First, Middle and Last-Mile Problems of Data Science
In this post we will explore the "First Mile", "Middle Mile" and "Last Mile" problems of Data Science.
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Framework
7 articles tagged with "Framework"
Data Maturity Models for Data Science
Data Maturity Models (DMM) are simple frameworks to understand the current state of your organization's Data and Analytics Journey. This is useful because it establishes common vocabulary to express the stage to which a Data Science project belongs.
Read article ->Streamlit Framework to Create Data Web Apps in Pure Python
Are you a Data Scientist/Analyst/Engineer and have always wanted to learn web development technologies like Django or Flask? Do you primarily use Python but want to build scalable Data Web Apps without having to learn yet-another framework or language like React.js, Angular, HTML, JavaScript, jQuery, PowerBI...? Read on to see if you can use the Streamlit framework to create Data Web Apps using only Python.
Read article ->But Do You Understand the Problem?
George Pólya was a professor of mathematics at Stanford University who taught the likes of John Von Neumann. Pólya spent considerable effort to identify systematic methods of problem-solving. In this post, we will look at one of his most popular problem-solving frameworks.
Read article ->How to Apply the "PPDAC" Framework to Your Data Science Problem
The "PPDAC" problem-solving cycle is a handy framework to formally apply the rigor of the "Scientific Method" to your Data Science Problem. Any specific statistical technique can be seen as one small component of this complete end-to-end cycle of problem-solving.
Read article ->Gestalt Principles for Data Science
Gestalt is a German word which means shape or form. They are a set of principles used to visually group objects. The Gestalt principles of "Enclosure", "Connection", "Proximity" and "Similarity" come from a branch of psychology that determine how we humans visually perceive the grouping of objects.
Read article ->10 Simple Rules for Effective Statistical Practice
One of our goals at Predinfer is to offer broadly applicable frameworks to help you become better at Data Science. In this post, let's take a look at ten simple rules you can apply to any Data Science problem.
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