The amount of data generated is exploding and organizations are trying all means to extract meaningful insights from those raw data to boost their businesses.
Data science frameworks play an important role in this endeavour by offering the required data science tools and libraries to data science professionals and assisting decision-makers with data-driven decision-making.
Data science frameworks simply refer to a collection of libraries, tools, methodologies, and techniques to perform various kinds of tasks within the data science project lifecycle. Python and R are the most popular programming languages, with 29.39% and 4.71% market share respectively, as per the PYPL index, mostly used to develop and use data science frameworks.
They can be used to automate different routine tasks and provide a consistent environment for data scientists to focus on solving the business problems which is their core task.
These data science frameworks offer several advantages to data science professionals; however, they must consider different factors to choose the right framework suitable for their projects and processes.
Every data science enthusiast, whether a beginner or an expert, must be proficient in various data science frameworks. So, we have compiled a list of the most popular data science frameworks in the following infographic covering their uses, benefits, and things to consider while choosing the right framework.
Check out the infographic and enhance your understanding of popular frameworks.
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