Lead and Code

Introduction Previously we discussed the meaning and methods of data science and machine learning. There are numerous tutorials on using machine language but it is always confusing in figuring out where to start when given a problem. Over the course of my career, I have developed a nine-step framework – ML Framework - with a set of questions that helps me get started towards laying the foundation. It is to be used only as a guide because planning every detail of the data science process upfront isn’t always possible and more often than not you’ll iterate multiple times between the different steps of the process. ML Framework Describe the problem  Chart a solution  Look for the necessary data  Check if the data is usable  Explore and understand the data  Decide on keep or delete features  Select a machine learning algorithm  Interpret the results  Plan for scaling  Describe the problem What are we trying to solve? The main purpose here is m...

This is a manager's guide to data science and machine learning. Part 1 provides a very high-level overview along with few definitions. Part 2 provides a framework to get started with machine learning for data science projects.  Data science is a field of study that aims to use a scientific approach to extract meaning and insights from data – collection of numbers, words, observations and just about anything.  Data science uses results from statistics, machine learning, and computer science to create models that can transform hypotheses (assumptions) and data into actionable predictions (forecast). Much of the theoretical basis of data science comes from statistics along with a strong influence from software engineering methodologies.  The basic purpose of statistics is simply to enable us to make sense of large amount of data - by providing the ability to consolidate and synthesize large numbers to reveal the collective characteristics and interrelationships, and transfor...