Datacube fully supported Top Talent Pass Scheme (TTPS) 2023

John Lee, the Chief Executive of Hong Kong SAR Government, released policy address last month. The Hong Kong government laid a lot of emphases on snatching international talents for its target at enhancing HK’s all-rounded competitiveness (*1).   Job opportunities As a talented newly graduated from universities, you might wonder and concern about whether or…

What data scientists could do when it comes to weather science and model prediction ?

Weather highly involves life-and-death matters. Missions in weather science and prediction is not merely to help us understand the environment and its changing nature, such as global warming, but also to devise proactive strategies into improving preparedness in disaster, mitigating economic and lives’ losses (*1) and enhancing the overall well-being of citizens. With these missions,…

Concepts in Feature Engineering for decision-makers (Part 1)

Feature Engineering is an important term in data science and machine learning. Data scientists spent 80% of time in processing tasks of Feature Engineering, and 20% in training machine learning (ML) (*3). To elaborate, it is crucial processes of selecting, transforming, extracting, combining, and manipulating raw data to generate the desired variables for analysis or…

Intelligent production experience for general manufacturing field

Most of today’s factory production lines have experienced a transition from manual-intensive mode to semi-automation to full automation. However, there are also many manufacturers who have gradually completed digital transformation (Digital transformation) and even begun to promote intelligent production models (Intelligent / Smart manufacturing).   Regardless of digital or intelligent production, what elements do factories…

Key Components and Best Practices in Effective Data Architecture

Data is often considered the lifeblood of modern organizations. It drives decision-making, informs strategy, and underpins business operations. To harness the full potential of data, organizations need a robust data architecture. Effective data architecture not only ensures data is stored and managed efficiently but also makes it accessible, reliable, and secure. In this article, we…

Data Engineering: Key Considerations for Data Teams

In the modern era, organizations have increasingly centered their operations around data, leading to intricate challenges and escalating costs as data volumes expand. Gartner’s research underscores this, revealing that data integrity issues burden organizations with an astonishing annual average cost of $12.9 million. Such statistics emphasize the urgency for data professionals to pivot from merely…

How Can Data Science Transform Your Business in the AI Era?

In an era where data is the new oil, the future of business is inextricably linked to advancements in artificial intelligence (AI) and machine learning (ML). According to IDC, a staggering 83% of CEOs are keen on transforming their companies into data-centric organizations. Moreover, 87% of C-suite executives consider the transition to an intelligent enterprise…

AI and Data: How Much is Really Enough?

Data is often likened to modern gold. Every enterprise is on a quest for more data, especially when it comes to training AI models. The amount of data required can vary based on the specific AI task. While some AI models demand extensive datasets, others can work with limited data, leaving many perplexed about the…