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Welcome to the Data-Scientist-With-Python-datacamp wiki!

  1. Introduction to Python: 01 Numpy
  2. Intermediate Python 01 Matplotlib 02 Dictionary & Pandas 03 Logic, Control Flow and Filtering 04 Loops 05 Case Study: Hacker Statistics
  3. Data Manipulation with pandas
    01 Transforming Data 02 Aggregating Data 03 Slicing and Indexing 04 Creating and Visualizing
  4. Merging DataFrames with pandas
    01 Preparing Data 02 Concatenating Data 03 Merging Data 04 Case Study
  5. Joining Data with pandas
    01 Data Merging Basics 02 Merging Tables With Different Join Types 03 Advanced Merging and Concatenating 04 Merging Ordered and Time Series Data
  6. Introduction to Data Visualization with Matplotlib
    01 Introduction to Matplotlib 02 Plotting time-series 03 Quantitative comparisons and statistical visualizations 04 Sharing visualizations with others
  7. Python Data Science Toolbox 1&2
  8. Intermediate Data Visualization with Seaborn
    01 Seaborn Introduction 02 Customizing Seaborn Plots 03 Additional Plot Types 04 Creating Plots on Data Aware Grids
  9. Introduction to Importing Data in Python
    01 Introduction and Flat Files 02 Importing data from other file types 03 Working with relational databases in Python
  10. Intermediate Importing Data in Python
    01 Importing data from the Internet 02 Interacting with APIs to import data from the web
  11. Cleaning Data in Python
    01 Common Data Problems 02 Text and categorical data problems 03 Advanced data problems 04 Record linkage
  12. Working with Dates and Times in Python
    01 Dates and Calendars 02 Combining Dates and Times 03 Time Zones and Daylight Saving 04 Dates and Times in Pandas
  13. Writing Functions in Python
    01 Best Practice 02 Context Managers 03 Decorators
  14. Exploratory Data Analysis in Python
  15. Statistical Thinking in Python 1& 2
  16. Supervised Learning with scikit-learn
    01 Classification 02 Regression 03 Fine tuning your model 04 Preprocessing and pipeline
  17. Unsupervised learning in Python
    01 Clustering for dataset exploration 02 Visualization with hierarchical clustering and t-SNE 03 Decorrelating data and dimension reduction 04 Discovering interpretable features , NMF
  18. Machine learning with Tree-based Models
    01 Classification And Regression Tree 02 The Bias-Variance Tradeoff 03 Bagging and Random Forest 04 Boosting 05 Model Tuning