Responsible Data Analytics

In recent years data has become the fuel of most digitalized organizations. Data Analytics is a powerful tool to observe, predict, diagnose issues within the organization and inform decision-making to solve these issues. It also helps explore, identify opportunities and assist in improving organizational processes.

But 'with great power comes great responsibility'. Data is seldom objective. It often represents a biased view of the world. When we base our observations and decisions upon this data, we need to be aware of this bias to avoid undesirable impacts. Besides, the way we frame, interpret, and act upon data analytics results can have negative externalities. A responsible data analytics project should account for and mitigate these harmful impacts and externalities.

In this course, you will master important tools and techniques used in data analysis projects” with a particular focus on the data scientist competencies. Data science can be defined as the computational skills of extracting meaningful insights from raw data and then effectively communicating those insights to generate value. You will apply data science tools and methods to answer real-life questions. You learn how to select data, preprocess data, and analyze and interpret results using basic statistics and machine learning techniques implemented in Python.

You will be invited to think critically about the field of Data Analytics and Machin Learning and understand the potential harmful externalities of the tools you are using in this course. You also will practice methods to identify and mitigate some of these externalities.