04 - Open data | ILEAD GLOBAL TRAINING CENTER

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IGTC01DA - Data Science Foundations Fundamentals (EN)

01 - Introduction

01 - Getting started (1:33)

02 - 1. What Is Data Science

02 - The data science Venn diagram (4:29)
01 - Supply and demand for data science (4:28)
03 - The data science pathway (4:51)
04 - The CRISP-DM model in data science (4:01)
05 - Roles and teams in data science (4:25)
06 - The role of questions in data science (4:53)

03 - 2. The Place of Data Science in the Data Universe

02 - Machine learning (8:06)
01 - Artificial intelligence (8:27)
03 - Deep learning neural networks (8:23)
04 - Big data (5:37)
05 - Predictive analytics (4:57)
06 - Prescriptive analytics (7:41)
07 - Business intelligence (4:40)

04 - 3. Ethics and Agency

02 - Security (5:32)
01 - Bias (6:35)
03 - Legal (6:42)
04 - Explainable AI (9:56)
05 - Agency of algorithms and decision-makers (4:52)

05 - 4. Sources of Data

01 - Data preparation (5:26)
02 - Labeling data (8:49)
03 - In-house data (5:38)
04 - Open data (4:15)
05 - APIs (2:40)
06 - Scraping data (4:44)
07 - Creating data (5:37)
08 - Passive collection of training data (3:57)
09 - Self-generated data (3:30)
10 - Data vendors (5:30)
11 - Data ethics (5:14)

06 - 5. Sources of Rules

01 - The enumeration of explicit rules (4:04)
02 - The derivation of rules from data analysis (4:26)
03 - The generation of implicit rules (3:33)

07 - 6. Tools for Data Science

02 - Languages for data science (3:55)
01 - Applications for data analysis (4:52)
03 - AutoML (4:16)
04 - Machine learning as a service (3:21)

08 - 7. Mathematics for Data Science

01 - Sampling and probability (5:27)
02 - Algebra (7:25)
03 - Calculus (5:03)
04 - Optimization and the combinatorial explosion (6:10)
05 - Bayes- theorem (4:25)

09 - 8. Unsupervised Learning

02 - Descriptive analyses (6:38)
01 - Supervised vs. unsupervised learning (3:38)
03 - Clustering (5:45)
04 - Dimensionality reduction (5:38)
05 - Anomaly detection (5:00)

10 - 9. Supervised Learning

01 - Supervised learning with predictive models (7:32)
02 - Time-series data (9:15)
03 - Classifying (5:34)
04 - Feature selection and creation (5:49)
05 - Aggregating models (8:32)
06 - Validating models (5:46)

11 - 10 Generative Methods in Data Science

01 - Generative adversarial networks (GANs) (5:51)
02 - Reinforcement learning (6:02)

12 - 11. Acting on Data Science

01 - The importance of interpretability (3:17)
02 - Interpretable methods (5:03)
03 - Actionable insights (2:53)

13 - Conclusion

01 - Next steps and additional resources (2:47)

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04 - Open data

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