Network Analytics for Business

The specialization is intended for a general audience of business analysts, seeking to augment their toolkit with the newest analytical methods. Specifically, they will get introduced to the analysis of networks and unstructured data (texts) – the two areas that are currently hailed as the “methods of the future.” The connections that people build and the words they use can potentially tell us much more about the organizational processes than the traditional analytics on numbers.

It is also for anyone who wants to learn practical analytics. “Business” is a very broad domain. Learners will know about the application of the newest methods to the analysis of business and will be able to extend the knowledge to other areas.

To immerse the newly learned methods into the broader analytical context, specialization offers two additional overview courses. One is dedicated to the field of analytics, helping form a solid understanding of the role that different methods play in generating insights. The second one is an overview of business analytics specifically, with practical tools and tips for a more successful analytic outcome.

Each course contains a variety of hands-on projects that will aid the understanding of new methods and provide real-life applications of newly learned skills. Having completed the specialization, learners will acquire advanced methodological tools that can be immediately implemented to improve the analysis and decision-making in the modern corporate world.

All projects included in this specialization are providing the hands-on experience with the newly learned methods. Unlike “textbook” projects that use clean, “nice” data, these projects are based on real-life data, with their corresponding problems and issues.

• Use the R programming language to work with both structured and unstructured (text) data

• Prepare text data for analysis

• Train and evaluate supervised learning models on text data

• Train and evaluate unsupervised learning models on text data

• Interpret the results of unsupervised and supervised modelling