Learn to process, analyze, and visualize large datasets for data-driven decision-making.
Fundamentals of Data Science
This section introduces key concepts in data science, including data collection, cleaning, and preprocessing. Students will learn about different types of data (structured vs. unstructured) and explore fundamental statistical techniques used in data analysis. Hands-on exercises will involve working with tools such as Python, SQL, and Jupyter Notebooks.
Machine Learning and Predictive Analytics
Students will delve into machine learning techniques, including regression models, clustering, and classification algorithms. The course will explore how predictive analytics is used in industries such as finance, healthcare, and marketing. Real-world datasets will be used to build and evaluate machine learning models, emphasizing the importance of data-driven insights.
Big Data and Ethical Considerations
The final section covers big data technologies such as Hadoop, Spark, and cloud computing. Students will examine ethical issues in data science, including algorithmic bias, privacy concerns, and responsible AI. Discussions will focus on regulatory frameworks such as GDPR and the role of data scientists in ethical decision-making.
