We offer workshops, modules, courses and labs on the following topics:

Data Strategy and Governance

  • Developing a Data Strategy Roadmap
  • Developing a Data Governance Strategy
  • Data Strategy and Governance Checklist Review
  • Empowering a Data Focused Workplace
  • Analyzing Data to Identify Improvement Activities
  • Initiating and Managing AI/ML Projects
  • AI/ML Projects: Case Study Workshop
  • Data Roles: Who to Hire in the information Workplace
  • Data Toolbox Overview I
  • A Manager’s Guide to Data Engineering
  • Black Books: Identifying and Managing Rogue Data Sources
  • Data Mapping: Identifying and Managing Organizational Data Sources
  • Visualizing Performance: Best Practices in Management Dashboard Design
  • Management Dashboard Design Workshop
  • Identifying Key Performance indicators
  • Preparing to Publish Open Data

Data Visualization with Power BI

  • Best practices in Data Visualization
  • Power BI lessons – an overview of Microsoft Power BI
  • Power BI lessons – building basic visualizations
  • Power BI lessons – Beyond the Basics
  • Power BI lessons – a deeper dive into data modeling
  • Power BI lessons – integrating R and Python
  • Power BI lessons – advanced DAX and M
  • Power BI lessons – supervised self learning
  • Power BI lessons – monthly Power BI Lab
    • Data Science

      Data Science Basics

      • Data science universals; systems thinking
      • Programming basics
      • Data processing
      • Data processing
      • Data engineering basics
      • Basic data analysis techniques
      • Measures and metrics
      • Data and information architecture
      • Predictive analytics

      Data Visualization for Analysis

      • Simple multi-variable methods
      • Simple visualizations in R/Python
      • Design suggestions
      • Grammar of graphics; dashboards
      • Visualizations with ggplot2
      • Visualizations with seaborne

      Machine Learning 101

      • Association rules
      • Clustering overview; k-means
      • Hierarchical; issues and challenges
      • Clustering
      • Classification overview; decision trees
      • Naïve Bayes; issues and challenges
      • Classification

Data Science, cont.

Focus on Supervised & Unsupervised Learning

  • DBSCAN; spectral clustering
  • Expectation-maximization; latent Dirichlet allocation
  • Ensembles clustering; fuzzy clustering; validation
  • Clustering
  • Logistic regression; value estimation
  • Support vector machines; neural networks
  • Rare occurrences; ensemble learning; boosting methods; evaluation
  • Classification

Text Mining and Natural Language Processing

  • Text processing
  • Building a lexicon
  • Text mining basics; sentiment analysis
  • Sentiment analysis
  • Text visualizations
  • Natural language processing basics
  • Natural language processing for French
  • Natural language processing tasks


  • Genetic algorithms
  • The semantic web
  • Optimization
  • Synthetic data and simulations
  • Self-organizing maps
  • Uniform manifold approximation & projection

Special Topics in AI/ML/DS

  • A big data adventure
  • Introduction to deep learning
  • Data science with streams
  • Web scraping
  • Recommender systems
  • Bayesian data analysis
  • Anomaly detection
  • Social network analysis
  • Reporting and deployment
  • Feature selection and data reduction