Unstructured Data Analytics
Companies, governments, and other organizations now collect massive amounts of data such as text, images, audio, and video. How do we turn this heterogeneous mess of data into actionable insights? A common problem is that we often do not know what structure underlies the data ahead of time, hence the data often being referred to as “unstructured”. This course takes a practical approach to unstructured data analysis via a two-step approach:
- We first examine how to identify possible structure present in the data via visualization and other exploratory methods.
- Once we have clues for what structure is present in the data, we turn toward exploiting this structure to make predictions.
Many examples are given for how these methods help solve real problems faced by organizations. Along the way, we encounter many of the most popular methods in analyzing unstructured data, from modern classics in manifold learning, clustering, and topic modeling to some of the latest developments in deep neural networks for analyzing text, images, and time series. We will write lots of Python code and also work with cloud computing (Google Colab).
By the end of the course, students are expected to have developed the following skills:
- Recall and discuss common methods for exploratory and predictive analysis of unstructured data
- Write Python code for exploratory and predictive data analysis that handles large datasets
- Work with cloud computing (Google Colab)
- Apply unstructured data analysis techniques discussed in class to solve problems faced by governments and companies
95888 or 90819 or 95898