An enterprise can create products or analyze data easier with visual analytics and other data science tools. Understanding how to use these tools can help a business become more productive and efficient. Here are some key data science tools for monitoring analytics, digital publishing, and building applications.
Unlike the most common programming languages, Python emphasizes simple and clear, readable code with plenty of white space. It’s based on a small core open source programming language that integrates with other languages. Python, which can be used to develop APIs and sophisticated desktop applications, is one of the top five most popular programming language behind Java, C, and C++, according to the TIBOE Index in 2019. Python is useful for standardizing analytics tools.
Amazon Web Services (AWS) is a cloud platform for businesses to build online infrastructures. AWS Lamda is a part of this ecosystem and runs code for events while automatically managing resources associated with the code. As a metered cloud service it helps simplify building basic on-demand applications triggered by events, website clicks and sensor readings from IoT devices. It allows companies to deploy new data models in the cloud.
Self-publishing has become powerful for businesses, particularly for creating blogs or ebooks. Bookdown is a tool that transforms blog content into a publishable format supporting several different kernels. Many data scientists use R Markdown documents, which can be converted into various formats such as PDF and ePub. Other rival publishing apps exist, but Bookdown is one of the easiest to use.
Data scientists who work for companies that deal with millions of customers often create data pipelines that lead to a database. A data pipeline is a series of data processing elements in which the output of an element becomes the input of another. Cloud DataFlow is useful for building data streaming software.
PySpark and Pandas UDFs
Once a programmer learns Python they can use PySpark to scale huge datasets with ease. PySpark is a programming language that combines Python and Apache Spark. Pandas UDFs is a separate application that makes it easy to reuse Python code in Spark. PySpark is a Python API that provides the simplicity of Python and the speed of Spark.