The growing prominence of data-driven decision-making across scientific, economic, and social domains has intensified the demand for graduates possessing strong competencies in statistics, data literacy, and data science. In response, higher education institutions are increasingly revising curricula and instructional practices to incorporate data science and statistical education across diverse academic disciplines. This study presents a systematic review of the scholarly literature examining pedagogical approaches for teaching data science and statistics in higher education. The review aims to synthesize existing research on instructional strategies, curriculum design, and the integration of computational tools that support the development of analytical and data-oriented competencies among university students. To ensure methodological rigor, a structured literature search was conducted using the Scopus database, focusing on peer-reviewed publications addressing statistics education, data science pedagogy, and data literacy within university-level contexts. The retrieved studies were screened and selected based on predefined inclusion and exclusion criteria, enabling the identification of relevant pedagogical patterns and emerging instructional practices. The analysis reveals several dominant approaches within the literature, including project-based learning, inquiry-oriented statistics education, the use of real-world datasets for applied data analysis, and the integration of programming environments that support computational thinking and data exploration. Furthermore, recent research highlights the growing role of digital tools and interactive technologies in facilitating data visualization, collaborative learning, and experiential engagement with complex datasets. Overall, the findings indicate a gradual pedagogical shift from traditional lecture-based instruction toward more applied, interdisciplinary, and technology-enhanced learning environments. This review contributes to a deeper understanding of evolving teaching practices in data science and statistics education and identifies key directions for future research in higher education pedagogy.