Automated Machine Learning (AutoML) has revolutionized the field of data science by automating the model development process, making machine learning accessible to non-experts and improving efficiency of experts. Deep learning models are becoming increasingly popular in interpreting complex data, turning unstructured data into actionable insights. They are widely applied in voice recognition, image classification, and natural language processing amongst various industries. Explainable AI (XAI) is emerging as a necessary component in data science. As machine learning models become more complex, the demand for understanding these models and interpreting their decisions becomes crucial. Edge computing has transformed the way data is handled, processed, and delivered from millions of devices around the world. By bringing computation and data storage closer to the location where it is needed, edge computing reduces latency and increases response rate. Data privacy and ethics have been brought to the forefront. Scientists are under increasing pressure to ensure that data usage is transparent, secure, and respectful to user privacy. Graph analytics is on the rise, unlocking complex patterns in data and providing critical insights that were previously overlooked. It has been used in social network analyses, fraud detection, and complex network topology. Reinforcement learning has made breakthroughs, particularly in the field of gaming and robotics. By experimenting in a structured environment, reinforcement learning provides automated decision-making capabilities. The year 2024 is a beacon on the path of progress in the field of data science. The landscape shows promising trends that would further disrupt the way we live, work and play.