Page Index - ellen567/Data-Science-Notebook GitHub Wiki
135 page(s) in this GitHub Wiki:
- Home
- 01 01 Introduction to Python Numpy
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- 02 01 Matplotlib
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- 02 02 Dictionary
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- 02 02 Pandas
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- 02 03 Logic, Control Flow and Filtering
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- 02 04 Loops
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- 02 05 Case Study: Hacker Statistics
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- 03 01 Transforming Data
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- 03 02 Aggregating Data
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- 03 03 Slicing and Indexing
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- 03 04 Creating and Visualizing DataFrames
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- 04 01 Preparing data
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- 04 02 Concatenating data
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- 04 03 Merging Data
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- 04 04 Case Study Summer Olympics
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- 04a 01 Data Merging Basics
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- 04a 02 Merging Tables With Different Join Types
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- 04a 03 Advanced Merging and Concatenating
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- 04a 04 Merging Ordered and Time Series Data
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- 05 01 Introduction to Matplotlib
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- 05 02 Plotting time series data
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- 05 03 Quantitative comparisons and statistical visualizations
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- 05 04 Sharing your visualization with others
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- 06 01 Introduction to Seaborn
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- 06 02 Visualizing Two Quantitative Variables
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- 06 03 Visualizing a Categorical and a Quantitative Variable
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- 06 04 Customizing Seaborn Plots
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- 07 Python Data Science Toolbox
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- 08 01 Seaborn Introduction
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- 08 02 Customizing Seaborn Plots
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- 08 03 Additional Plot Types
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- 08 04 Creating Plots on Data Aware Grids
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- 09 01 Introduction and Flat Files
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- 09 02 Importing data from other file types
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- 09 03 Working with relational databases in Python
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- 10 01 Importing data from the Internet
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- 10 02 Interacting with APIs to import data from the web
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- 11 01 Common Data Problems
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- 11 02 Text and categorical data problems
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- 11 03 Advanced data problems
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- 11 04 Record linkage
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- 12 01 Dates and Calendars
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- 12 02 Combining Dates and Times
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- 12 03 Time Zones and Daylight Saving
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- 12 04 Dates and Times in Pandas
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- 13 01 Best Practices
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- 13 02 Context Managers
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- 13 03 Decorators
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- 14 Exploratory Data Analysis in Python
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- 15 01 Statistical Thinking in Python (Part 1)
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- 15 02 Statistical Thinking in Python (Part 2)
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- 16 01 Classification
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- 16 02 Regression
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- 16 03 Fine tuning your model
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- 16 04 Preprocessing and pipelines
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- 17 01 Clustering for dataset exploration
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- 17 02 Visualization with hierarchical clustering and t SNE
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- 17 03 Decorrelating your data and dimension reduction
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- 17 04 Discovering interpretable features
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- 18 01 Classification And Regression Tree
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- 18 02 The Bias Variance Tradeoff
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- 18 03 Bagging and Random Forests
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- 18 04 Boosting
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- 18 05 Model Tuning
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- 19 02 Hierarchical Clustering
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- 19 03 K Means Clustering
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- 19 04 Clustering in Real World
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