“Statistics with Python Using NumPy, Pandas, and SciPy” explores how to apply statistical and mathematical techniques to data science problems.
Throughout the first half of the course, you’ll work on reviewing vector dot products, interpreting text as vectors, and matrix multiplication. You’ll also explore the basics of probability, laying the groundwork for statistical analysis. In the second half, you’ll cover how to interpret data distributions, reason about probability, explore the special properties of normal distributions, understand linear relationships in data, and the connection between probability and uncertainty.
This is the third course in the four-course series “Data-Oriented Python Programming and Debugging,” where you’ll work to strengthen your programming capabilities and enhance your problem-solving skills.
Statistics with Python Using NumPy, Pandas, and SciPy
University of Michigan via Coursera
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Overview
Syllabus
- Vector Operations and Text Representation in Data Science
- Understanding and Visualizing Data Distributions
- Understanding and Analyzing Data Distribution Characteristics
- Sampling Methods and Statistical Inference
Taught by
Elle O'Brien, Anthony Whyte, and Paul Resnick