Learning Python Data Analysis
Analyze and understand your data with the power and simplicity of Python About This Video Learn Data Analysis using modern processing techniques with Numpy, SciPy, and Pandas Apply Natural Language Algorithms other Machine Learning Algorithms to provide unique and interesting insights Present Insigh...
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| Formato: | |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing
2017.
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| Edición: | 1st edition |
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| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631067106719 |
| Sumario: | Analyze and understand your data with the power and simplicity of Python About This Video Learn Data Analysis using modern processing techniques with Numpy, SciPy, and Pandas Apply Natural Language Algorithms other Machine Learning Algorithms to provide unique and interesting insights Present Insights quickly and cleanly through the use of a Dashboard Application In Detail Python features numerous numerical and mathematical toolkits such as: Numpy, Scipy, Scikit learn and SciKit, all used for data analysis and machine learning. With the aid of all of these, Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. This video aims to teach Python developers how to perform data analysis with the language by taking advantage of the core data science libraries in the Python ecosystem. The learning objective for viewers is to understand how to locate, manipulate, and analyse data with Python, with the ability to analyse large and small sets of data using libraries such as Numpy, pandas, IPython and SciPy. This is a two part series. The first series is focused on getting and manipulation sizeable amounts of data using modern techniques. The second series is focused on advanced analysis of the data to include modern machine learning techniques. |
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| Notas: | Title from title screen (viewed July 11, 2018). Date of publication from resource description page. |
| Descripción Física: | 1 online resource (1 video file, approximately 5 hr., 56 min.) |
