Duration: 2 years
Career Prospects: Curriculum outline for an MCA program specializing in Data Science:
Foundation Courses:
• Introduction to Computer Science
• Data Structures and Algorithms
• Object-Oriented Programming with Python/R
• Mathematics for Data Science (Linear Algebra, Calculus, Probability, Statistics)
• Database Management Systems
Core Courses in Data Science:
• Introduction to Data Science
• Data Preprocessing and Cleaning
• Exploratory Data Analysis (EDA)
• Statistical Methods for Data Science
• Machine Learning Fundamentals
• Big Data Analytics
• Data Visualization
• Feature Engineering
• Model Evaluation and Validation
• Data Ethics and Privacy
Advanced Topics:
• Deep Learning
• Natural Language Processing
• Time Series Analysis
• Reinforcement Learning
• Graph Analytics
• Causal Inference
• Bayesian Methods
• Ensemble Learning
• AutoML (Automated Machine Learning)
• Anomaly Detection
Tools and Technologies:
• Python Data Science Stack (NumPy, Pandas, Matplotlib, Seaborn)
• R Programming
• SQL for Data Analysis
• Apache Hadoop and Spark
• TensorFlow and PyTorch for Deep Learning
• Scikit-learn for Machine Learning
• Tableau or Power BI for Data Visualization
• Jupyter Notebooks and Google Colab
Applications of Data Science:
• Predictive Analytics
• Fraud Detection
• Recommender Systems
• Healthcare Analytics
• Social Media Analytics
• Marketing Analytics
• Supply Chain Analytics
• Customer Segmentation
• IoT (Internet of Things) Data Analytics
Project Work and Case Studies:
• Real-world projects involving data analysis and predictive modeling
• Case studies analyzing data science applications in different industries
• Internship or practical training in data science companies or research labs
Electives:
Students might have the option to choose electives based on their interests, such as advanced topics in deep learning, natural language processing, computer vision, etc.