11
Quantitative MethodsModule 11 of 11

Introduction to Big Data Techniques

5

Concepts

0

Formulas

1

Decisions

3

Quiz Questions

Key Concepts

5 concepts covered in this module.

Big Data Characteristics

Volume (large amount), Velocity (speed of generation), Variety (structured/unstructured), Veracity (reliability).

Machine Learning Types

Supervised: labeled data (classification, regression). Unsupervised: no labels (clustering, dimensionality reduction). Reinforcement: reward-based learning.

Overfitting

Model fits training data too closely, captures noise, performs poorly on new data. Regularization and cross-validation help prevent it.

Text Analytics / NLP

Sentiment analysis, topic modeling, and text classification applied to financial documents, news, and social media.

Fintech Applications

Robo-advisors, algorithmic trading, blockchain, RegTech. Data-driven investment and risk management.

Decision Frameworks

1 decision frameworks to guide your analysis.

Supervised vs Unsupervised Learning?

  • Supervised: when you have labeled outcomes (predict stock up/down)
  • Unsupervised: when discovering hidden patterns (cluster similar stocks)

Mind Map

Visual overview of how concepts connect in this module.

Big Data & ML
Big Data (4 Vs)
Volume
Velocity
Variety
Veracity
ML Types
Supervised (labeled)
Unsupervised (unlabeled)
Reinforcement (reward)
Deep Learning
Applications
Text analytics/NLP
Robo-advisors
Algorithmic trading
Risk management
Challenges
Overfitting
Data quality
Bias
Interpretability
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Big Data Characteristics

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Volume (large amount), Velocity (speed of generation), Variety (structured/unstructured), Veracity (reliability).
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