Earning Criteria

• Over the course of 6 weeks, complete a minimum of 4 of the 6 classes offered: Introduction to Data Science Principles; Introduction to Probability & Statistics; Introduction to Structured & Unstructured Data; Introduction to Modeling & Machine Learning; Data Literacy; and, ROI: The Business Impact of Data Science. The classes are offered as blended learning modules designed to leverage fixed course content and instructor videos with instructor-led live sessions and an applied project.
• Complete an “Introduction to Data Science Principles” class, which provides a basic overview of critical descriptions and tasks including analytics taxonomy and methodology, practices that avoid biases, basic tools in data diagnostics (visualization, outliers, missing values, etc.), and types of analytic output (dashboards, algorithms, models, etc). Applied project required for completion.
• Complete an “Introduction to Probability & Statistics” class, which provides basic understanding of fundamental statistical processes, including: sample space, counting, independence, Bayes Theorem, probability distributions (binomial, Poisson, normal, and Students T), point estimation and confidence intervals. The course also also provides an understanding of how data science technical processes are important to data analytics. Applied project required for completion.
• Complete an “Introduction to Structured & Unstructured Data” class, which offers an intermediate dive into unstructured data. Topics include data wrangling, data organization, databases, the four V’s, SQL and non-SQL based data organization and retrieval, searching, text analysis, natural language, data curation and management, data lifetime. The class explains how to draw insights from analysis of formats such as audio, video, and social media postings. Applied project required for completion.
• Complete an “Introduction to Modeling & Machine Learning” class, which overviews the fundamentals of modeling and machine learning. The class introduces supervised classification and regression: linear, logistic, Poisson; Regression model evaluation metrics; and Model validation, evaluation, optimization; and addresses the top mistakes in predictive modeling. Applied project required for completion.
• Complete a “Data Literacy” class, which provides a basic overview of Big Data and data analytics used in organizations today, the evolution of data-based decision-making, primary topics and tools of data analytics, explanations and examples for buzzwords like Big Data, Regression, Machine learning, Data Networks, Cloud Computing, Predictive Modeling, and others. Applied project required for completion.
• Complete a “ROI: The Business Impact of Data Science” class, which focuses on creating well-defined Data Science questions, identifying Key Performance Indicators (KPIs), defining the project and metrics for evaluating success, and system tools needed to measure project team performance. Applied project required for completion.