The growth of data science as a key practice in modern business has brought unique challenges—technical, organizational, and ethical.
Data science teams are often structured more loosely than traditional IT or analytics teams, and they may use a wider range of platforms, tools, and techniques than IT teams are used to managing. Plus the techniques they use may be advanced and difficult to document or audit.
On top of this, there is increasing public concern about data security and privacy, and governments worldwide have introduced legislation to address this, including the European Union's GDPR. There are also lingering questions about the ethics of machine learning as it is applied in many cases, along with concerns about bias, business impact, and even social engineering.
This course introduces a consistent, practical framework for addressing governance and ethics concern in your data science initiatives both strategically and tactically.
You Will Learn
- The important differences between governance and compliance, security, and privacy
- The unique challenges faced by data science teams and the IT teams which support them
- The significance of the emerging role of the data engineer alongside the data scientist
- An outline of important regulations such as California’s Consumer Privacy Act and the EU’s GDPR
- Strategy and tactics for addressing regulatory concerns in modern data environments
- Methods of validating, documenting, and auditing data science processes
- Advice for both IT and business users who work with data science teams
- Data science staff
- CIOs and chief data officers
- Data governance staff
- Business/data analysts
- IT department staff supporting data scientists