Udemy-The Complete Pandas Bootcamp 2020 Data Science with Python

25. Statistical Concepts

• 1. Statistics - Overview, Terms and Vocabulary Page
• 3. Population vs. Sample Page
• 4. Visualizing Frequency Distributions with plt.hist() Page
• 5. Relative and Cumulative Frequencies with plt.hist() Page
• 6. Measures of Central Tendency (Theory) Page
• 7. Coding Measures of Central Tendency - Mean and Median Page
• 8. Coding Measures of Central Tendency - Geometric Mean Page
• 9. Variability around the Central Tendency Dispersion (Theory) Page
• 10. Minimum, Maximum and Range with PythonNumpy Page
• 11. Percentiles with PythonNumpy Page
• 12. Variance and Standard Deviation with PythonNumpy Page
• 13. Skew and Kurtosis (Theory) Page
• 14. How to calculate Skew and Kurtosis with scipy.stats Page
• 15. How to generate Random Numbers with Numpy Page
• 16. Reproducibility with np.random.seed() Page
• 17. Probability Distributions - Overview Page
• 18. Discrete Uniform Distributions Page
• 19. Continuous Uniform Distributions Page
• 20. The Normal Distribution (Theory) Page
• 21. Creating a normally distributed Random Variable Page
• 22. Normal Distribution - Probability Density Function (pdf) with scipy.stats Page
• 23. Normal Distribution - Cumulative Distribution Function (cdf) with scipy.stats Page
• 24. The Standard Normal Distribution and Z-Values Page
• 25. Properties of the Standard Normal Distribution (Theory) Page
• 26. Probabilities and Z-Values with scipy.stats Page
• 27. Confidence Intervals with scipy.stats Page
• 37. Case Study (Part 2) The Market Model (Single Factor Model) Page
• 28. Covariance and Correlation Coefficient (Theory) Page
• 29. Cleaning and preparing the Data - Movies Database (Part 1) Page
• 30. Cleaning and preparing the Data - Movies Database (Part 2) Page
• 31. How to calculate Covariance and Correlation in Python Page
• 32. Correlation and Scatterplots – visual Interpretation Page
• 33. What is Linear Regression (Theory) Page
• 34. A simple Linear Regression Model with numpy & Scipy Page
• 35. How to interpret Intercept and Slope Coefficient Page
• 36. Case Study (Part 1) The Market Model (Single Factor Model) Page
• Files Folder