Python is a universal programming language that enjoys great popularity among data scientists, who like to use it for machine learning and statistics. A cornerstone for the popularity of Python is the availability of powerful statistics libraries such as Numpy, Matplot and Pandas. These libraries offer extensive functions for transformation, visualization and analysis of data. Additional packages exist for machine learning. For instance Keras and Scikit Learn enable an easy implementation of different machine learning algorithms.
Python promotes a readable, concise code style. Furthermore, supports several programming paradigms, such as object-oriented, aspect-oriented, and functional programming. Because of its clear and concise syntax, Python is considered easy to learn.
Financial analysts have long been fascinated by the prospect of predicting the prices of financial assets. In recent years, there has been increasing interest in using machine learning and deep learning techniques to generate predictions, in addition to traditional methods such as technical and fundamental analysis. Python libraries like Keras and Scikit-Learn make it relatively … Read more
REST APIs provide straightforward access to remote data sources. Data scientists should learn about REST APIs because APIs (Application Programming Interfaces) are an important way for data scientists to access data from other sources. By using REST APIs, data scientists can access data from a wide range of sources, including databases, web services, and other … Read more
Anaconda is a popular open-source Python environment specifically designed for data science and machine learning. It comes with a range of useful features and tools, including Jupyter Notebooks, pre-installed packages, and a powerful package manager. It is the most widely used Python environment among data scientists and machine learning practitioners. In this article, we will … Read more
