The course is the easiest training you can get to start your journey as a data scientist. We help you prepare your portfolio by having you do tabular data classifications, image classifications, and more during eight mini-projects. Learning Python, NumPy, pandas, matplotlib, Seaborn, TensorFlow, or PyTorch is good, but working on real projects for data science is essential.

A lot of classes talk about mathematics and advanced statistics, but those things are not easy for an absolute beginner to learn. This course won’t teach you math or statistics. Instead, we'll guide you through practical applications.

We are not saying math and statistics are not important. It is imperative that you understand those concepts to become a data scientist. But when you first learn how to drive, you don't need someone to teach you the mechanical details of how your car runs. What you need is someone who can sit next to you and guide you from your home to school or the grocery store. If you get familiar with that, then you will want to go further. In the same way, after you do our projects step-by-step, you will want to know more about the math or statistics behind all that code.

We will start with Python, and then NumPy & pandas for some data manipulation. Then we will learn data visualization, preprocessing, machine learning, and modeling for binary classification with tabular data. Next, we will learn regression and multi-class classification with scikit-learn modules. After that, we'll learn how to deal with unsupervised learning tasks like image classification, image generation, and so on.