Course Overview: The Bachelor of Science in Data Science is a comprehensive program designed to immerse students in the rapidly evolving world of data analysis, machine learning, and artificial intelligence. This course provides a solid foundation in statistical analysis, data visualization, and computational techniques, all while emphasizing real-world applications across various industries. Graduates will be well-prepared to harness the power of data to drive decision-making in business, healthcare, finance, and beyond.

Program Objectives:

1. Provide a deep understanding of statistical and computational methods for data analysis.
2. Equip students with the skills to manage, visualize, and interpret large datasets.
3. Foster expertise in machine learning and predictive modeling.
4. Promote interdisciplinary collaboration, ethical data practices, and effective communication skills.

Course Structure: The program spans over six years, divided into twelve semesters, emphasizing both theoretical knowledge and hands-on experience.

Years 1-2: Introduction to Data Science, Programming for Data Science, Linear Algebra, Probability and Statistics, Data Visualization.

Years 3-4: Machine Learning, Database Management, Advanced Statistical Methods, Big Data Analytics, Data Ethics and Privacy.

Years 5-6: Deep Learning, Time Series Analysis, Natural Language Processing, Data Engineering, Capstone Project in Data Science.

Years 1-2:

1. Introduction to Data Science Exam
   • Overview of data science concepts and applications
   • Roles and responsibilities of a data scientist
2. Programming for Data Science Exam
   • Fundamental programming concepts in Python or R
   • Scripting and automation for data tasks
3. Linear Algebra Exam
   • Matrices, vectors, and linear transformations
   • Eigenvalues and eigenvectors
4. Probability and Statistics Exam
   • Basic probability theory and distributions
   • Hypothesis testing and inferential statistics
5. Data Visualization Exam
   • Principles of effective data visualization
   • Tools and techniques for creating visualizations

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Years 3-4:

6. Machine Learning Exam
   • Supervised and unsupervised learning methods
   • Model evaluation and optimization
7. Database Management Exam
   • Database design and normalization
   • SQL and NoSQL databases
8. Advanced Statistical Methods Exam
   • Regression analysis, ANOVA, and multivariate analysis
   • Non-parametric methods
9. Big Data Analytics Exam
   • Handling and processing large datasets
   • Distributed computing frameworks like Hadoop and Spark
10. Data Ethics and Privacy Exam

• Ethical considerations in data science
• Data privacy regulations and best practices

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Years 5-6:

11. Deep Learning Exam
    • Neural networks and backpropagation
    • Convolutional neural networks, recurrent neural networks, and transfer learning
12. Time Series Analysis Exam
    • Forecasting and trend analysis
    • Autoregressive and moving average models
13. Natural Language Processing Exam
    • Text preprocessing and tokenization
    • Sentiment analysis and topic modeling
14. Data Engineering Exam
    • Data pipelines and ETL processes
    • Data warehousing and cloud storage solutions
15. Capstone Project Presentation
    • Comprehensive research or field project in data science
    • Addressing a current challenge or innovation in the data science field

1. High school diploma or equivalent with strong performance in mathematics and computer science.
2. Letters of recommendation, preferably from math or computer science educators.
3. A personal statement detailing the applicant’s interest in data science and any relevant experiences.
4. An interview may be conducted to assess the applicant’s understanding and passion for the field.