Global Certificate in Classification Analysis for a Data-Driven World

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The Global Certificate in Classification Analysis for a Data-Driven World is a comprehensive course designed to equip learners with essential skills in data analysis. This certification focuses on classification, a fundamental aspect of data science, and teaches students how to make informed decisions based on data.

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In today's data-driven world, the demand for professionals with expertise in classification analysis has never been higher. This course prepares learners for careers in various industries, including technology, finance, healthcare, and marketing. By completing this program, students will gain a competitive edge in the job market, with skills in data preprocessing, model evaluation, and predictive modeling. The course covers a range of topics, including decision trees, logistic regression, and ensemble methods. Learners will also gain hands-on experience working with real-world datasets and industry-standard tools. By the end of the course, students will have a deep understanding of classification analysis and the practical skills to apply this knowledge in their careers.

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โ€ข Introduction to Classification Analysis: Defining classification analysis, its importance, and applications in data-driven decision making.
โ€ข Data Preprocessing: Data cleaning, wrangling, and transformation techniques for classification analysis.
โ€ข Feature Selection and Engineering: Identifying relevant features, dimensionality reduction, and creating new features for improved classification.
โ€ข Supervised Learning Algorithms: Overview of machine learning algorithms for classification, including logistic regression, decision trees, and support vector machines.
โ€ข Model Evaluation Metrics: Accuracy, precision, recall, F1 score, ROC curves, and other evaluation metrics for assessing classification models.
โ€ข Model Tuning and Validation: Techniques for hyperparameter tuning, cross-validation, and model selection to improve performance.
โ€ข Unsupervised Learning for Classification: Clustering algorithms and anomaly detection as alternatives or complements to supervised learning.
โ€ข Ethics in Classification Analysis: Understanding and addressing potential biases, fairness, and transparency issues in classification models.
โ€ข Deploying Classification Models: Best practices for deploying, monitoring, and updating classification models in production environments.

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GLOBAL CERTIFICATE IN CLASSIFICATION ANALYSIS FOR A DATA-DRIVEN WORLD
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ๅทฒๅฎŒๆˆ่ฏพ็จ‹็š„ไบบ
London School of International Business (LSIB)
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05 May 2025
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