If machine learning and statistics are synonymous with one another, why are we not seeing every statistics department in every university closing down or transitioning to being a ‘machine learning’ department? Because they are not the same! A major difference between machine learning and statistics is indeed their purpose. However, saying machine learning is all about accurate predictions whereas statistical models are designed for inference is almost a meaningless statement unless you are well versed in these concepts.
The importance of visualization is a topic taught to almost every data scientist in an entry-level course at university but is mastered by very few individuals. This article focuses on the importance of visualization with data. The amount and complexity of information produced in science, engineering, business, and everyday human activity is increasing at staggering rates. Good visualizations not only present a visual interpretation of data, but do so by improving comprehension, communication, and decision making.
The future of computation looks like it will involve speeding up computations to handle the relentless and exponential increase in data production. However, speeding up individual processors is difficult for various reasons, and Moore’s law cannot last forever — it is becoming increasingly constrained by the limits of heat transfer and quantum mechanics. A greater push will continue to be seen towards parallel computing, especially with more specialized hardware such as GPUs and TPUs, as well as towards more energy-efficient computing which may become possible as we enter into the realm of neuromorphic computing.