The popularity of data science jobs in India has leapfrogged over the past five years or so. Its successful run in the industry can be attributed to better research, project implementations and the general growth in big data. These developments have called out for techies trying to make a career in data science. \n\nWhile paid courses, full-fledged graduate programmes, or even online courses like Coursera, EdX, Udemy or Udacity, among others, are excellent resources for learning, these can be expensive for many. Even if most of the online courses mentioned above might be free, you might need to enrol for these courses beforehand or even require a membership.\n\nFor all of those who are looking for other alternatives, we bring to you few resources which are free to use in your own free time. This article lists five best courses and reference materials available on the internet, which are not only free but are also are downloadable without any strain on your pocket. \n\n1. Introduction to Computational Thinking and Data Science by MIT OpenCourseWare\n\nAn introductory course by Massachusetts Institute of Technology (MIT), this content material contains all the finer distinctions in data science for beginners. Being an actual course for computer science undergraduates, it covers concepts from statistics and machine learning from scratch. It has a strong emphasis on Python programming \u2014 the go-to language for data science implementations. On the other hand, optimisation and statistical concepts are also covered to focus on computational thinking for solving problems. \n\nYou can find the course material here.\n\n2. CS109 Data Science by Harvard University\n\nHarvard\u2019s CS109 Data Science is an exhaustive resource for preliminary data science. Mainly aimed at computer science students, it proposes the science of data in the form of five key facets:\n\n\n Data Transformation\n Data Management\n Exploratory Data Analysis\n Prediction methods\n Data Visualisation\n\n\nPython is the key language used for implementation. Since this is a course material for undergraduates, most of the content is presented in the form of lecture videos. With an emphasis on gaining insights from data, this course follows a top-down approach for understanding critical concepts in data science. \n\nYou can find the course material here.\n\n3. Learning From Data by California Institute of Technology\n\nThis machine learning online course by CalTech has a comprehensive take on the subject. With the content having a stern focus on theory as well as practice, it follows a storyline approach. ML has emerged as the top favourite among data science enthusiasts and this course will definitely help them get through the fundamental concepts underlying in ML. \n\nTutored by Professor Yaser Abu-Mostafa, the lectures are in the form of videos broken into 18 sections. You can find the complete list of videos here.\n\n4. The Open Source Data Science Masters by Clare Corthell\n\nRather than being a straightforward course, this site presents a comprehensive collection of useful data science resources. The reason it is listed here is that most of the links present in the site cover a large array of topics ranging from data science basics, mathematics, statistics, machine learning, programming and data visualisation. \n\nThis repository of resources also tells why a solid foundation of data science through open-source tools is essential to bridge the talent gap in the industry. You can find the link here.\n\n5. A Course in Machine Learning By Hal Daume III\n\nA standalone resource for machine learning, this introductory course by Professor Hal Daume III of the University of Maryland covers major topics in ML such as supervised learning, unsupervised learning, large margin methods, probabilistic modelling, learning theory and so on, in detail. \n\nThe approach taken by Daume in presenting the learning material follows on ideas rather than relying extensively on math. Backed by examples, this course material is also pedagogically organised for better understanding. \n\nYou can find the material here.