Fintech Course Concepts

1. AI Biases

1.1. Interaction Bias

1.2. Latent Bias

1.3. Selection Bias

2. Data Integrity

2.1. Validity

2.2. Completeness

2.3. Accuracy

2.4. Consistency

2.5. Uniformity

3. Limitations on Data

3.1. Garbage-In-Garbage-Out

3.2. Spurious Correlations

3.3. Data Labeling

4. Heuristics

4.1. Anchoring

4.2. Availability

4.3. Representativeness

5. Learning

5.1. Artificial Intelligence

5.2. Supervised

5.3. Unsupervised

5.4. Machine Learning

5.5. Deep Learning

6. Internet of Things

7. Big Data

7.1. Structured

7.2. Unstructured

8. BlockChain

9. 4 Vs of Data

9.1. Volume

9.2. Variety

9.3. Velocity

9.4. Veracity

10. Quantum Computing

11. Cognitive Biases

11.1. Bandwagon

11.2. Choice Supportive

11.3. Confirmation Bias

11.4. Placebo

11.5. Overconfidence

11.6. Survivorship

11.7. Selective

11.8. Blindspot

11.9. Ostrich Bias

12. Data Analytics

12.1. Descriptive

12.2. Predictive

12.3. Diagnostic

12.4. Prescriptive

13. Nudge Theory

14. Regulatory Sandbox

15. Alternative Data

15.1. Web scraping

15.2. Sentiment Analysis

16. Data Platforms

16.1. PowerBI

16.2. Alteryx

16.3. Tableau

17. SAS

17.1. Tidy Data

17.2. Feature Engineering

17.3. Importing Data

17.4. Merging

17.5. Accumulating

17.6. Panel Data

17.7. Visualization