Global Certificate in Data Assessment for Language Analytics
-- ViewingNowThe Global Certificate in Data Assessment for Language Analytics is a comprehensive course designed to equip learners with essential skills in language data assessment. This course is crucial in today's digital age, where businesses rely heavily on data-driven decision-making.
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⢠Data Acquisition for Language Analytics: This unit will cover the best practices for gathering and processing language data, including data cleaning, preprocessing, and normalization techniques.
⢠Natural Language Processing (NLP) Techniques: This unit will delve into the various NLP techniques used in language analytics, including tokenization, stemming, lemmatization, part-of-speech tagging, and named entity recognition.
⢠Machine Learning for Language Analytics: This unit will cover the fundamental concepts of machine learning and how they can be applied to language analytics, including supervised and unsupervised learning, classification, clustering, and regression analysis.
⢠Deep Learning for Language Analytics: This unit will explore the use of deep learning techniques for language analytics, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers.
⢠Sentiment Analysis and Opinion Mining: This unit will focus on the application of language analytics for sentiment analysis and opinion mining, including the use of polarity and subjectivity analysis, aspect-based sentiment analysis, and emotion detection.
⢠Topic Modeling and Text Summarization: This unit will cover the use of language analytics for topic modeling and text summarization, including the use of latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), and extractive and abstractive summarization techniques.
⢠Ethics and Bias in Language Analytics: This unit will explore the ethical considerations and potential biases that can arise in language analytics, including issues related to privacy, fairness, accountability, and transparency.
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