Monday 29 January 2024

Python Content for data analytics

 Python's Prominence in Data Analytics:

  • Readability and Simplicity: Python's clear syntax makes it easy to learn and code, even for beginners.

  • Extensive Libraries: It boasts a rich ecosystem of specialized libraries for data manipulation, analysis, and visualization.

  • Versatility: Python handles various tasks, from data cleaning and exploration to machine learning and model building.

  • Large Community: A thriving community offers support and resources, making learning and troubleshooting easier.

Key Libraries for Data Analytics:

1. NumPy:

  • Foundation for numerical computing in Python.

  • Efficiently handles multi-dimensional arrays and matrices.

  • Provides mathematical functions, linear algebra operations, and random number generation.

2. Pandas:

  • Built on top of NumPy, specifically for data analysis and manipulation.

  • Offers high-performance DataFrames for storing and working with tabular data.

  • Simplifies data cleaning, filtering, aggregation, and transformation.

3. Matplotlib:

  • Comprehensive library for creating static, animated, and interactive visualizations.

  • Generates various plots like line graphs, bar charts, scatter plots, histograms, and more.

4. Seaborn:

  • Built on top of Matplotlib, providing a high-level interface for visually appealing statistical graphics.

  • Simplifies creating informative and aesthetically pleasing visualizations.

5. Scikit-learn:

  • Versatile machine learning library with a wide range of algorithms for classification, regression, clustering, and dimensionality reduction.

  • Used for building and evaluating predictive models

  • NumPy: A library for scientific computing with Python.
  • Pandas: A library for data manipulation and analysis.
  • Matplotlib: A library for data visualization.
  • Seaborn: A library for statistical graphics.
  • Scikit-learn: A library for machine learning.
  • Statsmodels: A library for statistical modeling and analysis.
  • Apache Spark: A unified analytics engine for large-scale data processing.
  • PySpark: A Python API for Apache Spark.
  • TensorFlow: A library for machine learning and artificial intelligence.
  • PyTorch: A library for machine learning and artificial intelligence.
  1. Preliminaries
  2. Python language basics, IPython, and Jupyter notebooks
  3. Built-in data structures, functions, and files
  4. NumPy basics: arrays and vectorized computation
  5. Getting started with pandas
  6. Data loading, storage, and file formats
  7. Data cleaning and preparation
  8. Data wrangling: join, combine, and reshape
  9. Plotting and visualization
  10. Data aggregation and group operations
  11. Time series
  12. Advanced pandas
  13. Introduction to modeling libraies in Python
  14. Data analysis examples
  15. Advanced NumPy
  16. More on the IPython system.

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