Artificial intelligence (AI) has transformed various fields, significantly impacting our daily lives. A major factor in AI success is high-quality data. In this paper, we present a comprehensive review of the evolution of data quality (DQ) awareness from traditional data management systems to modern data-driven AI systems, which are integral to data science. We synthesize the existing literature, highlighting the quality challenges and techniques that have evolved from traditional data management to data science including big data and ML fields. As data science systems support a wide range of activities, our focus in this paper lies specifically in the analytics aspect driven by machine learning. We use the cause-effect connection between the quality challenges of ML and those of big data to allow a more thorough understanding of emerging DQ challenges and the related quality awareness techniques in data science systems. To the best of our knowledge, our paper is the first to provide a review of DQ awareness spanning traditional and emergent data science systems. We hope that readers will find this journey through the evolution of data quality awareness insightful and valuable.

Inspired by Google DeepMind's team, Shakir Mohamed, William Isaac, and Implikit's founder Marie-Therese Png article, Decolonial AI, my experience with Data Science and readings, I'll try to propose a production strategy that compensates the lack of scalable ethics in Data Science Systems and make it embedded since the beginning of the development, saving the cost of change later. The main problem that I'll approach might be kind of obvious. Data Science does not implement efficient and scalable Ethical guidelines. Data Science is not Customer Centric, yet. The reason, I'll detail along the article is: Implicitly, our work might be motivated by solely on optimizing revenue, costs, human and non-human operational resources under the facade of enriching Customer Experience when we are launching Data-based products. This is as complicated to say as is it to tackle.

The trustworthiness of data science systems in applied and real-world settings emerges from the resolution of specific tensions through situated, pragmatic, and ongoing forms of work. Drawing on research in CSCW, critical data studies, and history and sociology of science, and six months of immersive ethnographic fieldwork with a corporate data science team, we describe four common tensions in applied data science work: (un)equivocal numbers, (counter)intuitive knowledge, (in)credible data, and (in)scrutable models. We show how organizational actors establish and re-negotiate trust under messy and uncertain analytic conditions through practices of skepticism, assessment, and credibility. Highlighting the collaborative and heterogeneous nature of real-world data science, we show how the management of trust in applied corporate data science settings depends not only on pre-processing and quantification, but also on negotiation and translation. We conclude by discussing the implications of our findings for data science research and practice, both within and beyond CSCW.

There is a tremendous amount of active research in making deep learning models interpretable (e.g., LIME and Layer wise Relevance Propagation). In summary, a high accuracy data science component by itself may not mean much even if it solves a pressing business need. On one extreme, it could be that the data science solution achieves high accuracy at the cost of high compute power or high turnaround time, neither of which are acceptable by the business. On the other extreme, it could be that the component that the end-user interacts with has minimal sensitivity to the errors of the data science component and thus a relatively simpler model would have sufficed the business needs. A good understanding of how the data science component fits into the overall end-to-end solution will undoubtedly help make the right design and implementation decisions.

Sites selection for warehouses to minimize distribution costs (proximity to vendors, balanced against proximity to consumers). How many warehouses are needed, and what capacity each of them should have. Selection of optimal routes, schedules, and products groupings, to minimize delivery costs (using graph theory) Supply chain optimization (III). Minimize time spent by drivers in traffic jams (requires traffic prediction) while optimizing delivery speed, gas usage and other factors (better be stuck 20 minutes in a traffic jam than a costly detour, or departing later?) Pricing and profit optimization (per-product price elasticity studies needed; may require products to be aggregated in categories, to create buckets that yield statistical significance) Fraud detection for credit card transactions (use decision tree methods).

Sites selection for warehouses to minimize distribution costs (proximity to vendors, balanced against proximity to consumers). How many warehouses are needed, and what capacity each of them should have. Selection of optimal routes, schedules, and products groupings, to minimize delivery costs (using graph theory) Supply chain optimization (III). Minimize time spent by drivers in traffic jams (requires traffic prediction) while optimizing delivery speed, gas usage and other factors (better be stuck 20 minutes in a traffic jam than a costly detour, or departing later?) Pricing and profit optimization (per-product price elasticity studies needed; may require products to be aggregated in categories, to create buckets that yield statistical significance) Fraud detection for credit card transactions (use decision tree methods).

These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection of articles related to the entry in question. Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science. Many of these articles are fundamental to understanding the technique in question, and come with further references and source code. Starred techniques (marked with a *) belong to what I call deep data science, a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics.

Thousands of articles and tutorials have been written about data science and machine learning. Hundreds of books, courses and conferences are available. You could spend months just figuring out what to do to get started, even to understand what data science is about. In this short contribution, I share what I believe to be the most valuable resources - a small list of top resources and starting points. This will be most valuable to any data practitioner who has very little free time.

Many people new to data science might believe that this field is just about R, Python, Hadoop, SQL, and traditional machine learning techniques or statistical modeling. Below you will find fundamental articles that show how modern, broad and deep the field is. Some data scientists are actually doing none of the above. In my case, I don't even code, but instead, I make various applications talk to each other, in a machine-to-machine communication framework. It is true though that most data scientists use R, Python and Hadoop-related systems.

Data Science sits at the core of any analytical exercise conducted on a Big Data or Internet of Things (IoT) environment. Data science involves a wide array of technologies, business, and machine learning algorithms. The purpose of data science is just not doing machine learning or statistical analysis but also to derive insights out of the data that a user with no statistics knowledge can understand. In a fast paced environment such as Big Data and IoT where the type of data might vary over the course of time, it becomes difficult to maintain and recreate the models each and every time. This gap calls up for an automated way to manage the Data Science algorithms in those environments.