This rubric covers the use of statistical tools working on large datasets to create models and derive inferences, as well as coverage of the field in its entirety. This differs from machine learning primarily in that the latter focuses on functional gradient analysis or neural networks (kernels) to derive models.
Without using any API or any Python library, yet delivering better results. You would think this is a massive undertaking. However, it took me less… Read More »Why and How I Created my Own LLM from Scratch
The emergence of deepfakes has presented both fascinating opportunities and formidable challenges in the digitally evolving landscape. Deepfakes, a portmanteau of “deep learning” and “fake,”… Read More »The integral role of data science in navigating deepfakes
How do some industries seem to have cracked the code for success? It’s not luck—it’s the power of data science that changes the game. Whether it’s… Read More »How data science is reshaping diverse industries
Artificial Intelligence (AI) has been around for many decades but now it has become a buzzword even among non-technical people because of the generative AI… Read More »Mitigating Ethical Risks in Generative AI: Strategies for a Safe and Secure AI Application
One of the efforts our Dataworthy Collective will be ramping up in 2024 involves standardizing the building of logical knowledge graphs at the level of… Read More »Why FAIR data assets are essential to AI data management
Data science has come a long way! Using basic statistical models, 19th-century organizations gathered, stored, and processed data. Later, when computers entered the picture, the… Read More »Data science transformations for 2024 and beyond
After playing with GPT for some time, testing GenAI vendor solutions, designing my own, and reading feedback from other users, I uncovered a number of… Read More »Towards Better GenAI: 5 Major Issues, and How to Fix Them
Data analysts must have a strong grasp of practical data visualization skills to paint a clear picture of complex data for a broader audience. Seeing… Read More »Beyond the numbers: The soft skills that elevate data analysts to the next level
Modern data quality practices leverage advanced technologies, automation, and machine learning to handle diverse data sources, ensure real-time processing, and foster collaboration across stakeholders. They… Read More »Difference between modern and traditional data quality
In the last two years, I published 5 machine learning and AI books, including one on synthetic data by Elsevier. This represents over 800 pages… Read More »New Book: Statistical Optimization for GenAI and Machine Learning
