ChingTien / DataCamp-Data-Scientist

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Data Scientist with Python


  • 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

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