Welcome to the Data Science Portfolio Builder course! This course is tailored for beginners who are eager to delve into the field of data science and build a solid portfolio of projects. This course covers everything that you need in order to prepare for your next interview. It consists of an end-to-end description of multiple Data Science projects, starting from explaining business aspects of running a Data Science solution, basic concepts of Data Science algorithms all the way up to training the model. Throughout the course, you will explore essential domains of data science, including time series analysis, natural language processing (NLP), computer vision, and graph neural networks (GNN). Each module is designed to provide you with a foundational understanding of these areas, progressing from basic concepts to practical applications. These are in-demand skills, something what the recruiters are looking for. By adding these projects, you would make your portfolio very versatile. I have included sensible projects in this course in terms of practicality, and not something which you might see on the internet, like a "Stock Price Predicting application". These projects are relevant in the industry and recruiters are looking to hire people who have a knowledge of them. By completing these projects, you will not only gain valuable experience but also build a portfolio that showcases your proficiency in data science to potential employers, even if your interview is next week! Whether you're a student, a career changer, or simply interested in expanding your skill set, this course will equip you with the necessary tools and knowledge to succeed in the dynamic field of data science. Enroll now and take the first step towards building your data science portfolio!
TO MAC USERS: If RAR password doesn't work, use this archive program:
RAR Expander 0.8.5 Beta 4 and extract password protected files without error.
TO WIN USERS: If RAR password doesn't work, use this archive program:
Latest Winrar and extract password protected files without error.