Data Science Libraries - Python
Python is an accepted language for Data Science , and great for general-purpose programming language as well. Python has quickly become a dominant programming language for Data Science applications and Machine Learning . Because Python is simple and multifaceted and has easy readability, it is an obvious language of choice in the field of Data Science .
The focus of this course is to provide in depth details of some of these Data Science Libraries along with some solid examples of how these libraries can be used in several data science applications .
Python computing libraries
- NumPy (Arrays & matrices)
- Panda (Data Structures & Tools)
- SciPy (Integrals, solving differential equations, optimization)
Python Visualization Libraries
- Matplotlib (Plots and Graphsr)
- Seaborn(Plots: heatmaps, time series, violin plots)
Python Algorithmic Libraries
- Scikit Learn (Machine Learning: regression, classification, clustering)
- Statsmodels (Explore data, estimate statistical model and perform statistical tests)
This course begin with the core libraries necessary for working with Data Science in Python. Installing all Python Data Science libraries individually can be a bit difficult for beginners, so we recommend Anaconda, an all-in-one installer. Anaconda , the World's Most Popular Data Science Platform, (Mac, Windows, Linux) distribution for large-scale data processing and scientific research computing in Python(includes scientific research and data analysis libraries such as Pandas , Numpy, Matplotlib, scikit-learn etc., as well as IPython). This is the recommended package for this course.