Data Science

Data Science using Python Data Stack

Data Science (https://en.wikipedia.org/wiki/data_scienceis an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.

Data Scientists use their data and analytical ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings.

Data Analysis (https://en.wikipedia.org/wiki/data_analysisis a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

The Data Analysis process contains, in general, the following ten main logical steps:

1. Business Situation.

2. Define Influenced Variables.

3. Data Collection.

4. Data Processing.

5. Data Cleaning.

6. Data Presentation.

7. Data Analysis.

8. Data Transformation.

9. Data Conclusion.

10. Making Business Decisions.

15 IT Resources has specialized in Data Science using Python Data Stack. Why do we use Python for Data Science today? Below are three main points:   

1. Free download and use under the GPL-compatible licenses.

2. Easy to code, debug, deployment and maintain.

3. Extensible math libraries and community support for data manipulation and visualization tasks.

These three simple points make Python one of the most popular scripting programming languages for scientific computing in both industry applications and academic research today.

15 IT Resources use the following Python Windows Integrated Development Environments (IDE):

I. Eclipse IDE with PyDev Plugin providers the following features:

1. Code completion.

2. Code completion with auto import.

3. Type hinting.

4. Code analysis.

5. Go to definition.

6. Refactoring.

7. Debugger.

8. Remote debugger.

9. Find Referrers in Debugger.

10. Tokens browser.

11. Interactive console.

12. Unit-test integration.

13. Code coverage.

14. Django integration.

II. Python Tools for Microsoft Visual Studio .NET support the following features:

1. Support CPython, PyPy, IronPython interpreters and more.

2. Detailed IntelliSense.

3. Interactive debugging.

4. Integrated with Visual Studio powerful features.

5. Free and open-source.

6. Select it in Visual Studio custom install.

15 IT Resources use the following essentials Python community libraries for Data Science:

1. NumPy –  fundamental package for scientific computing (Numerical Python).

2. pandas – provides easy-to-use and high-performance data structures.

3. matplotlib – a 2D plotting library which produces publication quality figures in a variety of hard copy formats and interactive environments across platforms.

4. IPython – a component in the standard scientific tool set that ties everything together. It provides a robust and productive environment for interactive and exploratory computing.

5. SciPy – it provides many user-friendly and efficient numerical routines for numerical calculation and optimization.

6. PyQT – a GUI toolkit to design and develop applications for iOS, Android, OS X, Linux and Microsoft Windows.

7. scikit-learn Machine Learning – a simple and efficient tool for data mining and data analysis. Built on NumPy, SciPy, and matplotlib libraries.

15 IT Resources is proud to provide high-performance Data Science software and results to many companies today. Call us, provide the data, and we will perform the data analysis process.  With this we will inform you of possible improvements that could be made to enhance your business productivity and efficiency.

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