your MBA online by enrolling Business Analytics Concentration our AACSB Accredited MBA program. You can complete the MBA in one year of full-time graduate study or two years of part-time study. You can also complete our online Post Baccalaureate Certificate in Business Analytics. The Certificate can be completed in as little as 9 months of part-time study. Both programs offer three rolling start dates during the year and flexible 7-week online courses. These options are great if you are looking for an academic credential (degree or certificate) in addition to the Digital Generalist Credential. Learn more about our Graduate Program Options.

We also offer a low-cost option through the EdX online learning platform. You will complete six self-paced courses. Each course will take 4-6 hours of effort to complete, so you can finish in just a few months. There are three start dates per year, so you have additional flexibility. This option is great if you are looking for a faster, low-cost option, and you do not need an academic credential. 

What you will learn

The Digital Generalist Credential comprises 6 areas detailing the Knowledge, Skills, Abilities, and learning outcomes:

  • The Role of Data and Analytics
    1. Explain the importance of data and what data represent
    2. Differentiate common data typologies, including structured vs. unstructured, numeric vs. text, root vs. derived 
    3. Explain potential uses/applications given a source and type of data
    4. Demonstrate how data can be used to reduce uncertainty and risk related to decisions and decision-making
    5. Explain and demonstrate how differences in data and desired outcomes impact the appropriateness of data analysis techniques (e.g., descriptive vs. diagnostic vs. predictive vs. statistical)
  • Probability and Descriptive and Inferential Statistics
    1. Demonstrate knowledge of probability and standard statistical distributions
    2. Explain hypothesis testing and statistical significance
    3. Demonstrate and explain the role and importance of model validation and accuracy metrics in analytics projects, hypothesis testing, and information retrieval
    4. Explain the concept of the least squares criterion
    5. Describe the conditions that comprise the simple linear regression model
  • Data Manipulation
    1. Perform basic data manipulation and exploration using appropriate tools and software, including use of key Excel functions
    2. Create and edit simple data structures and storage
    3. Detect and remediate missing, miscoded, and anomalous data
    4. Explain the purpose of and code conditional logic statements
    5. Use a computer application to manage large amounts of information
    6. Implement common information retrieval and filtering applications in databases and data systems
    7. Find and access publicly available datasets
    8. Conduct ad hoc analysis (summarize, estimate, predict data, use pivot tables)
  • Data Visualization and Communication
    1. Explain the role of data visualization in discovery, communication, and decision-making 
    2. Evaluate data visualization options for proper application in various situations
    3. Create effective static and interactive data visualizations or narratives that employ analytics and visualization software and strategies for various audiences
    4. Visualize data using various types of displays including tables, dashboards, graphs, maps, and trees
    5. Distinguish between advanced visualizations and explain the advantages of each
    6. Discuss techniques for creating advanced data visualizations
    7. Apply the principles of color, composition, and hierarchy to design
    8. Properly define a problem in context, use appropriate data, and deliver a compelling visualization to explain or answer a question
    9. Understanding of ADA/508 compliance for accessibility
  • Data Ethics
    1. Identify how global legal, policy and/or ethical constraints might impact data analyses
    2. Identify the established ethical and legal issues in data management facing organizations
    3. Explain how ethical, compliance, and legal issues should/must be considered in data driven decision making
    4. Explain the importance of personal privacy issues related to the collection and usage of data
    5. Explain the important issues around data governance
    6. Recognize potential sources of bias in data or analysis
  • Data Security
    1. Explain information assurance (IA) principles and organizational requirements that are relevant to confidentiality, integrity, availability, authentication, and non-repudiation
    2. Apply confidentiality, integrity, and availability principles
    3. Explain data classification standards and methodologies based on sensitivity and other risk factors
    4. Explain authorization and access control principles and methods
    5. Describe the fundamental concepts of Risk Management and Risk Management Life Cycle
    6. Explain rationale for data anonymization and data security standards
    7. Identify situations vulnerable to insider threats
    8. Explain various methods to prevent insider threats
    9. Describe the spectrum of insider threats and its implications