Data Science With Artificial Intelligence

Being a successful student requires understanding how you learn, identifying your values, and setting clear goals. It also means mastering time and financial management, developing strong note-taking and test-taking skills, collaborating effectively, and leveraging technology to enhance your efficiency. In this course, you will learn essential communication, writing, and presentation skills.

Additionally, you will focus on financial literacy through the Enriched Academy program, designed specifically for Oxford College students, to help you confidently manage your finances.

This module provides a foundational understanding of key mathematical principles necessary for data analytics. Students will develop logical thinking and problem-solving skills while exploring algebra and linear algebra, essential for analyzing and interpreting data in machine learning and data science. Topics covered include vector and matrix operations, statistical analysis, data mining, natural language processing, and computer vision. By the end of this module, students will be equipped with the mathematical tools needed to understand and apply data science algorithms effectively.

Calculus is a critical mathematical tool in data science, providing the framework for optimization, probability distributions, and more. This module delves into calculus applications in data analysis, neural networks, and gradient descent. Students will gain essential knowledge to analyze complex datasets, build models, and extract insights, preparing them for advanced study and practical applications in the field of data science.

Statistics is fundamental to data science, offering tools for understanding and interpreting data. This module covers descriptive and inferential statistics, probability theory, and regression theory, which are essential for machine learning, data mining, and predictive analytics. Students will explore how statistics help data scientists extract insights, make decisions, and apply statistical methods in various domains, preparing them for real-world data science challenges.

This module introduces students to programming, focusing on Object-Oriented Programming (OOP). OOP models real-world entities as objects, helping developers organize and manage software projects effectively. Students will learn the basics of programming and how to apply OOP principles to build scalable and efficient data science applications, equipping them with essential skills for programming in a data-driven world.

Database management is essential for data storage, organization, and retrieval in data science. Students will learn techniques for data integration, governance, warehousing, and real-time analytics. The module will also cover the management of large datasets and the application of machine learning. By the end, students will be equipped to manage databases efficiently and ensure data quality to support data-driven decision-making.

Data analysis involves examining data to discover patterns, trends, and insights. This module covers data cleaning, preparation, and exploratory data analysis techniques. Students will learn to apply these methods to solve real-world problems in fields like healthcare, finance, and marketing. By the end of the module, students will be able to use data analysis effectively to drive decisions and uncover valuable insights.

This module introduces the basics of Artificial Intelligence (AI) and Machine Learning (ML), including their real-world applications. Students will explore how machine learning algorithms enable computers to learn from data and improve performance without explicit programming. Key topics include automation, pattern recognition, and prediction, highlighting ML’s impact on efficiency and innovation in various industries.

This module focuses on Natural Language Processing (NLP), which enables computers to interpret and generate human language. Students will explore applications like sentiment analysis, text classification, machine translation, and chatbots. By the end of this course, students will be equipped to apply NLP techniques in data science for tasks such as information extraction and text summarization.

Data design ensures data is organized and accessible for analysis. In this module, students will explore techniques for data warehousing, data modeling, data security, and visualization. The course will focus on designing effective data structures and ensuring data quality to support analysis and decision-making. Students will also learn to implement data-driven strategies across various domains.

Data mining is the process of discovering patterns and insights from large datasets. This module covers techniques for identifying anomalies and trends in fields such as fraud detection, market analysis, and healthcare. Students will learn how to mine data to extract valuable knowledge and make informed decisions, applying these skills to real-world challenges.

Big Data Analytics involves analyzing large, complex datasets to extract insights. This module prepares students to handle and analyze big data in industries like healthcare, finance, and customer relationship management. Students will learn to use big data tools and techniques to uncover trends and leverage insights for strategic decision-making and competitive advantage.

This module explores the role of cloud computing in enabling scalable and cost-effective data science solutions. Students will learn to use core cloud services—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)—for data science initiatives. The course will focus on managing large datasets, deploying machine learning models, and implementing data governance in the cloud.

In this capstone module, students will bring together their knowledge of data science and artificial intelligence to solve a real-world problem using advanced data techniques. Drawing on skills from earlier modules, students will collect and analyze data, build and train AI models, and present actionable insights through visualizations and reports. This project demonstrates their ability to manage full data workflows and apply AI to generate intelligent outcomes.