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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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R vs Python: What’s the Best Language for Natural Language Processing?

Sisense

One of the most-asked questions from aspiring data scientists is: “What is the best language for data science? People looking into data science languages are usually confused about which language they should learn first: R or Python. NLP can be used on written text or speech data. R or Python?”.

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Of Muffins and Machine Learning Models

Cloudera

Each project consists of a declarative series of steps or operations that define the data science workflow. We can think of model lineage as the specific combination of data and transformations on that data that create a model. This might require making batch and individual predictions.

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Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g., What is text mining?

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

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CIO 100 Award winners prove the transformative value of IT

CIO Business Intelligence

Whether a project aims to improve suicide prevention using data science or to create new revenue streams by reimagining an organization’s core business, CIO 100 Award winners demonstrate the innovative spirit of today’s IT in the face of rapidly evolving organizational challenges.

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Techniques for Collecting, Prepping, and Plotting Data: Predicting Social Media-Influence in the NBA

Domino Data Lab

Eighty percent of this problem is collecting the data and then transforming the data. The other 20 percent is ML- and data science–related tasks like finding the right model, doing EDA, and feature engineering. Gathering the Data. there is a list of data sources to extract and transform.