- 01. Python Basic
- 02. Python Lists
- 03. Functions and Package
- 04. Numpy
- 05. Matplotlib
- 06. Dictionaries & Pandas
- 07. Logic, Control Flow and Filtering
- 08. Loops
- 09. Case Study: Hacker Statistics
- 10. Writing your own functions
- 11. Default arguments, variable-length arguments and scope
- 12. Lambda functions and error-handling
- 13. Using iterators in PythonLand
- 14. List comprehensions and generators
- 15. Bringing it all together!
- 16. Introduction and flat files
- 17. Importing data from other file types
- 18. Working with relational databases in Python
- 19. Importing data from the Internet
- 20. Interacting with APIs to import data from the web
- 21. Diving deep into the Twitter API
- 22. Exploring your data
- 23. Tidying data for analysis
- 24. Combining data for analysis
- 25. Cleaning data for analysis
- 26. Case study
- 27. Data ingestion & inspection
- 28. Exploratory data analysis
- 29. Time series in pandas
- 30. Case Study - Sunlight in Austin
- 31. Extracting and transforming data
- 32. Advanced indexing
- 33. Rearranging and reshaping data
- 34. Grouping data
- 35. Bringing it all together
- 36. Preparing data
- 37. Concatenating data
- 38. Merging data
- 39. Case Study - Summer Olympics
- 40. Selecting columns
- 41. Filtering rows
- 42. Aggregate Functions
- 43. Sorting, grouping and joins
- 44. Basics of Relational Databases
- 45. Applying Filtering, Ordering and Grouping to Queries
- 46. Advanced SQLAlchemy Queries
- 47. Creating and Manipulating your own Databases
- 48. Putting it all together
- 49. Customizing plots
- 50. Plotting 2D arrays
- 51. Statistical plots with Seaborn
- 52. Analyzing time series and images
- 53. Basic plotting with Bokeh
- 54. Layouts, Interactions, and Annotations
- 55. Building interactive apps with Bokeh
- 56. Putting It All Together! A Case Study
- 57. Graphical exploratory data analysis
- 58. Quantitative exploratory data analysis
- 59. Thinking probabilistically-- Discrete variables
- 60. Thinking probabilistically-- Continuous variables
- 61. Parameter estimation by optimization
- 62. Bootstrap confidence intervals
- 63. Introduction to hypothesis testing
- 64. Hypothesis test examples
- 65. Putting it all together: a case study
- 66. Introduction to joins
- 67. Outer joins and cross joins
- 68. Set theory clauses
- 69. Subqueries
- 70. Classification
- 71. Regression
- 72. Fine-tuning your model
- 73. Preprocessing and pipelines
- 74. Exploring the raw data
- 75. Creating a simple first model
- 76. Improving your model
- 77. Learning from the experts
- 78. Clustering for dataset exploration
- 79. Visualization with hierarchical clustering and t-SNE
- 80. Decorrelating your data and dimension reduction
- 81. Discovering interpretable features
- 82. Basics of deep learning and neural networks
- 83. Optimizing a neural network with backward propagation
- 84. Building deep learning models with keras
- 85. Fine-tuning keras models
- 86. Introduction to networks
- 87. Important nodes
- 88. Structures
- 89. Bringing it all together
- 90. Classification and Regression Trees
- 91. The Bias-Variance Tradeoff
- 92. Bagging and Random Forests
- 93. Boosting
- 94. Model Tuning