Week 1:  July 10 - 14 -- On-line

• Introduction to data science lifecycles 

• Basics of Python programming language (types, variables, expressions, order of operations) 

• Team building activities 

Week 2: July 17 - 21 -- On-line

• Conditional control flow and loops 

• Functions, name spaces, and scope 

• Lists and search algorithms 

• Good coding practices, modules, file access, and data acquisition 

Week 3: July 24 - 28 -- On-line

• Exploratory data analysis and visualization 

• Predictive machine learning models (regression and classification) 

• Forming project teams 

Week 4: July 31 - August 4 -- On-line -- REQUIRED

• Pipelines for data analysis and data exploration cycle 

• Neural networks and multi-layer perceptron networks 

Week 5: August 7 - 11 -- On-line -- REQUIRED

• Team project work and preparation for presentations to industry guests 

Week 6: August 14 - 18 -- IN-PERSON -- REQUIRED

• Characteristics of trustworthy data science lifecycles 

• Risks in various stages of data science lifecycles and risk mitigations 

• Forming the learning community 

• Improving trustworthiness of a data science lifecycle through team projects 

• Final project presentations to external guests