Data Science Bootcamp

Kingston Open College

Assessment

Included in course price

• Module 01: Foundations of Data Science 08:19

  • 1: Foundations of Data Science: Part 01 Preview 05:14
  • 2: Foundations of Data Science: Part 02 03:05

• Module 02: Statistical Learning and Model Assessment 07:05

  • 3: Statistical Learning and Model Assessment: Part 01 02:37
  • 4: Statistical Learning and Model Assessment: Part 02 04:28

• Module 03: Supervised Learning Methods 07:23

  • 5: Supervised Learning Methods: Part 01 03:24
  • 6: Supervised Learning Methods: Part 02 03:59

• Module 04: Unsupervised Learning and Pattern Discovery 06:48

  • 7: Unsupervised Learning and Pattern Discovery: Part 01 04:05
  • 8: Unsupervised Learning and Pattern Discovery: Part 02 02:43

• Module 05: Advanced Topics and Emerging Trends 07:13

  • 9: Advanced Topics and Emerging Trends: Part 01 04:31
  • 10: Advanced Topics and Emerging Trends: Part 02 02:42

• Module 06: Ethics, Communication, and Professional Practice 06:00

  • 11: Ethics, Communication, and Professional Practice: Part 01 06:00

Data Science Bootcamp begins with foundations of data science. It defines the discipline and explains its evolution. It covers structured, unstructured, and complex data. It introduces data collection, preprocessing, and quality management. It explains Python, R, SQL, and the KDD and CRISP-DM processes. The course then strengthens statistical learning and model assessment. It explains supervised and unsupervised learning methods. It clarifies bias–variance trade-offs and generalisation principles. It covers training, validation, and test splits. It evaluates models using accuracy, ROC, and precision–recall metrics. It also addresses ethical and interpretability considerations. The programme then explores supervised learning techniques. It covers linear and logistic regression, ridge and lasso, decision trees, ensemble methods, SVMs, and neural networks. It then shifts to unsupervised learning and pattern discovery. It explains clustering, association rules, anomaly detection, PCA, and factor analysis. Advanced topics follow with NLP, time series analysis, CNNs, RNNs, transformers, MLOps pipelines, deployment, drift management, generative AI, and LLMs. Finally, it addresses fairness, privacy, communication, and career pathways.

• Graduates in STEM fields
• Business analysts
• IT professionals
• Career changers into analytics
• Researchers and academics

• You should have basic mathematical knowledge.
• However, prior programming experience is helpful but not mandatory.
• In addition, analytical thinking supports success.

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