Learning Data-Driven Decisions for Managers in New Style Companies with Professor Dutta from USA Part 6.4

The development of information technology today is very helpful in the company's business. However, if we don't understand the type of technology needed, we might make the wrong choice of technology. Especially in the field of decision making for companies, there is one information technology product that is very helpful, namely a decision support system.


STEKOM University's efforts to have a global reach include holding webinars on an international scale. On this occasion we will discuss an international webinar held by STEKOM University in which one of the speakers is a professor from the United States. The resource person is Kaushik Dutta who is a Professor and School Director at the University of South Florida. Professor Dutta in his presentation delivered material on decision support systems which are IT products that are very useful in corporate business.


The material presented by Professor Dutta includes Framework, Applications for Business, Techniques, and Infrastructure. Because the material presented is quite long, the news article that discusses Professor Dutta's presentation is divided into several parts. We are currently entering part 6.4. If the reader wants to know the previous presentation, please see some of the previous chapters in the title of the same article.


Continuing from the previous part, then Professor Dutta explained about various machine learning tools. Among them are Weka, Rapidminer, R, and Python. This time we will discuss about Python. This tool is the most flexible and scalable of all. The reason is because python is a multi-functional programming language. Besides being known for machine learning, python is also widely known for creating web and desktop applications.


Python is one of the programming languages ​​that is considered the most appropriate option for all Artificial Intelligence (AI) projects, including machine learning. Then, why python in machine learning can be the most appropriate choice among other types of programming languages? We'll talk about it.


Machine learning has been applied in various industrial sectors that put forward Artificial Intelligence or AI projects as the foundation. Of the several programming languages ​​that exist, many industry players choose to use python for their machine learning applications.


The company's choice to use Python is not without reason, several studies have shown positive results about the strong side of Python as a programming language, making it more convincing for companies to choose it to power machine learning projects.


A good library ecosystem is the main advantage of python machine learning for AI. The collection of libraries on this system provides base level items, so data scientists don't have to code them from scratch every time. It is certainly very efficient, even libraries or modules published by different sources such as Pypi, already include pre-written code that allows users to perform several different functions.


Python libraries allow developers to perform continuous data processing required by machine learning, because it allows them to access, handle, and modify data freely. There are so many choices of libraries or libraries that developers can use for ML, for example, Scikit-learn, Panda, TensorFlow, Keras, Matplotlib, NLTK, PyBrain, Caffe, StatsModel, and Scikit-image. Also, in the Pypi repository, developers can find and compare more python libraries.


For beginners in the field of data science, programming languages ​​can be very difficult to learn. However, this is not the case with python which has a low entry barrier. Python's programming language tends to be similar to English. So, as long as people who learn Python have good English skills, then they will most likely be able to learn the Python programming language easily.


The syntax is also relatively simple, so the relationships between system elements are clear and complex, so developers can work more comfortably. Especially with tools like iPython which is an interactive shell that provides additional features such as testing, debugging, tab completion, and others, which can facilitate the developer's work process.


Python language is very flexible because it provides the option of choosing the use of OOPs or scripting, does not need to re-compile the source code, and can be combined with other languages ​​for optimizing purposes. Developers can also choose their own programming style as they want or according to their needs. The possibility of combining styles such as imperative, functional, and procedural styles can also solve problems efficiently.


Python in machine learning is capable of running on all platforms, including Windows, MacOS, Linux, Unix, and more. Developers only need to modify a few lines of code and change small-scale settings to make it executable on the chosen platform. To be more practical, using the PyInstaller package can be easier because it is able to prepare code automatically so that it can run on various platforms.


The many features offered by python make it popular as the best programming language so various industries choose this language for their purposes.


In the traveling sector, for example, the Skyscanner travel industry uses this program to predict new aircraft routes. It is able to compare thousands of origins and destinations, then evaluate them against 30 different criteria to determine requests from passengers. Implementation in the travel industry like this is very helpful for suggesting destinations to users, assisting in the creation of marketing budgets, as well as setting initial prices for new routes.


Within the financial services sector, python helps solve problems related to risk management, fraud prevention, personalized banking, automation and providing users with high quality financial services. With the help of this program, it is estimated that the financial industry can reduce operating costs by 22% by 2030 while generating $1 trillion dollars in revenue.


In addition, the transportation and health sectors also use python to develop their industry. For example, the development of the Michelangelo PyML platform by Uber in the transportation sector and Fathom and AiCure in the health sector.


Based on the above review, it is clear that choosing python in machine learning is one of the best AI options for programming languages ​​because it is simple, easy to understand, versatile, flexible, time efficient and multi-implementation. Many industrial sectors have used it as a development and useful to make work easier, analyze data quickly and accurately, and even make a profit.


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