This chapter explores fundamental analytical techniques in data science, distinguishing between data analysis (backward-looking) and data analytics (forward-looking prediction).

Six key analysis categories are covered:

Descriptive Analysis examines current data through statistical measures (mean, median, mode) and visualizations to understand "what is happening."

Diagnostic Analytics investigates "why something happened" using correlation analysis, emphasizing the distinction between correlation and causation.

Predictive Analytics forecasts future outcomes using historical data and regression analysis.

Prescriptive Analytics determines optimal courses of action by analyzing potential decisions.

Exploratory Analysis discovers unknown relationships through visualization when questions aren’t predetermined.

Mechanistic Analysis examines exact variable changes and their effects.

The chapter emphasizes statistical literacy as essential for data scientists, covering key concepts like variable types, frequency distributions, measures of centrality and dispersion, and regression modeling. Hands-on examples demonstrate applications across business, healthcare, and social sciences.

This chapter focuses on applying data science and machine learning techniques to real-world problems using Python. It covers four main applications: clinical data analysis, social media data collection and analysis, and large-scale data processing.

The chapter begins with exploring clinical data from a dermatology study, demonstrating visual exploration, gradient descent regression, random forest classification, and k-means clustering techniques. It then transitions to social media analysis, specifically working with Reddit APIs to collect and analyze posts, examining relationships between variables like post length, scores, and upvotes.

The YouTube section covers API authentication and data collection for video statistics analysis. Finally, the Yelp analysis demonstrates big data processing techniques, exploring user behavior patterns through correlation analysis, regression modeling, and clustering of review data.

The chapter emphasizes practical API usage, data visualization, statistical testing, and the importance of understanding both the problem and data before analysis.

The introduction briefly reviews the growing significance of remote work and then presents the volume’s holistic and interactive approach to studying the impact and regulation of this employment approach. With a rooting in methodological discussions and institutional analysis, this approach assumes that the full impact of remote work can only be understood by identifying and analyzing ways in which different employment forms and their regulation interact with one another in complex ways. Thus, for example, an employee’s work is not only remote or located in the traditional workplace but it is also part time or full time and so forth. Moreover, each of these conditions may be only partial in nature. Not only in empirical reality but also in the regulation of work, types of employment and their regulation interact with one another in ways that the volume identifies, explains and theorizes, opening up new understandings. The introduction then lays out the thematic concerns and main arguments of the chapters authored by a distinguished set of contributors.

The rise of remote work and work-from-home, accelerated by the COVID pandemic, is reshaping and disrupting the social dimension of work—that is, the incidence of cooperation, sociability, and solidarity (as well as conflict) among co-workers. Most discussion of the trend toward remote work centers naturally on its impact on firms (especially in terms of productivity) or workers (especially in terms of work-life balance). This essay focuses instead on how remote work and work-from-home might affect the social underpinnings of political life outside the workplace. The quotidian experience of working together—traditionally, face-to-face, and often across salient lines of social division—generates weak and strong interpersonal bonds that can strengthen the foundations of a democratic society. The cumulative societal benefits of co-worker interactions are at risk if remote work thins out and weakens workplace ties. That is especially likely because those societal benefits are “public goods” and spillover benefits of workplace interactions. Those social benefits may thus be neglected by analysts and observers. This essay develops that thesis and then reflects briefly on whether and how the conventional institutional arsenal of labor and employment law might be deployed to increase the production of such public goods.

This chapter explores the fundamentals of data in data science, covering data types (structured vs. unstructured), collection sources (open data, social media APIs, multimodal data, synthetic data), and storage formats (CSV, TSV, XML, RSS, JSON). It emphasizes the critical importance of data pre-processing, including data cleaning (handling missing values, smoothing noisy data, data munging), integration, transformation, reduction, and discretization. Through hands-on examples, the chapter demonstrates how to systematically prepare "dirty" real-world data for analysis by addressing inconsistencies, outliers, and missing information. The chapter highlights that data preparation is often half the battle in data science, requiring both technical skills and careful attention to data quality and bias.

This chapter examines the main challenges posed by remote working from the perspective of occupational health and safety protection. Methodologically, the chapter utilizes a multi-level perspective and also focuses on how the temporal and spatial breadth of remote work affect health and safety at work and its regulations. The chapter analyzes the problem of applying the current concepts of effective working time and rest time to the new activity times that arise in remote work. The study also examines the problems that arise regarding controlling and recording working time in remote work, as well as the legal limits of the new forms of control used by companies. The need to articulate specific forms of digital disconnection and to introduce online working time as a psychosocial risk factor is addressed. The chapter also examines the implications of remote work for the management of occupational risk prevention. In addition to how occupational risk prevention planning is carried out, special attention is paid to the new occupational risks that may appear in the digital sphere, such as cyber-bullying, but also the increase in more traditional psychosocial risks, and the difficulties that arise in achieving an effective assessment of these risks.

This introductory chapter defines data science as a field focused on collecting, storing, and processing data to derive meaningful insights for decision-making. It explores data science applications across diverse sectors including finance, healthcare, politics, public policy, urban planning, education, and libraries. The chapter examines how data science relates to statistics, computer science, engineering, business analytics, and information science, while introducing computational thinking as a fundamental skill. It discusses the explosive growth of data (the 3Vs: velocity, volume, variety) and essential skills for data scientists, including statistical knowledge, programming abilities, and data literacy. The chapter concludes by addressing critical ethical concerns around privacy, bias, and fairness in data science practice.