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  • Udemy-The Complete Pandas Bootcamp 2020 Data Science with Python

    1. Home
    2. Courses
    3. Data Science Courses
    4. pandas
    5. 25. Statistical Concepts
    • 1. Getting Started
    • 2. PART 1 PANDAS FROM ZERO TO HERO (BUILDING BLOCKS)
    • 3. Pandas Basics (DataFrame Basics I)
    • 4. Pandas Series and Index Objects
    • 5. DataFrame Basics II
    • 6. Manipulating Elements in a DataFrame Slice Important, know the Pitfalls!
    • 7. DataFrame Basics III
    • 8. Visualization with Matplotlib
    • 9.PART 2 FULL DATA WORKFLOW A-Z
    • 10. Importing Data
    • 11. Cleaning Data
    • 12. Merging, Joining, and Concatenating Data
    • 13. GroupBy Operations
    • 14. Reshaping and Pivoting DataFrames
    • 15. Data Preparation and Feature Creation
    • 16. Advanced Visualization with Seaborn
    • 17. PART 3 COMPREHENSIVE PROJECT CHALLENGE
    • 18. PART 4 MANAGING TIME SERIES DATA WITH PANDAS
    • 19. Time Series Basics
    • 20. Time Series Advanced Financial Time Series
    • 21.WHATS NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE
    • 22. APPENDIX PYTHON BASICS, NUMPY and STATISTICS
    • 23. Python Basics
    • 24. The Numpy Package
    • 25. Statistical Concepts
    • 26. Download .py files
    • 27. Whats next
    • 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
    ◄24. The Numpy Package26. Download .py files►
    • pandas
    • 1. Getting Started
    • 2. PART 1 PANDAS FROM ZERO TO HERO (BUILDING BLOCKS)
    • 3. Pandas Basics (DataFrame Basics I)
    • 4. Pandas Series and Index Objects
    • 5. DataFrame Basics II
    • 6. Manipulating Elements in a DataFrame Slice Important, know the Pitfalls!
    • 7. DataFrame Basics III
    • 8. Visualization with Matplotlib
    • 9.PART 2 FULL DATA WORKFLOW A-Z
    • 10. Importing Data
    • 11. Cleaning Data
    • 12. Merging, Joining, and Concatenating Data
    • 13. GroupBy Operations
    • 14. Reshaping and Pivoting DataFrames
    • 15. Data Preparation and Feature Creation
    • 16. Advanced Visualization with Seaborn
    • 17. PART 3 COMPREHENSIVE PROJECT CHALLENGE
    • 18. PART 4 MANAGING TIME SERIES DATA WITH PANDAS
    • 19. Time Series Basics
    • 20. Time Series Advanced Financial Time Series
    • 21.WHATS NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE
    • 22. APPENDIX PYTHON BASICS, NUMPY and STATISTICS
    • 23. Python Basics
    • 24. The Numpy Package
    • 25. Statistical Concepts
    • 26. Download .py files
    • 27. Whats next
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