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.
In the contemporary digital landscape, data has emerged as a critical asset for organizations aiming to make informed decisions and foster innovation. Data analytics can… Read More »Understanding the difference: Data analyst, data scientist, and data engineer
This cutting-edge area of AI focuses on building models that can create original material, including music, images, text, and even entire virtual worlds.
In the ever-evolving landscape of the digital era, the relentless quest for deriving actionable insights from a sea of information has become the cornerstone of… Read More »Cracking the code: The rising demand for data scientists in various industries
E-commerce has improved technology and convenience for consumers globally. Fraud is a problem in e-commerce. Merchants and platforms fight fraud to protect their businesses and… Read More »In fraud detection for e-commerce: How does anomaly detection fit in and what are the key approaches?
Working as a data scientist is the dream of many IT professionals these days. It is no secret that data science is a skyrocketing field… Read More »Are data science certifications the gateway to competitive pay?
CUPED: Improve Your A/B Testing – Detect Smaller Gains, Utilise Smaller Samples and Make Smarter Decisions!
Ever-growing volumes of unstructured data stored in countless document formats significantly complicate data processing and timely access to relevant information for organizations. Without proper optimization of data management workflows, it’s difficult to talk about business growth and scaling. That is why progressive companies opt for intelligent document processing powered by artificial intelligence.
Learn about the challenges of data privacy and security, and the potential of smart technologies in creating efficient, livable urban environments.
Introduction Data Science is a vast field that incorporates several processes. From problem definition to data collection and data cleaning to data visualization, a lot… Read More »How can Data Scientists use ChatGPT for developing Machine Learning Models?
There are thousands of articles explaining the differences between data scientist and machine learning engineer. Data science gets broken down even further, with data analysts… Read More »The Rise of the Dual Data Scientist / Machine Learning Engineer
