Data Science

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7 data science predictions for 2021

After a trying 2020, signs point to data science becoming an enterprise-wide capability that impacts every line of business and functional department in the coming year.

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Patrick J. McGovern Foundation invests in AI for public good

Organization pledges $40 million for initiatives to help nonprofits and NGOs benefit from data science and to foster the responsible and inclusive use of data and AI.

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How to merge data in Python using Pandas merge

Use the popular Pandas library for data manipulation and analysis to read data from two files and join them into a single dataset.

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Reconciling political beliefs with career ambitions

Data professionals face personal dilemmas as AI contributes to ethical issues such as reduced privacy, algorithmic bias, and advanced military weapons.

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Today’s data science roles won’t exist in 10 years

AutoML is poised to turn developers into data scientists — and vice versa. Here’s how AutoML will radically change data science for the better.

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The cloud’s role in elections grows more significant

Most campaigns view polling data, logistics, and demographics as fundamental to winning an election. More likely, data science and cloud computing will win the day.

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Where to find free and open data sets on the web

From government, health, and finance data to weather, baseball, and Star Trek, countless collections of free data are available to scratch almost your analytical itch

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What is computer vision? AI for images and video

Computer vision systems are not only good enough to be useful, but in some cases more accurate than human vision

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How to choose a cloud machine learning platform

12 capabilities every cloud machine learning platform should provide to support the complete machine learning lifecycle

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How to run Anaconda side by side with other Pythons

Want to use Anaconda alongside other Python distributions? Here’s how to keep those Python installs from stepping on each other’s toes

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How to choose a data analytics platform

A brief guide to the analytics lifecycle, the expanding array of tools and technologies, and selecting the right data platform for your needs

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15 hot tech skills getting hotter -- no certification required

Employers are apt to invest more often in cash premiums for noncertified tech skills compared to certifications. Here are a few they’re coveting the most now and going forward.

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For data scientists, drudgery is still job #1

Data cleaning and preparation still eats up nearly half the workload of data scientists, according to Anaconda’s new survey

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Kaggle: Where data scientists learn and compete

By hosting datasets, notebooks, and competitions, Kaggle helps data scientists discover how to build better machine learning models

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3 ways to apply agile to data science and dataops

Take an agile approach to dashboards, machine learning models, cleansing data sources, and data governance

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Data science during COVID-19: Some reassembly required

Most likely, the assumptions behind your data science model or the patterns in your data did not survive the coronavirus pandemic. Here’s how to address the challenges of model drift

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Julia vs. Python: Which is best for data science?

Python has turned into a data science and machine learning mainstay, while Julia was built from the ground up to do the job

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How data analysis, AI, and IoT will shape the post-pandemic ‘new normal’

Only a common public health infrastructure of AI, cloud computing, streaming, and the Internet of Things can marshal our data against pandemics

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