1. "Introduction to Probability and Data" by Duke University: This course is designed to provide a foundational understanding of probability and statistical concepts and techniques. It covers topics such as probability, statistical inference, and data visualization.
  2. "Applied Data Science with Python" by University of Michigan: This course covers the fundamentals of data science, including statistical analysis, data visualization, and machine learning. It is suitable for those with some programming experience, as it uses Python as the primary programming language.
  3. "Data Science Essentials" by IBM: This course is designed to provide a broad overview of data science, including statistical analysis, data visualization, and machine learning. It is suitable for those with little or no experience in data science.
  4. "Data Science Methodology" by IBM: This course covers the principles and processes of data science, including statistical analysis, data visualization, and machine learning. It is suitable for those with some background in data science.
  5. "Introduction to Data Science" by Johns Hopkins University: This course covers the fundamentals of data science, including statistical analysis, data visualization, and machine learning. It is suitable for those with little or no experience in data science.
  6. "Introduction to Data Science in Python" by University of Michigan: This course covers the fundamentals of data science using Python as the primary programming language. It covers topics such as statistical analysis, data visualization, and machine learning.
  7. "Introduction to Data Analysis using Excel" by Microsoft: This course covers the basics of data analysis using Excel, including how to import, clean, and analyze data. It is suitable for those with little or no experience in data analysis.
  8. "Introduction to Statistical Thinking" by Stanford University: This course covers the fundamentals of statistical thinking, including probability, statistical inference, and data visualization. It is suitable for those with little or no experience in statistics.
  9. "Introduction to Big Data" by University of California, San Diego: This course covers the basics of big data, including how to process and analyze large datasets using tools such as Hadoop and Spark. It is suitable for those with little or no experience in big data.
  10. "Applied Machine Learning" by University of Michigan: This course covers the principles and techniques of machine learning, including supervised and unsupervised learning, decision trees, and neural networks. It is suitable for those with some background in machine learning. I hope these recommendations are helpful. If you have any specific questions about statistics or data science, please don't hesitate to ask.