Computer data science continues to be a rapidly evolving field, with new trends and subfields emerging to address the challenges and opportunities presented by increasing volumes of data and advancements in technology. Some of the emerging study fields in computer data science are focusing on some notable areas:

1. Explainable AI (XAI):
Understanding and interpreting how AI algorithms arrive at their decisions is crucial, especially in fields like healthcare and finance. XAI focuses on making AI systems more transparent and interpretable for users and regulators.

2. Federated Learning:
Federated learning enables model training across multiple decentralized edge devices (like smartphones or IoT devices) without exchanging raw data. This is particularly important for privacy-sensitive applications.

3. Quantum Computing:
Quantum computing explores the use of quantum-mechanical phenomena to perform computation. In the context of data science, it has the potential to solve complex problems, including optimization and cryptography, at speeds currently inconceivable with classical computers.

4. Automated Machine Learning (AutoML):
AutoML focuses on automating the process of applying machine learning to real-world problems. This field involves the development of techniques that automatically select the best machine-learning models and algorithms for a particular dataset.

5. Natural Language Processing (NLP) Advancements:
NLP is evolving with the development of more sophisticated algorithms, especially in areas like sentiment analysis, language translation, and context-aware language understanding. This is closely tied to advancements in deep learning and transformer-based architectures.

6. Robotic Process Automation (RPA):
RPA involves using software robots (bots) or "chatbots" to automate highly repetitive and routine tasks in business processes. Data science plays a crucial role in optimizing these processes and making them more intelligent.

7. Exponential Growth in Big Data Technologies:
While not a new field, the tools and technologies for handling big data are continually evolving. This includes advancements in distributed computing frameworks like Apache Hadoop and Apache Spark, as well as new database technologies designed to handle massive volumes of data.

8. Ethics in Data Science:
With the increasing power of data science comes a greater responsibility. Ethical considerations related to data privacy, bias in algorithms, and the societal impact of data-driven decisions are becoming critical areas of study.

9. Blockchain Technology:
Blockchain technology, primarily known for its use in cryptocurrencies, has applications in data science, especially in ensuring data integrity, provenance, and security in various applications.

10. Edge Computing:
Edge computing involves processing data closer to the source of data generation (e.g., IoT devices) rather than relying on a centralized cloud server. Data scientists are exploring ways to optimize algorithms for edge devices, making real-time, decentralized data analysis possible.

11. Augmented Analytics:
Augmented analytics uses machine learning and natural language processing to enhance data analytics tools. It automates data preparation, insight discovery, and sharing, making data analysis accessible to users with varying levels of technical expertise.