This course is part of Data-Oriented Python Programming and Debugging Specialization

Instructors: Elle O'Brien +2 more

What you'll learn

  •   Use vector operations in NumPy for applied mathematics.
  •   Visualize and analyze data distributions using NumPy and SciPy.
  •   Use statistics to describe patterns in data distributions.
  •   Conduct statistical inference using hypothesis testing with computational methods.
  • Skills you'll gain

    There are 4 modules in this course

    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.

    Understanding and Visualizing Data Distributions

    Understanding and Analyzing Data Distribution Characteristics

    Sampling Methods and Statistical Inference

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