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  • Courseware 
    Big Data Strategies to Transform Your Business

    Big Data Strategies to Transform Your Business

    While big data infiltrates all walks of life, most firms have not changed sufficiently to meet the challenges that come with it. In this course, you will learn how to develop a big data strategy, transform your business model and your organization. This course will enable professionals to take their organization and their own career to the next level, regardless of their background and position. Professionals will learn how to be in charge of big data instead of being subject to it. In particular, they will become familiar with tools to: - assess their current situation regarding potential big data-induced changes of a disruptive nature, - identify their options for successfully integrating big data in their strategy, business model and organization, or if not possible, how to exit quickly with as little loss as possible, and - strengthen their own position and that of their organization in our digitalized knowledge economy The course will build on the concepts of product life cycles, the business model canvas, organizational theory and digitalized management jobs (such as Chief Digital Officer or Chief Informatics Officer) to help you find the best way to deal with and benefit from big data induced changes. During the course, your most pressing questions will be answered in our feedback videos with the lecturer. In the assignments of the course, you will choose a sector and a stakeholder. For this, you will develop your own strategy and business model. This will help you identify the appropriate organizational structure and potential contributions and positions for yourself.

    Subjects:
    Management and Computing
    Keywords:
    Business -- Data processing Big data
    Resource Type:
    Courseware
     
  • Presentation 
    Analyzing Word Frequencies in Large Text Corpora using Inter-arrival Times and Bootstrapping

    Analyzing Word Frequencies in Large Text Corpora using Inter-arrival Times and Bootstrapping

    This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. Comparing frequency counts over texts or corpora is an important task in many applications and scientific disciplines. Given a text corpus, we want to test a hypothesis, such as "word X is frequent", "word X has become more frequent over time", or "word X is more frequent in male than in female speech". For this purpose we need a null model of word frequencies. The commonly used bag-of-words model, which corresponds to a Bernoulli process with fixed parameter, does not account for any structure present in natural languages. Using this model for word frequencies results in large numbers of words being reported as unexpectedly frequent. We address how to take into account the inherent occurrence patterns of words in significance testing of word frequencies. Based on studies of words in two large corpora, we propose two methods for modeling word frequencies that both take into account the occurrence patterns of words and go beyond the bag-of-words assumption. The first method models word frequencies based on the spatial distribution of individual words in the language. The second method is based on bootstrapping and takes into account only word frequency at the text level. The proposed methods are compared to the current gold standard in a series of experiments on both corpora. We find that words obey different spatial patterns in the language, ranging from bursty to non-bursty/uniform, independent of their frequency, showing that the traditional approach leads to many false positives.

    Subjects:
    Management and Computing
    Keywords:
    Computational linguistics Text processing (Computer science) Discourse analysis -- Data processing
    Resource Type:
    Presentation
     
    OER