Fundamentals of Data Science Certificate Online: Courses

This course introduces the Python programming language along with data analysis and exploration techniques. Topics covered include the fundamentals of Python programming, visualization, and exploratory data analysis using key libraries such as NumPy, Seaborn, Pandas, and Matplotlib.

This course provides a comprehensive understanding of fundamental statistical concepts that are essential for data analysis. The course explores basic statistical concepts including data modeling, random variables and hypothesis testing, clustering, principal component analysis, linear models, logistic regression, and analysis of variance. The course tackles common mistakes and issues in data analysis such as causality vs. correlation, confounders, statistical artifacts, Simpson’s paradox, base rate fallacy, stage migration, survivorship bias, censoring, and misleading visualization.

This course introduces the R programming language for statistical computing and data analysis. Participants will learn the fundamentals of R programming, data manipulation, visualization, and statistical analysis techniques.

Data science enables us to process big amounts of structured and unstructured data to detect patterns and perform in-depth and conclusive analysis. This course covers the main techniques involved in the data processing pipeline, including data capture (scraping, cleaning, and filtering), feature engineering (representation, selection, and transformation), data augmentation (knowledge-based and corpus-based), data mining (regression analysis and predictive modeling), and data visualization (search and exploration). Real-life applications will be considered including search engines, text summarization, text auto-correction, chat bots, personal assistants, social network analysis, sentiment analysis, and event detection, among others. Students will learn about advanced AI techniques and tools used on these applications.

This course covers the essential and practical skills necessary to communicate information about data clearly and effectively through graphical means based on principles from graphic design, visual art, perceptual psychology, and cognitive science. The course introduces the value of visualization, the principles and techniques of data visualization, and specific techniques in information and scientific visualization. The course also focuses on big data organization and mining for decision support, and how to best leverage visualization methods. The assignments will involve the use of Tableau, Seaborn, Dash, Folium, Matplotlib, and the ability to manipulate data sets with code.

This course serves as an introduction to fundamental ethics concepts essential for the development of improved intelligent systems, while exploring their implications on the economy, civil society, and government. Topics include ethical data sourcing, data bias mitigation, explainability versus black-box AI, economic equity considerations, and data governance including privacy, security, and stewardship. Special focus is placed on the pivotal data and modeling choices made by developers during system creation and deployment, along with their societal ramifications.