Text as Alternative Data: An Introduction with Forecasting Applications

Learn with our expert faculty

facultyHannes Mueller
PhD, London School of Economics
IAE-CSIC and BSE

Director
facultyArgimiro Arratia
PhD, University of Wisconsin
Associate Professor at Universitat Politècnica de Catalunya

Instructor

Course overview

Text mining is a rapidly growing field within data science that involves analyzing large amounts of text data to extract valuable insights and information. Text is an "alternative data source" that can be processed to produce data from a variety of sources including social media, news articles, and customer reviews. By analyzing this data, organizations can gain a better understanding of their customers, track trends and sentiment, identify themes and topics, and make more informed decisions. This makes text mining skills valuable in a variety of sectors including marketing, finance, healthcare, education, and government.

The goal of this course is to introduce you to the simplest methods of text mining (like dictionary-based methods) up to the newest methods (like transformer-based methods). This will be done with specific applications, taught by professors with a long experience in providing text mining solutions to the public and private sectors.

Get to know how text mining is used in larger pipelines that provide concrete decision-making support

This 20h online course will enable you to integrate textual data into your work environment after only two weeks of training. You will be able to use dictionary-based sentiment analysis for stock market evaluations, use the topic model to conduct political risk predictions, and use BERT to conduct sentiment analysis on financial text.

After successful completion of the course you will:

  • Understand text pre-processing
    Text pre-processing is an essential step in the text mining process, as it prepares the text data for further analysis. By learning text pre-processing techniques such as tokenization and stemming, you will be able to effectively clean and prepare text data for analysis.

  • Have learnt LDA
    Latent Dirichlet Allocation (LDA) is a popular technique for topic modeling that allows you to identify the main themes or topics present in a collection of documents. This can be useful for text summarization, document classification, and information retrieval.

  • Understand sentiment analysis
    Sentiment analysis is the process of identifying the sentiment expressed in a piece of text, whether it be positive, negative, or neutral. By learning sentiment analysis techniques, you will be able to extract valuable insights and information from large amounts of text data.

  • Have learnt BERT
    BERT is a state-of-the-art transformer-based language model that has achieved impressive results on a variety of natural language processing tasks. By learning about BERT, you will be able to use this powerful tool to perform tasks such as text classification and language translation.

Get the tools to effectively analyze and extract insights from large amounts of text data, a valuable skill in today's data-driven world.

This course will be taught online but it will be live and interactive.

INTENSIVE COURSE

Text as Alternative Data: An Introduction with Forecasting Applications

Applications will open soon!
  ONLINE
Regular Fee 1950 €
Reduced Fee 1100 €

10% early-bird discount applies to payments made on or before April 7, 2023 at 23:59 (CET)

See below for reduced fee eligibility


Early-bird payment deadline: April 7, 2023

  ONLINE
Regular Fee 1950 €
Reduced Fee 1100 €

10% early-bird discount applies to payments made on or before April 7, 2023 at 23:59 (CET)

See below for reduced fee eligibility


Last day to apply: May 3, 2023

  ONLINE
Regular Fee 1950 €
Reduced Fee 1100 €

10% early-bird discount applies to payments made on or before April 7, 2023 at 23:59 (CET)

See below for reduced fee eligibility

This edition is closed. Next edition TBA.

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