 Hi, everyone. I am Ramkumar Rajendran. This is the introduction video to learning analytics tools course. Due to huge amount of data that is from BEB 2.0, we generate a lot of data. We also consume a lot of data in office also in personal use. For example, there are 1.66 billion daily active users in Facebook or 55 million pictures are uploaded every day in Instagram. So, with this huge data which we produce also we consume, can we do something with this huge data? Yes, there are some organizations using this data to extract meaningful value and use this value to make recommendations to the users and reduce the users work. For example, video streaming website like Netflix or Prime Video, analyze the data of their viewers and watching pattern and the likes this like what is the popular show, which part of the show they like it, all this information is used to recommend the next set of videos for the users. Also interestingly, Netflix has started using these data to inform the directors to make what kind of videos are most popular among the viewers so that they can make a successful TV shows or TV series or the movies. Maybe in future, we might use big data of DNA to determine the perfect treatment to cure disease like cancer by using data not from medical professionals. Maybe medical professionals plus a data engineer can create a cure for cancer. That is interesting. Similar to the other domain, in education domain also we produce lot of data. For example, we have modal learning management systems like modal blackboard or we use Google Classroom or we are using MOOCs to learn many things and also there are lot of other softwares for library management, content management for the maintenance schools or college. So, which means we produce lot of data in educational domain also. Can we use these data to provide some meaningful information to improve the teaching learning process? That is the focus of this course. So, what we do in this course, we will start with the data collection from different learning environments. For example, a classroom environment on the MOOC or in a internship tutoring systems, what data to collect, how to collect such data. Then we will talk about how to process this data in a specific format. Then we move on to use tools such as Excel, Vekha. Excel is a property software. You can use Google Sheet or some other open source version equivalent to Excel. Vekha is a machine learning tool developed by University of Vicad to New Zealand free for academic and commercial usage. Orange is similar ML tool but is for commercial software. Orange is available free for academic usage. Then we also talk about tools like a process mining, pattern miner and ISAT. In this course, you will apply these tools on the data collected from the learning environments. Also, we will talk about some of the ML algorithms like a linear regression classification algorithms. And in this course, you will do 4 to 5 mini projects in 12 weeks. This is to get hands-on experience of using these tools, using the learners data or creating the learning environment. So, who are the audience for this course? So, teachers with interest in understanding learners and they want to improve the teaching learning strategies, this course will be useful. Also for students who are interested in analytics, also in educational data, also they want to start a wonderful career in data science, this can be the introduction course. Employees in the tech sector, the employees who are working in education industries, this course might be useful. So, what is the prerequest? I mentioned that this course as ML algorithms, some tools, but absolutely this course has no prerequest, which means anyone can take this course. And we do not expect you to have a programming skill. However, if you want to collect your own data and convert them into a features which is applicable to ML tools, we expect you to know some scripting language, simple coding in a script language like a Python or JavaScript. But it is not necessary to complete this course. Knowledge on basic mathematics is essential to understand the machine learning algorithms. If you understand the machine learning algorithms, it will help you to use the tool effectively and create a models, more meaningful models. Thank you.