The Data Science pre-work covers introductory Data Science concepts. By the end of pre-work, you will be prepared to dive into the course material and you will be at the same level as your coursemates. You will also learn about Data Science Principle and dive deep into python programming.

In this phase, students will learn the fundamentals of Python for Data Science, including the use of Jupyter Notebooks and key libraries like Numpy and Pandas. They’ll explore data structures, relational databases, and SQL for querying structured databases, while also gaining skills in accessing data via APIs and web scraping. By the end, students will be able to collect, organize, and visualize data to deliver actionable insights.

In phase 2, students learn about the fundamentals of probability theory like combinations and permutations. They also learn about statistical distributions and how to create samples, then apply this knowledge by running A/B experiments. At the end of this phase, students will be able to build their first data science model using linear regression.

Students will go through training with our professional development trainers in leading self, working with others, project management, career readiness, communicating for impact & Entrepreneurial Thinking

In this phase, students dive into machine learning with a strong focus on supervised learning. They begin with regression analysis, including logistic regression, and learn key techniques such as regularization to prevent overfitting and cross-validation for model validation. By the end, students will be able to build and implement essential machine learning techniques effectively.

Students get a 1-week break to relax and boost energies to complete the remaining modules.

This phase of the course explores various Data Science techniques, focusing on unsupervised learning methods such as clustering and dimensionality reduction. Students will be introduced to threading and multiprocessing to handle big data, learn to use tools like PySpark and AWS to build recommendation systems. Additionally, they will delve into deep learning and neural networks, gaining skills in performing sentiment analysis.

In the final project, learners will work individually or in groups to apply their technical and soft skills in a comprehensive data science or machine learning project. This project offers an in-depth opportunity to showcase their learning achievements and experience the process of working on a large-scale data science initiative, allowing students to integrate their knowledge and demonstrate their capabilities in a practical setting.