 This is an introduction video about learning antics tools course. So due to Web 2.0, every user is generating a lot of data, also we can see data in our official work also in our social media services. For example, you can see Facebook, or Instagram, Twitter, we generate a lot of data. What can be done with this user? There have been a lot of organizations trying to extract value from this data. Despite they are very huge, they use cloud and AI and machine learning algorithms to create useful recommendations and they make useful influences from this data. For instance, video streaming websites like Netflix or Prime Video, analyze the users' watching behavior, viewing behavior, patterns, likes, dislikes, the popular shows, they can make recommendations to what we do to watch next. Also, based on the popularity among the different age group and which episodes have been watched more times or which movies watched more times, they can recommend to the directors what is the expectation of the viewers. So, directors can make a successful web series. In future, we can use big data of DNA to determine the perfect treatment for diseases like cancer. We might. Like other domains, in education domain also, we are generating a lot of data. We do search in education labs or learning management systems like Moodle or Google Classrooms, MOOCs, a lot of MOOCs courses, people register and they participate in certifications or library management systems. So, we have a lot of data generated in education domain also. So, can we do something with this huge data generated in educational domain? The focus of this course is about how to use data from educational environments, how to make inference, so that we can help the learner to learn better. So, in this course, we'll talk about different types of data and techniques applied on educational data. First, I'll start with data collection from different environments. Then, we'll talk about pre-processing of data in a special format. And we'll introduce a lot of tools like Microsoft Excel, how to use Excel for data analytics, and VECA, open source ML tool, orange commercial software, but free for educational use, it's also ML tool. Process mining software, Pro-EM, Adminer, to identify the frequent sequence of actions, and ISAC for the better visualization. We'll introduce these tools in this course. Also, we'll introduce a couple of ML algorithms so that you can, you better understand the record and orange algorithms. In this course, we will have four to five mini projects in PoliExpair. This mini project gives you and some experience of how to use these tools and how to apply the educational data and these tools to get some results. We will provide the data for these projects. So, who can take this course? So, this course is open for you and PEG for all students. But if the teachers were interested to understand the learners' learning behavior and they want to improve the teaching learning strategy to help the learner to learn better, this course will be suitable. Also, for students who are interested in data analytics or in educational data, this course might be interesting. Also, for the employees in the analytics sector, this course might be interesting. So, is there any prerequisites for this course? Since this course is open for everyone, UG, PEG, any department, we made sure this course has no prerequisites. And also, no programming skill list required for the assignments and to understand this course. However, having a script language, knowing script language is better because so that you can collect your own data and you can write scripts to format the data in a specific format and you can use these tools. The expert, the students have basic mathematics understanding. So, we'll see you all in the Learning Analytics Tools course.