Data Science is considered as one of the most modern and fascinating jobs of our time. It can be funny and can give you satisfaction, but is it really as it's described? At the beginning of their career, Data Scientists think that Data Science is a wonderful, magical world full of algorithms, Python functions that performs every possible spell with a line of code and statistical models able to detect the most useful correlations among data that could make you an invincible superhero in your company. You start dreaming about your CEO congratulating with you and shaking your hand, you begin to see decision trees and clusters everywhere and, of course, the most terrifying neural network architectures your mind can dream. But since the very first day of your first Data Science project, you start to realize what reality is.
Imagine that the product you've been working on for months or years implodes on its first day of life--a Product Manager's worst nightmare. Unfortunately, this story happens too often in the software world. This article covers what you can do to prevent it. I originally wrote this article as a guest post on MindTheProduct.com A failed launch is that terrible situation in which your product starts breaking or misbehaving as soon as it is launched into production.
In case you have never thought about this, now it is time to face the truth about projects and project management. This is what we deal with on a daily basis. Project management as a professional sphere is booming and there is a dramatic increase in professional project managers and jobs requiring project-oriented skills. The Project Management Institute, the leading global organization for project management, indicates that by 2027, employers will need almost 88 million employees directly involved in project management and project management-oriented roles -- a mind-blowing amount. If you are engaged in data science, machine learning, etc., you know how important it is to structure information, schedule and plan steps and phases.
Companies responded to the analytics boom by hiring the best data scientists they could find--but many of them haven't gotten the value they expected from their data science initiatives. For an analytics project to create value, the team must first ask smart questions, wrangle the relevant data, and uncover insights. Second, it must figure out--and communicate--what those insights mean for the business. The ability to do both is extremely rare--and most data scientists are trained to do the first, not the second. A good data science team needs six talents: project management, data wrangling, data analysis, subject expertise, design, and storytelling. The right mix will deliver on the promise of a company's analytics. Data science is growing up fast. Over the past five years companies have invested billions to get the most-talented data scientists to set up shop, amass zettabytes of material, and run it through their deduction machines to find signals in the unfathomable volume of noise. Data has begun to change our relationship to fields as varied as language translation, retail, health care, and basketball.
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