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12 data science certifications that will pay off

CIO Business Intelligence

The US Bureau of Labor Statistics (BLS) forecasts employment of data scientists will grow 35% from 2022 to 2032, with about 17,000 openings projected on average each year. You need experience in machine learning and predictive modeling techniques, including their use with big, distributed, and in-memory data sets.

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The top 15 big data and data analytics certifications

CIO Business Intelligence

Candidates are required to complete a minimum of 12 credits, including four required courses: Algorithms for Data Science, Probability and Statistics for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization.

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Unlocking the Secrets of Your Customer Data

DataRobot

Data scientists typically come equipped with skills in three key areas: mathematics and statistics, data science methods, and domain expertise. It’s easy to deploy, monitor, and manage models in production and react to changing conditions. And any predictive model can become an AI app in minutes—no coding required.

ROI 52
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Best Practice of Using Data Science Competitions Skills to Improve Business Value

DataRobot Blog

Initially, the customer tried modeling using statistical methods to create typical features, such as moving averages, but the model metrics (R-square) was only 0.5 The larger the value, the better the model represents the data, and the smaller the value, the less well it represents the data.

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Defining data science in 2018

Data Science and Beyond

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. I was very comfortable with that definition, having spent my PhD years on several predictive modelling tasks, and having worked as a software engineer prior to that.

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Experiment or Die. Five Reasons And Awesome Testing Ideas.

Occam's Razor

There is a tendency to think experimentation and testing is optional. So you don't have to worry about integrations with analytics tools, you don't have to worry about rushing to get a PhD in Statistics to interpret results and what not. So as my tiny gift for you here are five experimentation and testing ideas for you.

Testing 112
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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Although it’s not perfect, [Note: These are statistical approximations, of course!] GloVe and word2vec differ in their underlying methodology: word2vec uses predictive models, while GloVe is count based. layer type and dropping it into a model architecture where you might otherwise. Example 11.6 place an LSTM() layer.