This chapter looks into the fundamental concepts and definitions in Network Science, starting with an in-depth introduction to the foundational elements of graphs, their notations, and matrix algebra essentials. The importance of handling network data is underscored with a comprehensive review of methods for importing and generating such data. The chapter further explores advanced concepts including the interpretation and transformation of bipartite networks into one-mode projections, enhancing the understanding of the structural properties of networks. A central focus is given to network visualization techniques, elucidating the importance of visual aids in discerning complex network patterns. Incorporating the unique perspective of textual data, the chapter introduces the emerging field of text networks, and demonstrates how Network Science can be applied to natural language data. In all these sections, practical implementations are explored using three widely adopted statistical software packages: Stata, R, and Python, ensuring the acquired knowledge can be readily put into practice. The chapter closes with a relevant case study, which investigates text networks applied to European Central Bank speeches related to the climate crisis. The section concludes by blending theory with practice, providing readers with a multifaceted understanding of Network Science.