Advanced Certificate Data for Social Change: High-Performance Strategies

• Advanced Data Analysis: This unit covers advanced statistical techniques and data analysis methods, including regression analysis, time series analysis, and multivariate analysis.
• Data Visualization: This unit explores the latest tools and techniques for visualizing data, including Tableau, PowerBI, and ggplot. Students will learn how to create compelling visualizations that communicate complex data insights effectively.
• Machine Learning: This unit covers the fundamentals of machine learning, including supervised and unsupervised learning, predictive modeling, and model evaluation. Students will learn how to apply machine learning techniques to large datasets to uncover hidden patterns and relationships.
• Big Data Management: This unit covers the challenges and opportunities of working with big data, including data management, data governance, and data security. Students will learn how to use tools like Hadoop and Spark to process and analyze big data.
• Data Ethics: This unit explores the ethical considerations of working with data, including privacy, confidentiality, and informed consent. Students will learn how to navigate ethical dilemmas and make responsible decisions when working with data.
• Data Storytelling: This unit covers the art and science of data storytelling, including how to frame data insights, create compelling narratives, and communicate data insights effectively to different audiences.
• Data for Social Impact: This unit explores how data can be used to drive social change, including how to identify data-driven opportunities for social impact, measure and evaluate impact, and communicate data insights to stakeholders.
• Data Science Workflow: This unit covers the end-to-end data science workflow, including data cleaning, data preparation, data modeling, and data visualization. Students will learn how to manage the data science process effectively and efficiently.
• Advanced Data Tools: This unit explores the latest data tools and technologies, including natural language processing, computer vision, and deep learning. Students will learn how to apply these tools to real-world problems and datasets.
• Data-Driven Decision Making: This unit covers the principles of data-driven decision making, including how to use data to inform strategic decisions, how to evaluate the impact of decisions, and how to build a data-driven culture within an organization.