A PD2 report template that incorporates all formatting necessary for students in the course, so that they can focus on the actual content instead of worrying about less important details.
Conceived in the 80s by Guido Van Rossum, Python was as multiparadigm language that allowed for object oriented, structural and functional programming. This dynamic language, along with it's large community and timing made it a great choice for both academics who wanted something easy to use and for industry who wanted to rapidly develop products. Another factor is NumPy (Numeric Python) and its extension, SciPy (Scientific Python), collection of open source Python projects that support large-scale scientific data processing. Once these tools went mainstream, data analysis with python skyrocketed to become what it is today.
Machine learning, a particularly popular form of analysis became extremely accessible with the advent of a few more notable tools in the Python ecosystem:
ipython notebooks a way to explore data with python code
pandas a data manipulation tool
scikit-learn an extensive machine learning package that contains various pipeline tools and ready to use algorithms.
These three tools allows us to rapidly develop end to end machine learning platforms with extremely low technical debt while maintain high maintainability and readability.
From how easy it is to explore the data and generate reproducible research to the fast and efficient data manipulation and consistent and easy to understand machine learning pipeline apis provided my scikit learn, python is a great tool to get starting in building a machine learning model. However it is without its draw backs. Once that data gets larger and the computing must be out of core, we must either move on to different tools in the python ecosystem or move on to different languages that provide more power at the cost of simplicity.
jxnl
commented
9 years ago
Conceived in the 80s by Guido Van Rossum, Python was as multiparadigm language that allowed for object oriented, structural and functional programming. This dynamic language, along with it's large community and timing made it a great choice for both academics who wanted something easy to use and for industry who wanted to rapidly develop products. Another factor is NumPy (Numeric Python) and its extension, SciPy (Scientific Python), collection of open source Python projects that support large-scale scientific data processing. Once these tools went mainstream, data analysis with python skyrocketed to become what it is today.
Machine learning, a particularly popular form of analysis became extremely accessible with the advent of a few more notable tools in the Python ecosystem:
ipython notebooksa way to explore data with python codepandasa data manipulation toolscikit-learnan extensive machine learning package that contains various pipeline tools and ready to use algorithms.These three tools allows us to rapidly develop end to end machine learning platforms with extremely low technical debt while maintain high maintainability and readability.
From how easy it is to explore the data and generate reproducible research to the fast and efficient data manipulation and consistent and easy to understand machine learning pipeline apis provided my scikit learn, python is a great tool to get starting in building a machine learning model. However it is without its draw backs. Once that data gets larger and the computing must be out of core, we must either move on to different tools in the python ecosystem or move on to different languages that provide more power at the cost of simplicity.