The field of data science is growing with increasing demand. Data science is not limited to only consumer goods or tech or healthcare. There is a high demand to optimize business processes using data science from banking, transport to manufacturing. Organizations are now hiring data science professionals to deal with complex data. To become an expert in data science read the article and check out the list of top-rated data science courses on Coursera.

AI is becoming strategic for many companies across the world. The technology can be transformative for just about any part of a business. But AI is not easy to implement. Even top-notch companies have challenges and failures. So what can be done? Well, one strategy is to provide AI education to the workforce.

Machines or your computers only understand numbers and these numbers need to be represented and processed in a way that enables these machines to solve problems by learning from data instead of predefined instruction as in the case of programming. All types of programming use mathematics at some level and machine learning is programming data to learn the function that best describes the data. The problem(or process) of finding the best parameters of a function using data is called model training in ML. Therefore, in a nutshell, machine learning is programming to optimize for the best possible solution and we need math to understand how that problem is solved. The first step towards learning Math for ML is Linear algebra. Linear Algebra is that mathematical foundation that solves the problem of representing data as well as computations in machine learning models.

We explore an online learning reinforcement learning (RL) paradigm for optimizing parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a workload donation model, (2) a high-order workload estimation model, and (3) a communication cost model, to optimize the performance of data-parallel particle tracing dynamically. First, we design an RL-based workload donation model. Our workload donation model monitors the workload of processes and creates RL agents to donate particles and data blocks from high-workload processes to low-workload processes to minimize the execution time. The agents learn the donation strategy on-the-fly based on reward and cost functions. The reward and cost functions are designed to consider the processes' workload change and the data transfer cost for every donation action. Second, we propose an online workload estimation model, in order to help our RL model estimate the workload distribution of processes in future computations. Third, we design the communication cost model that considers both block and particle data exchange costs, helping the agents make effective decisions with minimized communication cost. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations up to 16,384 processors.

Knowledge transfer across several streaming processes remain challenging problem not only because of different distributions of each stream but also because of rapidly changing and never-ending environments of data streams. Albeit growing research achievements in this area, most of existing works are developed for a single source domain which limits its resilience to exploit multi-source domains being beneficial to recover from concept drifts quickly and to avoid the negative transfer problem. An online domain adaptation technique under multisource streaming processes, namely automatic online multi-source domain adaptation (AOMSDA), is proposed in this paper. The online domain adaptation strategy of AOMSDA is formulated under a coupled generative and discriminative approach of denoising autoencoder (DAE) where the central moment discrepancy (CMD)-based regularizer is integrated to handle the existence of multi-source domains thereby taking advantage of complementary information sources. The asynchronous concept drifts taking place at different time periods are addressed by a self-organizing structure and a node re-weighting strategy. Our numerical study demonstrates that AOMSDA is capable of outperforming its counterparts in 5 of 8 study cases while the ablation study depicts the advantage of each learning component. In addition, AOMSDA is general for any number of source streams. The source code of AOMSDA is shared publicly in https://github.com/Renchunzi-Xie/AOMSDA.git.

Federated learning (FL) is a popular technique to train machine learning (ML) models with decentralized data. Extensive works have studied the performance of the global model; however, it is still unclear how the training process affects the final test accuracy. Exacerbating this problem is the fact that FL executions differ significantly from traditional ML with heterogeneous data characteristics across clients, involving more hyperparameters. In this work, we show that the final test accuracy of FL is dramatically affected by the early phase of the training process, i.e., FL exhibits critical learning periods, in which small gradient errors can have irrecoverable impact on the final test accuracy. To further explain this phenomenon, we generalize the trace of the Fisher Information Matrix (FIM) to FL and define a new notion called FedFIM, a quantity reflecting the local curvature of each clients from the beginning of the training in FL. Our findings suggest that the {\em initial learning phase} plays a critical role in understanding the FL performance. This is in contrast to many existing works which generally do not connect the final accuracy of FL to the early phase training. Finally, seizing critical learning periods in FL is of independent interest and could be useful for other problems such as the choices of hyperparameters such as the number of client selected per round, batch size, and more, so as to improve the performance of FL training and testing.

However, keeping in mind that there is no "right way" to study or pursue a career in data science – a self-study plan is not an alien concept. So, let's dive deep for a detailed comparison between the two on various aspects. The field of data science looks for skills and problem-solving attitudes. Getting degrees is an accomplishment, but degrees alone offer no guarantee of landing a job. Pick up a programming language (Python or R), learn how to code, and practise fundamental concepts such as calculus, statistics, probability, regression analytics, etc. Once the foundation is well-laid, go for advanced specialisation in neural networks, machine learning, and deep learning.

AI is becoming strategic for many companies across the world. The technology can be transformative for just about any part of a business. But AI is not easy to implement. Even top-notch companies have challenges and failures. So what can be done?

Artificial intelligence is being deployed in many different areas. Within higher education, it is used for college admissions and financial aid decisions. Health researchers employ it to scan the scientific literature for chemical compounds that may generate new medical treatments. E-commerce sites deploy algorithms to make product recommendations for consumers based on their areas of interest.1 But one of the most important growth areas lies in finance and operations. Both public and private sector organizations have large budgets to manage and it is important to operate efficiently and effectively. Accusations of budget inefficiencies or wasteful spending decrease public confidence and make it important to figure out how to manage resources in fair ways. To help with budgetary oversight, AI is being used for financial management and fraud detection. Advanced algorithms can spot abnormalities and outliers that can be referred to human investigators to determine if fraud actually has taken place. It is a way to use technology to improve budget audits, personnel performance, and organizational activities. Yet is it crucial to overcome several problems that plague public sector innovation: procurement obstacles, insufficiently trained workers, data limitations, a lack of technical standards, cultural barriers to organizational change, and making sure anti-fraud applications adhere to responsible AI principles.

With the advancement of technology, AI influencers and virtual human models are becoming the new trend. It has recently emerged as a blue-chip in the advertising industry because there are no privacy scandals and there are no time-space restrictions with these virtual humans. In particular, the use of virtual humans seems to be gaining more momentum in the COVID-19 pandemic, where there are many restrictions on travel and limitations on the number of people gathering. On September 10, Baek Seung Yeop, CEO of Sidus Studio X that created'Rozy,' the newly rising blue-chip in the advertisement industry, explained, "These days, celebrities are sometimes withdrawn from dramas that they have been filming because of school violence scandals or bullying controversies. However, virtual humans have zero scandals to worry about."