Stoffplan: Python für Data-Analysten

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1. Iphyton     1.1 Help and Documentation
    1.2 Keyboard Shortcuts
    1.3 Magic Commands 
    1.4 Input and Output History
    1.5 Iphyton History
    1.6 Iphyton and Shellcommands
    1.7 Profiling and timing Codes


2. Introduction to NumPy    2.1 Basic NumPy Arrays
    2.2 Computation NumPy Arrays
    2.3 Aggregation
    2.4 Comparisons, Masks and Boolean Logic
    2.5 Fancy Indexing
    2.6 Sorting Arrays
    2.7 Structured Data


3. Data Manipulation with Pandas
    3.1 Pandas Objects
    3.3 Data Indexing and Selection
    3.4 Operation on Data in Pandas
    3.5 Handling missing Data
    3.6 Hierarchical Indexing
    3.7 Combining Datasets (Concat, Append, Merge, Join)
    3.8 Aggregation and Grouping
    3.9 Pivot Tables
    3.10 Vectorized String Operations
    3.11 Working with Time Series
    3.12 High Performance Pandas


4. Visualisation with Matplotlib
    4.1 General Tips
    4.2 Simple Line Plots
    4.3 Simple Scatter Plots
    4.4 Visualizing Errors
    4.5 Density and Contour Plots
    4.6 Histograms
    4.7 Multiple Subplots
    4.8 Text and Annunciation
    4.9 Customizing Ticks
    4.10 Three Demensional Plotting
    4.11 Geographic Data with Basecamp
    4.12 Visualisation with Seaborn


5. Machine Learning
    5.1 Introducing Scikit Learn
    5.2 Hyperparameters
    5.3 In Depth Machine Learning