Statistics with Python Using NumPy, Pandas, and SciPy
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Statistics with Python Using NumPy, Pandas, and SciPy
This course is part of Data-Oriented Python Programming and Debugging Specialization
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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.
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Tools you'll learn
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There are 4 modules in this course
“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.
What's included
6 videos2 readings1 programming assignment1 discussion prompt2 ungraded labs
6 videos•Total 61 minutes
- Welcome to the Course and Specialization•7 minutes
- Welcome to 'Course 3'•2 minutes
- Vector Dot Products - Code•12 minutes
- Text as Vectors - Code•10 minutes
- Matrix Multiplication•16 minutes
- Debugging Challenge•14 minutes
2 readings•Total 15 minutes
- Course Syllabus•10 minutes
- Help Us Learn About You•5 minutes
1 programming assignment•Total 120 minutes
- Assessment 1•120 minutes
1 discussion prompt•Total 10 minutes
- Introduce Yourself•10 minutes
2 ungraded labs•Total 60 minutes
- Jupyter Lab Environment•0 minutes
- Jupyter Lab 1•60 minutes
What's included
4 videos1 assignment1 programming assignment2 ungraded labs
4 videos•Total 77 minutes
- Basic Probability with a Bernoulli•18 minutes
- Discrete Data Distributions•23 minutes
- Continuous Data Distributions•26 minutes
- Debugging Challenge•10 minutes
1 assignment•Total 30 minutes
- Assessment 2.1•30 minutes
1 programming assignment•Total 120 minutes
- Assessment 2.2•120 minutes
2 ungraded labs•Total 60 minutes
- Jupyter Lab Environment•0 minutes
- Jupyter Lab 2•60 minutes
What's included
6 videos1 programming assignment2 ungraded labs
6 videos•Total 90 minutes
- Learning From Data Distributions•11 minutes
- Reasoning About Probability from a Distribution•13 minutes
- Special Properties of Normal Distributions•15 minutes
- Linear Relationships in Data•16 minutes
- Probability and Uncertainty•19 minutes
- Debugging Challenge•15 minutes
1 programming assignment•Total 120 minutes
- Assessment 3•120 minutes
2 ungraded labs•Total 60 minutes
- Jupyter Lab Environment•0 minutes
- Jupyter Lab 3•60 minutes
What's included
9 videos2 readings1 programming assignment2 ungraded labs
9 videos•Total 148 minutes
- Sampling Distributions•23 minutes
- How Sample Size Affects Variance of the Sampling Distribution•16 minutes
- Bootstrap Sampling•21 minutes
- The Central Limit Theorem•12 minutes
- Statistical Inference Part 1: Null Hypothesis Testing•17 minutes
- Statistical Inference Part 2: Simulating the Null with Shuffling•8 minutes
- Statistical Inference Part 3: Bootstrap Confidence Intervals•13 minutes
- Statistical Inference Part 4: a Bayesian Approach•24 minutes
- Debugging Challenge•14 minutes
2 readings•Total 20 minutes
- Post Course Survey•10 minutes
- Attributions•10 minutes
1 programming assignment•Total 120 minutes
- Assessment 4•120 minutes
2 ungraded labs•Total 60 minutes
- Jupyter Lab Environment•0 minutes
- Jupyter Lab 4•60 minutes
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