Description: Social media sites such as Twitter and Facebook have connected billions of people and given the opportunity to the users to share their ideas and opinions instantly. That being said, there are several ill consequences as well such as online harassment, trolling, cyber-bullying, fake news, and hate speech. Out of these, hate speech presents a unique challenge as it is deep engraved into our society and is often linked with offline violence. Social media platforms rely on local moderators to identify hate speech and take necessary action, but with a prolific increase in such content over social media many are turning toward automated hate speech detection and mitigation systems. This shift brings several challenges on the plate, and hence, is an important avenue to explore for the computation social science community. In this translation style tutorial, we present an exposition of hate speech detection and mitigation in three steps. First, we shall describe the current state of research in the hate speech domain, focusing on different detection and mitigation systems that have developed over time. Next, we shall highlight the challenges that these systems might carry like bias and lack of transparency. The final section will concretize the path ahead, providing clear guidelines for the community working on hate speech and related areas. We shall outline the open challenges and research directions for interested researchers. Here, we plan to cover the following topics, i) How is hate speech affecting different platforms? ii) Existing dataset, iii) Text-based hate speech systems, iv) User-based hate speech systems, v) How we can mitigate or slow down the process of spread of hate speech, vi) What are the challenges still present in the domain, e.g.: Explainability and bias, Multimodal and multilingual challenges etc, in the tutorial.