Use Python for Analyzing Visualizing and Presenting Data: Use Python for Analyzing, Visualizing and Presenting Data
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106 Lectures
20h 57m
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Curriculum For This Course
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1. Installation Setup and Overview7m
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2. IDEs and Course Resources11m
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3. iPython/Jupyter Notebook Overview15m
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1. Creating arrays7m
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2. Using arrays and scalars5m
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3. Indexing Arrays14m
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4. Array Transposition4m
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5. Universal Array Function6m
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6. Array Processing22m
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7. Array Input and Output8m
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1. Series14m
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2. DataFrames18m
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3. Index objects5m
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4. Reindex16m
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5. Drop Entry6m
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6. Selecting Entries10m
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7. Data Alignment10m
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8. Rank and Sort6m
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9. Summary Statistics23m
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10. Missing Data12m
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11. Index Hierarchy14m
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1. Reading and Writing Text Files10m
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2. JSON with Python4m
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3. HTML with Python5m
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4. Microsoft Excel files with Python4m
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1. Merge21m
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2. Merge on Index13m
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3. Concatenate9m
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4. Combining DataFrames10m
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5. Reshaping8m
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6. Pivoting6m
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7. Duplicates in DataFrames6m
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8. Mapping4m
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9. Replace3m
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10. Rename Index6m
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11. Binning6m
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12. Outliers7m
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13. Permutation5m
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1. GroupBy on DataFrames18m
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2. GroupBy on Dict and Series13m
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3. Aggregation13m
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4. Splitting Applying and Combining10m
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5. Cross Tabulation5m
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1. Installing Seaborn2m
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2. Histograms9m
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3. Kernel Density Estimate Plots26m
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4. Combining Plot Styles6m
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5. Box and Violin Plots9m
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6. Regression Plots19m
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7. Heatmaps and Clustered Matrices17m
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1. Data Projects Preview3m
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2. Intro to Data Projects5m
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3. Titanic Project - Part 117m
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4. Titanic Project - Part 216m
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5. Titanic Project - Part 316m
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6. Titanic Project - Part 42m
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7. Intro to Data Project - Stock Market Analysis3m
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8. Data Project - Stock Market Analysis Part 111m
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9. Data Project - Stock Market Analysis Part 218m
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10. Data Project - Stock Market Analysis Part 310m
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11. Data Project - Stock Market Analysis Part 47m
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12. Data Project - Stock Market Analysis Part 528m
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13. Data Project - Intro to Election Analysis2m
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14. Data Project - Election Analysis Part 118m
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15. Data Project - Election Analysis Part 221m
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16. Data Project - Election Analysis Part 315m
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17. Data Project - Election Analysis Part 426m
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1. Introduction to Machine Learning with SciKit Learn13m
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2. Linear Regression Part 118m
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3. Linear Regression Part 218m
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4. Linear Regression Part 319m
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5. Linear Regression Part 422m
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6. Logistic Regression Part 114m
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7. Logistic Regression Part 214m
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8. Logistic Regression Part 312m
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9. Logistic Regression Part 422m
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10. Multi Class Classification Part 1 - Logistic Regression19m
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11. Multi Class Classification Part 2 - k Nearest Neighbor23m
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12. Support Vector Machines Part 113m
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13. Support Vector Machines - Part 229m
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14. Naive Bayes Part 110m
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15. Naive Bayes Part 212m
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16. Decision Trees and Random Forests32m
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17. Natural Language Processing Part 17m
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18. Natural Language Processing Part 216m
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19. Natural Language Processing Part 321m
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20. Natural Language Processing Part 416m
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1. Intro to Appendix B3m
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2. Discrete Uniform Distribution6m
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3. Continuous Uniform Distribution7m
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4. Binomial Distribution13m
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5. Poisson Distribution11m
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6. Normal Distribution6m
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7. Sampling Techniques5m
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8. T-Distribution5m
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9. Hypothesis Testing and Confidence Intervals20m
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10. Chi Square Test and Distribution3m
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11. Bayes Theorem10m
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1. Introduction to SQL with Python10m
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2. SQL - SELECT,DISTINCT,WHERE,AND & OR10m
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3. SQL WILDCARDS, ORDER BY, GROUP BY and Aggregate Functions8m
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1. Web Scraping Part 112m
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2. Web Scraping Part 212m
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1. Python Overview Part 119m
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2. Python Overview Part 212m
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3. Python Overview Part 310m
