Introduction to Big Data Techniques

Refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

AI involves creating computer systems capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

A subset of AI, ML involves algorithms that parse data, learn from that data, and then apply what they have learned to make informed decisions. ML is particularly useful in settings where designing and programming explicit, rule-based algorithms is challenging.

A type of ML where the model is trained on a labeled dataset, which means the data is already tagged with the correct answer.

A type of ML where the model is trained using information that is neither classified nor labeled, and the system tries to learn the patterns from the data without guidance.

An advanced type of ML that uses deep neural networks to analyze various levels of data features, allowing the model to make intelligent decisions from unstructured data.

Techniques used to capture, curate, store, search, and transfer data efficiently to ensure quality and readiness for analysis.

Big Data refers to extremely large datasets that are characterized by high volume, velocity, and variety. These datasets include traditional data from financial statements and markets, as well as alternative data from sources like social media and IoT devices. Big Data is used in quantitative investment analysis to enhance decision-making and generate alpha.

Machine Learning is a subset of AI that involves algorithms learning from data to make predictions or decisions without being explicitly programmed. In fintech, ML algorithms analyze financial data to identify patterns and make investment decisions.

AI involves creating computer systems capable of performing tasks that typically require human intelligence. In fintech, AI is used to process and analyze large amounts of data to identify complex relationships that might not be evident through traditional methods.

Alternative data refers to data derived from non-traditional sources such as sensors, social media, and the Internet of Things (IoT). This data is increasingly used in quantitative investment analysis to provide additional insights that are not available from traditional data sources.

Data processing methods include capture, curation, storage, search, and transfer, each playing a crucial role in handling and preparing data for analysis. These methods ensure data quality, manage storage, and facilitate efficient data retrieval and transfer.

Data visualization techniques such as 3D graphics, heat maps, and tag clouds help in understanding and analyzing Big Data by visually representing data to uncover trends and relationships. These tools are essential for both structured and unstructured data.

Text analytics and NLP involve analyzing text and voice data to extract meaningful information. Applications include sentiment analysis, topic detection, and compliance monitoring, which are crucial for understanding market sentiments and aiding investment decisions.