Master the complete data science workflow from programming fundamentals to advanced machine learning. Each module builds upon the previous one to create a comprehensive learning experience.
This course covers a solid foundation in Python programming, emphasizing practical skills essential for software development and data analysis. The curriculum encompasses Python essentials like data structures, object-oriented programming, and libraries such as Numpy and Pandas for data manipulation. Additionally, it delves into web scraping, REST API development, and asynchronous task handling using Celery.
Prerequisites and Eligibility: This course is open to learners from all educational and professional backgrounds. No prior programming experience is required - beginners and experienced learners alike can benefit.
This course offers a comprehensive foundation in SQL, focusing on practical skills essential for data querying, management, and analysis. The curriculum covers core SQL concepts such as database design, normalization, writing complex queries, joins, subqueries, and aggregations. Learners will also gain hands-on experience with relational database management systems (RDBMS) like MySQL or PostgreSQL.
Prerequisites and Eligibility: This course is open to learners from all educational backgrounds and skill levels. It is designed to support both beginners and those with prior experience in databases or programming.
This course provides a comprehensive introduction to data science and machine learning, covering supervised and unsupervised learning techniques including linear regression, decision trees, clustering algorithms, and ensemble methods. Participants learn model selection, evaluation, and optimization strategies, along with essential concepts in probability, statistics, feature engineering, and preprocessing for machine learning tasks.
Prerequisites and Eligibility: This course welcomes learners of all educational backgrounds and skill levels. However, proficiency in Python is a prerequisite, and a basic understanding of mathematics would be an advantage.
This course provides a comprehensive overview of deep learning, spanning from foundational concepts like neural networks to advanced techniques such as CNNs, RNNs, and GANs. Participants gain practical experience in gradient computation and explore applications like object detection and speech data processing.
Prerequisites and Eligibility: This course welcomes learners of all educational backgrounds and skill levels. However, proficiency in Machine Learning is a prerequisite, and a basic understanding of mathematics would be an advantage.
This course provides a thorough journey through the fundamentals and applications of Natural Language Processing (NLP), spanning from basic text processing techniques to advanced topic modeling methods. Additionally, it covers the practical insights into content recommendation systems and dimensionality reduction techniques for enhancing the understanding of real-world NLP challenges and solutions.
Prerequisites and Eligibility: This course welcomes learners of all educational backgrounds and skill levels. However, proficiency in Machine Learning is a prerequisite, and a basic understanding of mathematics would be an advantage.
This course progresses from fundamental concepts to more advanced topics in DSA, ensuring a smooth learning curve. Each topic is accompanied by practice problems and exercises to reinforce learning and problem-solving skills.
Prerequisites and Eligibility: This course welcomes learners of all educational backgrounds and skill levels. However, basic understanding of programming and mathematics is a prerequisite.
Dataiku certifications are highly regarded in the data science and analytics industry. This course will cover the fundamental concepts and functionalities of Dataiku DSS and guide you through the process of designing and building end-to-end data pipelines
Prerequisites and Eligibility: This course welcomes learners of all educational backgrounds and skill levels. However, basic understanding of Data Science is a prerequisite.
We provide opportunities to delve into passion projects with a focus on creating positive social change. Through these projects, learners will not only enhance their technical skills but also gain valuable experience in applying data science methodologies to address real-world social issues.
Prerequisites and Eligibility: For the passion project component, eligibility is limited to students enrolled in the full-time 6-month program, while those enrolled for individual courses are not eligible. This requirement ensures a comprehensive immersion in data science principles and practices, preparing participants to undertake impactful projects.