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Data Analytics with Python

December 12 - December 13

This course will cover topics such as data preparation, data analysis with Python, alongside Machine Learning with scikit-learn and much more.

Course Coverage

 

Setting Up Integrated Analysis Environment & Tools Overview

  • Setting up a Data Analysis Environment
  • IPython Shell and IPython Notebook
  • Tour of Data Science Packages: NumPy, SciPy, scikit-learn, Pandas, Matplotlib.

 

Python Essentials

  • Data types and objects
  • Loading External Packages
  • Control flow and Iterations
  • Data I/O

 

Data Acquisition and Preparation

  • Loading Data with Python
  • Data Munging
  • Data Cleansing

 

NumPy & SciPy: Numerical Analysis and Scientific Computing

  • Fundamental NumPy Data Structures
  • Working with NumPy Arrays
  • N-dimensional NumPy array operations and manipulations
  • SciPy Linear Algebra
  • SciPy Statistics

 

Visualization with Matplotlib

  • A tour of Matplotlib Library
  • Common Visualization Elements
  • Creating Plots with Matplotlib

 

Exploring Data

  • Pandas Data Analysis Library
  • Pandas Data Frames
  • Working with High-dimensional Data
  • Working with Time Series Data

 

Machine Learning with scikit-learn

  • Typical Machine Learning Tasks
  • A tour of scikit-learn library
  • Pre-processing data
  • Regression
  • Classification
  • Clustering
  • Evaluating Machine Learning Models