Working with Big Data
Full course description
This course provides a systematic introduction to the tools and analytical methods that are being used by data analysts, with special attention to sentiment analysis, the process of computationally identifying and categorizing opinions expressed in text, in order to determine whether the writer's attitude towards the topic is positive, negative, or neutral. This includes understanding how to collect and organise data at scale, and gain new insights on how Big Data analysis can help in addressing high impact research questions. You will develop both the technical-computational skills that are in high demand across a range of research organizations and industry, as well as critical skills in computational thinking, algorithm design, big data fundamentals, and data-driven analysis, with plenty of opportunities to apply and explore your new learnings through case studies.
At the end of this course, you will be able to:
- Understand key concepts and identify technologies in the field of Big Data;
- Design research based upon Big Data sentiment analysis, including selecting appropriate digital methods, technologies, and strategy for storage and processing data;
- Learn Big Data analytics and apply guided Sentiment Analysis and data mining tool on Structured and Unstructured datasets using R;
- Use appropriate digital methods to interpret and share results obtained by means of Big Data analyses;
- Critically evaluate and discuss the implications of employing Big Data and sentiment analysis in society.
We strongly recommend that you have successfully completed DSO2502 Quantitative Data Analysis before starting this course. We will be building on material covered in DSO2502.
Agresti, A. (2018). Statistical Methods for the Social Sciences (Fifth edition). Harlow: Pearson