Bachelor’s in Data Science

During this module, learners dive deeper into C programming (including Shell scripting), followed by intermediate-level data structures and algorithms. Students will also begin learning Python and the fundamentals of machine learning, including regression, training datasets, structured and unstructured data, as well as data collection, visualization, and storage.

They will gain a solid foundation in back-end development, covering pointers, arrays, strings, algorithms, hash data structures, software architecture, blockchain basics, and more.

Key Topics: C, IDE setup, Python, PyTorch, Jupyter

Students transition into Python and machine learning fundamentals, exploring regression, training datasets, structured and unstructured data, as well as data collection, visualization, and storage. The module also introduces cloud-based tools used for machine learning.

Key Topics: Python, Pandas, TensorFlow, Keras, Jupyter

After mastering Python, students will move into advanced machine learning, working with large and complex datasets, understanding business fundamentals, and learning how to leverage data to improve core business outcomes. The module explores how data can become a strategic asset for companies.

Students will complete several large-scale, real-world projects to strengthen their practical skills. The goal at this stage is to learn how to enhance business performance and apply data as a key business resource.

Key Topics: Python, Pandas, TensorFlow, Keras, Jupyter

Learners will develop a final capstone project aligned with the industry field they aim to work in. This project will span approximately five months and will involve a high level of technical complexity.

In addition, students will participate in mock technical interviews to prepare for real-world job applications and strengthen their presentation and problem-solving skills.