Use Python for Analyzing Visualizing and Presenting Data: Use Python for Analyzing, Visualizing and Presenting Data

Use Python for Analyzing Visualizing and Presenting Data: Use Python for Analyzing, Visualizing and Presenting Data

BEST SELLER 106 Lectures 20h 57m

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Curriculum For This Course

3 lectures 33m
  • 1. Installation Setup and Overview
    7m
  • 2. IDEs and Course Resources
    11m
  • 3. iPython/Jupyter Notebook Overview
    15m

7 lectures 1h 6m
  • 1. Creating arrays
    7m
  • 2. Using arrays and scalars
    5m
  • 3. Indexing Arrays
    14m
  • 4. Array Transposition
    4m
  • 5. Universal Array Function
    6m
  • 6. Array Processing
    22m
  • 7. Array Input and Output
    8m

11 lectures 2h 14m
  • 1. Series
    14m
  • 2. DataFrames
    18m
  • 3. Index objects
    5m
  • 4. Reindex
    16m
  • 5. Drop Entry
    6m
  • 6. Selecting Entries
    10m
  • 7. Data Alignment
    10m
  • 8. Rank and Sort
    6m
  • 9. Summary Statistics
    23m
  • 10. Missing Data
    12m
  • 11. Index Hierarchy
    14m

4 lectures 23m
  • 1. Reading and Writing Text Files
    10m
  • 2. JSON with Python
    4m
  • 3. HTML with Python
    5m
  • 4. Microsoft Excel files with Python
    4m

13 lectures 1h 44m
  • 1. Merge
    21m
  • 2. Merge on Index
    13m
  • 3. Concatenate
    9m
  • 4. Combining DataFrames
    10m
  • 5. Reshaping
    8m
  • 6. Pivoting
    6m
  • 7. Duplicates in DataFrames
    6m
  • 8. Mapping
    4m
  • 9. Replace
    3m
  • 10. Rename Index
    6m
  • 11. Binning
    6m
  • 12. Outliers
    7m
  • 13. Permutation
    5m

5 lectures 59m
  • 1. GroupBy on DataFrames
    18m
  • 2. GroupBy on Dict and Series
    13m
  • 3. Aggregation
    13m
  • 4. Splitting Applying and Combining
    10m
  • 5. Cross Tabulation
    5m

7 lectures 1h 28m
  • 1. Installing Seaborn
    2m
  • 2. Histograms
    9m
  • 3. Kernel Density Estimate Plots
    26m
  • 4. Combining Plot Styles
    6m
  • 5. Box and Violin Plots
    9m
  • 6. Regression Plots
    19m
  • 7. Heatmaps and Clustered Matrices
    17m

17 lectures 3h 38m
  • 1. Data Projects Preview
    3m
  • 2. Intro to Data Projects
    5m
  • 3. Titanic Project - Part 1
    17m
  • 4. Titanic Project - Part 2
    16m
  • 5. Titanic Project - Part 3
    16m
  • 6. Titanic Project - Part 4
    2m
  • 7. Intro to Data Project - Stock Market Analysis
    3m
  • 8. Data Project - Stock Market Analysis Part 1
    11m
  • 9. Data Project - Stock Market Analysis Part 2
    18m
  • 10. Data Project - Stock Market Analysis Part 3
    10m
  • 11. Data Project - Stock Market Analysis Part 4
    7m
  • 12. Data Project - Stock Market Analysis Part 5
    28m
  • 13. Data Project - Intro to Election Analysis
    2m
  • 14. Data Project - Election Analysis Part 1
    18m
  • 15. Data Project - Election Analysis Part 2
    21m
  • 16. Data Project - Election Analysis Part 3
    15m
  • 17. Data Project - Election Analysis Part 4
    26m

20 lectures 5h 50m
  • 1. Introduction to Machine Learning with SciKit Learn
    13m
  • 2. Linear Regression Part 1
    18m
  • 3. Linear Regression Part 2
    18m
  • 4. Linear Regression Part 3
    19m
  • 5. Linear Regression Part 4
    22m
  • 6. Logistic Regression Part 1
    14m
  • 7. Logistic Regression Part 2
    14m
  • 8. Logistic Regression Part 3
    12m
  • 9. Logistic Regression Part 4
    22m
  • 10. Multi Class Classification Part 1 - Logistic Regression
    19m
  • 11. Multi Class Classification Part 2 - k Nearest Neighbor
    23m
  • 12. Support Vector Machines Part 1
    13m
  • 13. Support Vector Machines - Part 2
    29m
  • 14. Naive Bayes Part 1
    10m
  • 15. Naive Bayes Part 2
    12m
  • 16. Decision Trees and Random Forests
    32m
  • 17. Natural Language Processing Part 1
    7m
  • 18. Natural Language Processing Part 2
    16m
  • 19. Natural Language Processing Part 3
    21m
  • 20. Natural Language Processing Part 4
    16m

11 lectures 1h 29m
  • 1. Intro to Appendix B
    3m
  • 2. Discrete Uniform Distribution
    6m
  • 3. Continuous Uniform Distribution
    7m
  • 4. Binomial Distribution
    13m
  • 5. Poisson Distribution
    11m
  • 6. Normal Distribution
    6m
  • 7. Sampling Techniques
    5m
  • 8. T-Distribution
    5m
  • 9. Hypothesis Testing and Confidence Intervals
    20m
  • 10. Chi Square Test and Distribution
    3m
  • 11. Bayes Theorem
    10m

3 lectures 28m
  • 1. Introduction to SQL with Python
    10m
  • 2. SQL - SELECT,DISTINCT,WHERE,AND & OR
    10m
  • 3. SQL WILDCARDS, ORDER BY, GROUP BY and Aggregate Functions
    8m

2 lectures 24m
  • 1. Web Scraping Part 1
    12m
  • 2. Web Scraping Part 2
    12m

3 lectures 41m
  • 1. Python Overview Part 1
    19m
  • 2. Python Overview Part 2
    12m
  • 3. Python Overview Part 3
    10m
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