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