2 Weeks
6 Online Lessons
Certificate of Completion
BCA in
Applied AI & Data Analytics
Programme overview
The BCA in Applied AI & Data Analytics is a future-focused undergraduate program that blends the power of Artificial Intelligence, Machine Learning, and Data Analytics to equip students with the skills to transform data into actionable insights. Designed under a Work-Integrated Learning Model, the program bridges academic concepts with real-world industry applications, ensuring learners are job-ready from day one.
Students gain in-depth knowledge of AI algorithms, predictive analytics, big data tools, and visualization techniques, while also mastering the fundamentals of programming, databases, and business intelligence. The curriculum emphasizes hands-on learning through live projects, internships, and mentorship from industry professionals, allowing learners to apply AI and analytics techniques to real-world business and technology challenges. Graduates emerge as skilled professionals ready to take on roles such as Data Analyst, Machine Learning Engineer, AI Developer, and Business Intelligence Specialist, driving innovation and decision-making in data-driven organizations.
Curriculum Structure
- Python Fundamentals & Data Structures
- Computer Organization & Architecture
- Statistical Methods for Data Science
- Probability & Vector Spaces
- Intro to Data Visualization
- SQL & Database Design for Analytics
- Advanced Python for Data Engineering
- Exploratory Data Analysis
- Programming Practices using Java
- Machine Learning & Applied AI Models
- Work Integrated learning begins
- Data Cleaning & Wrangling Techniques
- Feature Engineering & Model Selection
- Predictive Analytics & Evaluation Metrics
- Industry Case Study – Forecasting and Trends
- Deep Learning Fundamentals (CNNs, RNNs)
- Natural Language Processing Essentials
- Visualization Dashboards
- Big Data Processing (Hadoop, Spark)
- Cloud Analytics (GCP, AWS)
- Mobile Computing
- MLOps Pipelines & Version Control
- Project
- Data Governance & AI Compliance
- Data Governance AI Compliance
- Capstone project
Course Highlights
Real-World Datasets from Industry Partners
Work with authentic datasets provided by industry collaborators to gain practical experience in solving real business and technology challenges.
End-to-End Analytics Project Pipeline
Learn to manage the complete analytics lifecycle — from data collection and processing to model building, visualization, and insight generation.
Focus on Data Ethics and Responsible AI
Understand the ethical dimensions of AI and analytics, ensuring your solutions are transparent, fair, and socially responsible.
Career Pathways
This specialization prepares students to transform data into intelligence and insight into impact. Graduates gain deep expertise in analytics, machine learning, and applied AI, enabling them to solve real-world problems using data-driven decision-making. They are equipped to work across domains such as business intelligence, finance, retail, and research, helping organizations predict trends, optimize processes, and innovate faster. With a strong foundation in data science tools and responsible AI practices, students become industry-ready professionals who can design predictive models, automate insights, and communicate analytics effectively to decision-makers driving growth and innovation in every sector where data powers progress.
- Data Scientist
- Data Analyst
- BI Developer
- ML Engineer
Frequently Asked Questions
How does Applied AI differ from traditional Artificial Intelligence studies?
Traditional AI centers on theory, while Applied AI focuses on deploying machine learning and deep learning models for real-world problems across industries, bridging research and application.
What role does Data Analytics play in this specialisation?
Data Analytics is central to AI decision-making. Students gain proficiency in gathering, cleaning, and interpreting data using tools like Python and SQL, turning raw information into actionable insights.
How is the Work-Integrated model beneficial for AI & Analytics learners?
Internships, live projects, and mentorship enable students to directly apply their knowledge to real industry data and challenges, thereby building both technical and domain expertise before graduation.
What kind of professional roles and domains can graduates explore?
Graduates pursue roles like AI Analyst, Data Scientist, and Machine Learning Engineer. Their combined AI skills and industry experience prepare them for finance, healthcare, and technology sectors.