Data Science Program

About the Course

Program Aims To
• Empower the next generation of leaders
• Offer an open learning experience and give back to society
• Provide immersive training, mentorship, and hands-on project work
What is this program for
• Train : Provide comprehensive, hands-on reskilling/ upskilling in cutting-edge technologies
• Educate : Deliver industry-aligned educational programs
• Innovate : Foster innovation through real-world projects and applications
Target Audience
• Engineering graduates and tech professionals
• Faculty members of academic institutions seeking to enhance their instructional repertoire
• Individuals looking to transition into high-demand technology careers
Program Philosophy
• Hands-on Learning: Real-world datasets and practical applications
• Expert Mentorship: Guidance from 30+ seasoned industry mentors
• Technical Excellence: Meaningful problems that blend statistics, code, and context
• Real-world Projects: 30+ industry-relevant projects applying various technologies
Common Program Features
• Individuals with full 6-month availability
• Basic understanding of programming and mathematics (varied by program)
Delivery Mode
• Online delivery with live sessions
• Compulsory attendance for live sessions
• Time commitment: Minimum 48 hours per week
Selection Process
• Registration and awareness workshops
• Aptitude test
• Final interview
• Onboarding for successful candidates
Program Timings
• Live sessions commence at 8:00 AM
• 2-3 classes daily, each lasting 1-1.5 hours
• Weekly catch-up sessions with mentors
• Expert sessions (compulsory attendance)
What you'll Learn

Syllabus / Curriculum

• Module 1: Module 1: PYTHON PROGRAMMING

  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.
• Module 2: Module 2: SQL (STRUCTURED QUERY LANGUAGE)

  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.
• Module 3: Module 3: MACHINE LEARNING

  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.
• Module 4: Module 4: DEEP LEARNING

  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.
• Module 5: Module 5: NATURAL LANGUAGE PROCESSING

  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.
• Module 6: Module 6: DATA STRUCTURES AND ALGORITHMS

  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.
• Module 7: Module 7: DATAIKU

  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.
• Module 8: Module 8: PASSION PROJECT

  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.

Eligibility Criteria

Selection Process

ⓘ Shortlisted candidates will be notified via Email and SMS. Admit cards available on dashboard.

Fee Structure

* Scholarships may be available via NSP. Fee once paid is non-refundable except in cases of centre cancellation.

Faculty & Industry Partner

Centre Details