Learning Outcomes

Collect, store, and access data by identifying and leveraging applicable technologies

Any analysis to be conducted is only as good as its data. Garnering that data and making use of it in an effective, responsible, and safe manner is the foundation of any analytical process.

Create actionable insight across a range of contexts (e.g. societal, business, political), using data and the full data science life cycle

Data is as ubiquitous as ever, and there is always a story to be told from it. Ensuring that the story is meaningful and truthful is critical, and taking necessary meticulous steps can solve any problem an organization may face.

Apply visualization and predictive models to help generate actionable insight

Visuals can be more powerful than words and can generate a greater effect on policy when wielded properly. Predictive modeling also holds great power to paint a clearer picture of an uncertain future. If nothing and no one can say what will happen, it certainly helps to have a scope of all possible outcomes.

Use programming languages such as R and Python to support the generation of actionable insight

“A computer cannot be held accountable. Therefore a computer must never make a management decision.” The tools we have at our disposal give us the computational power to extract insights and interpret them, but it is on the analyst at the controls to facilitate the action required as a result of the analysis.

Communicate insights gained via visualization and analytics to a broad range of audiences (including project sponsors and technical team leads

Many decision makers and executives do not possess the same technical expertise as those conducting the analysis. Conveying any generated insights to stakeholders is as important as finding those insights to begin with. What good is driving change in the world if it’s impossible to communicate the positive outcomes that the change will bring?

Apply ethics in the development, use and evaluation of data and predictive models (e.g., fairness, bias, transparency, privacy)

Any data will have its limitations, regardless of its source or its use. Those limitations have to be taken into account when any insights are generated and acted upon.