To be successful in the near future you need to follow the following skillsets! These focus points will upgrade your porfolio and make you more demanding in the industry.
AI Ethics and Policy – explores the profound implications of AI on business and society. The ethical and policy issues linked with the application of AI in business are covered in-depth, including such issues as overcoming the job displacement due to AI by job creation, ensuring the public good as AI pervades the new economy, and balancing privacy and transparency in AI related endeavors.
Big Data Analytics – big data architectures, the Hadoop ecosystem (especially Spark), NoSQL databases, and a sampling of powerful applications of big data analytics, including recommender systems and social network analytics. New concepts will include additional applications of big data analytics, including text and unstructured analytics, such as sentiment analysis, document clustering, and document classification.
Analytics in Financial Markets – will include traditional models such as: the CAPM, portfolio optimization, applied contingent-claims analysis, Altman’s-Z, Monte-Carlo methods and applied econometric models. In addition, the course will also cover recent advances in artificial neural networks and machine learning tools applied to forecasting financial time-series and corporate default as well as Block-Chain analytics.
Predictive Modelling – three key elements: analytics techniques, business applications, and basic coding/programming (in R, one of the leading open-source tools for analyzing data that you will be able to use in your jobs.) The emphasis will be not on the technicalities or theory, but rather on applications to various business cases. Basic familiarity with R is required, but for most classes you will receive a starter code, by running and modifying which you will learn analytics techniques and coding principles, and which you will also be able to use in your jobs. Because of that, much of the course will be in a form of a “hands-on” workshop; students are be expected to bring your laptop to class (with all the necessary software tools installed) and actively participate in the learning process. The course will cover 2 major topics within the domain of predictive analytics: “predicting quantities” and “predicting events”. Within the “quantities” part we will focus on linear models, variable selection and regularizations, as well as on time-series analyses. Within the “events” part we will focus on generalized linear models (logistic regression) and get an introduction to supervised machine learning (CART, random forest, boosting, and neural networks).
Please reach out if you have any question! I am here to share my experiences and help you succeed in your career!