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Weiterbildung: Text Analysis

 

Text Analysis


 
Weitere Informationen zu diesem Seminar

Details:
Zielgruppe:
Employees and leaders interested in driving the digital transformation in their companies.
  
Voraussetzung:
  • first university degree
  • good level of English
  • Beschreibung:
    Big Data, Digital Innovation, Business Analytics and Management Analytics are crucial tools for the upcoming challenges for businesses. Digitization is changing the business world fast and fundamentally.
    In order to keep up with these developments and leverage them for your company's success, we offer you our flexible and modular Business Analytics Certificate. You can either benefit from the whole intense ten-week program or book single classes to pinpoint and address specific topics in your business. All courses are taught online.
    Understand the relevance of Business Analytics for strategic decision-making. Get to know methods and tools for data analysis. Learn how to identify new business opportunities and create value for your own business.

    Buy your ticket here:
    https://www.xing-events.com/UYKUPIU.html

    Inhalt:
    In the digital age, techniques to automatically process textual content have become ubiquitous. In the course Text Analysis, participants learn the basic concepts of text mining and natural language processing as well as tools and best practices for automatically obtaining actionable information from large amounts of unstructured textual data. The participants of the course learn the basics of natural language processing – text preprocessing, syntactic analysis (capturing structure) and semantic analysis (capturing meaning) of text -- as well as the common approaches for tasks and use-cases commonly found in companies and generally in the business setting: information extraction, document classification, business process mining, and sentiment analysis. While the course will introduce the necessary theoretical concepts, it will primarily focus on practical applications and introduce a range of tools and software libraries for analyzing text and inducing actionable knowledge from various sources of unstructured textual data.

    Course Objectives:
    The aim of the course is to equip participants with the understanding of most important concepts, tools and methods of processing textual data and extracting actionable information from text. The focus on company use cases and applications from the business domain will enable participants to add business value in company settings abundant with text data. The following are the expected learning outcomes of the course:

    1. Understanding of the basic building blocks of text mining and natural language processing systems: text preprocessing, capturing structure in text (syntax), capturing meaning of text (semantics).

    2. Knowledge and understanding of effective approaches to tasks frequently found in business use-cases and scenarios:
  • Document analysis: document clustering and classification
  • Information extraction, e.g., (named) entity extraction
  • (Business) process mining from textual descriptions
  • Sentiment analysis and processing of social media text


  • 3. Familiarity with tools and libraries for text processing and information extraction from text (as well as related machine learning libraries): e.g., scikit-learn, SpaCy, NLTK, Keras

    4. Knowledge of metrics for evaluating the quality of text analysis methods and tools

    Examination mode:
  • Case Study

    Optional tutorials included:
  • Data Science With R
  • Data Science With Python
  • Methode:
  • synchronous & asynchronous lectures
  • case studies
  • group work
  • guest lectures
  • Abschlussart:
    Zertifikat
    Max. Teilnehmer:
    60
    Trainer/Referent:
    Prof. Dr. Goran Glavaš
    Seminartyp/en:
    Live Training Live Seminar
    E-Learning Training eLearning Seminar
    Kategorien:
    Business Administration
    IT-Grundlagen
    Statistik
     

    Termine, Ort und Preis auf Anfrage
    Kontakt/Seminaranfrage Katalog

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    Letzte Aktualisierung: 28.01.2021
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