Python Eco-System for Data Science

Before Start with Data Analysis/ Data Science, we suppose to understand the Python Ecosystem for Data Science and Machine Learning

Pandas

Panel + Data = Pandas

• Provides high-level data structures and functions.
  • Ability to translate complex operations with data using simple commands.
    • Methods for grouping, combining data, and filtering, as well as time-series functionality.
    • Re-indexing, Iteration, Sorting, Aggregations, and Concatenations.
    • Easy to reshape, slice, and dice the data.
• Execution time is very Fast and Expensive.
• Flexible data manipulation capabilities like SQL.
• Very flexible to handle missing data, cleaning, and manipulation data.
• Python pandas is well suited for different kinds of data, such as:
  • Tabular data with heterogeneously typed columns.
  • Ordered and Unordered time series data.
  • Arbitrary matrix data with row & column labels, fit for Unlabelled data.
  • Any other form of observational or statistical data sets.

All above features of Pandas make best fit for Data Analysis point of view.

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NumPy

Numerical Python = NumPy

• Provides Data Structure, Algorithm for the Scientific application which requires numerical data.
• Which supports a multi-dimensional array manipulation.
• Easy to reshape, slice, and dice the array. And fast array process capability.
• Makes complex mathematical implementations very simple.
• Excellent support for Linear Algebra, Fourier Transformer, etc.,

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Statistical model

Statistical model

• A statistical model is a mathematical model that represents a set of statistical statements relating to the generation of sample data.
• A statistical model is usually specified as a mathematical relationship between one or more random variables and other non-random variables.
• Python has a built-in Statistical module, that you can use to calculate statistics of numeric data.

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Visualization

matplotlib

• Generate high-quality plotting variety of graphs (histograms – heat plots)
  • Line plots, scatter plots, histograms, bar charts, heat plots, and much more.
  • matplotlib is a 2D plotting library.

Seaborn

• Seaborn is another powerful data visualization library based on matplotlib.
• It provides a high-level interface visualization and informative statistical graphics.
• It closely integrates with pandas data structures.

Scikit Learn

Scikit Learn

• Scikit Learn contains lots of tools for Machine Learning
• It helps to do Data mining tasks like
  • Reducing dimensionality
  • Classification
  • Regression
  • Clustering
  • Model selection