Using agile technologies and analytics to help the government predict and prepare for outcomes

ITnova Bytes of Information

Government Applications

Predictive analytics and AI let the government attack likely problems before they cause damage. With natural language processing, image and voice recognition, and machine learning, the government can focus more on averting problems than just dealing with their outcomes. Predictive analytics is currently used in many departments such as security, defense, human services, and health care.

The Rise of Python

In recent years, Python has become increasingly common in the data analytics field. Because of big data, the demand for data analysts is higher than ever. IBM predicts that the number of available data analysis jobs will increase from 364,000 openings in 2017, to 2,720,000 in 2020.

Why Python?

This programming language is perfect for data analysis. It has been around for over thirty years and as of now, there are more than 70,000 libraries in the Python Package Index, and more are always being added. This makes Python a great tool for data analysts since it has many libraries geared towards the field. PANDAS is a widely known software library for Python that is a high-performance application for data manipulation and analysis. Python also makes it easy to write defensive code, easily go from prototype to production, and because of its scalability, lets it be used in almost any context.

Benefits of Using Python

Python is very readable, and it is easier to understand what the code means in this language rather than other similar languages. Other languages also make it harder to figure out errors that pop up, because they don’t tell you the full list of errors, but python will because it’s completely open. And when you need help, almost all questions about python will be answered on the StackOverflow website, a community where developers learn and share information.
This language is helpful when you must extrapolate valuable information from large pools of statistics and data. Because the data is not usually sorted, it is hard to connect with accuracy. Python lets you create CSV outputs to easily read data in a spreadsheet. Speaking of data extrapolation, you can automatically access and extract large amounts of data that is freely available on the internet. This technique is called web scraping, and it saves a lot of time and work.
Python completely free, and so are its downloadable packages, some of which are available on the internet for machine learning. This can be used for predicting future outcomes using your data. With these packages, you don’t need to learn the basics of machine learning, because it will be done for you. Other languages don’t have as many machine learning libraries as Python, making it the best option for machine learning and data analysis.

Comparisons to Other Languages

For data analysis, Python is compared to other domain-specific open source and commercial programming languages like R, MATLAB, SAS, Stata, and more. In an Analytics India Magazine survey, it was discovered that 44% of data scientists have a preference for Python over other programming languages such as SQL and SAS.

 Figure 1 shows KDnuggets Analytics/Data Science 2018 Software Poll about the top
 

Table 2 shows the top Analytics/Data Science/ML Software in 2018 KDnuggets

How ITnova Can Help

ITnova has extensive experience in providing business analytics and software engineering services to the government sector. We are able to use Python and track longitudinal data (data over different points of time) to tackle issues for our customers. Get real time and fast delivery projects using ITnova’s team.

As a conclusion, Python is an excellent programming language that provides solid documentation and extensive libraries. For the government, having access to well-written documentation is key to achieve knowledge transfer and continuity. The extensive number of libraries allows agile and faster development to support the government mission. 

ITnova Summer Intern- Allia Mahmood

Citations
Adibhatla, B. (2019, June 19). General Guide To Learning Python For Data Analytics In 2019. Retrieved July 23, 2019, from
https://www.analyticsindiamag.com/guide-python-data-analytics-2019/

Markow, W., Braganza, S., Taska, B., Miller, S. M., & Hughes, D. (2017). The Quant Crunch. Retrieved July 23, 2019, from https://www.ibm.com/downloads/cas/3RL3VXGA

T. (2018, September 20). Online IT Training Videos, IT Certification Training. Retrieved July 23, 2019, from
https://www.cbtnuggets.com/blog/2018/09/why-data-scientists-love-python/

Perricos, C., & Kapur, V. (2019, June 24). Anticipatory government. Retrieved July 23, 2019, from
https://www2.deloitte.com/insights/us/en/industry/public-sector/government-trends/2020/predictive-analytics-in-government.html

Piatetsky, G. (2018, May). KDnuggets. Retrieved July 23, 2019, from https://www.kdnuggets.com/2018/05/poll-tools-analytics-data-science-machine-learning-results.html

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