Course Duration:Self-Paced

Course Details

Introduction and Setting Up Your Integrated Analysis Environment

  • Integrated Analysis Environment
  • IPython Shell
  • Custom environment settings
  • IPython Notebooks
  • Script editor
  • Packages: NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc.

    Using Python to Control and Document Your Data Science Processes

  • Data types and objects
  • Loading packages, namespaces
  • Reading and writing data
  • Simple plotting
  • Control flow
  • Debugging
  • Code profiling

    Accessing and Preparing Data

  • Loading from CSV files
  • Accessing SQL databases
  • Stripping out extraneous information
  • Direct Test Mode
  • Formatting data

    Numerical Analysis, Data Exploration, and Data Visualization with NumPy Arrays & Matplotlib

  • The NumPy array
  • 2D plotting with Matplotlib
  • N-dimensional array operations and manipulations
  • Memory mapped files

    Exploring Data with Pandas and scipy.stats

  • Data manipulation with Pandas
  • Statistical analysis with Pandas
  • Time series analysis with Pandas
  • Overview of statistical tools in scipy.stats

    Machine Learning with scikit-learn

  • Input: 2D, samples, and features
  • Estimator, predictor, transformer interfaces
  • Pre-processing data
  • Regression
  • Classification
  • Model selection

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