Global Certificate in Textual Data Analytics

Name: Global Certificate in Textual Data Analytics
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The Global Certificate in Textual Data Analytics is a comprehensive course designed to empower learners with the essential skills required to thrive in today's data-driven world. This course is critical for individuals seeking to gain a competitive edge in their careers, as textual data analytics has become increasingly important across industries.

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AboutThisCourse

The course covers a range of topics, including natural language processing, machine learning, and data visualization, providing learners with a well-rounded understanding of textual data analytics. By completing this course, learners will be able to extract valuable insights from unstructured text data, enabling them to make informed decisions and drive business success. In addition to learning cutting-edge techniques and tools, learners will also have the opportunity to work on real-world projects, providing them with practical experience and preparing them for success in the workforce. With the demand for textual data analytics skills on the rise, this course is an excellent investment in your career advancement and long-term success.

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CourseDetails

• Data Preprocessing for Textual Data: Data cleaning, tokenization, stemming, lemmatization, stop words removal, and vectorization.
• Natural Language Processing (NLP): Text processing, part-of-speech tagging, named entity recognition, and sentiment analysis.
• Machine Learning for Text Analytics: Supervised and unsupervised learning algorithms, model evaluation, and hyperparameter tuning.
• Deep Learning for NLP: Word embeddings, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers.
• Topic Modeling and Text Summarization: Latent Dirichlet allocation (LDA), latent semantic analysis (LSA), and text summarization techniques.
• Information Retrieval and Search: Information retrieval models, search algorithms, and ranking strategies.
• Text Classification and Clustering: Multi-class and multi-label classification, hierarchical and k-means clustering.
• Ethical Considerations in Textual Data Analytics: Bias, fairness, transparency, and privacy concerns.

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EntryRequirements

BasicUnderstandingSubject
ProficiencyEnglish
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BasicComputerSkills
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FastTrack GBP £140

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AcceleratedLearningPath

ThreeFourHoursPerWeek
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StandardMode GBP £90

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FlexibleLearningPace

TwoThreeHoursPerWeek
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GLOBAL CERTIFICATE IN TEXTUAL DATA ANALYTICS

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London School of International Business (LSIB)

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05 May 2025

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