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What is data science? Transforming data into value

CIO Business Intelligence

What is data science? Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. Data science gives the data collected by an organization a purpose. Data science vs. data analytics.

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15 best data science bootcamps for boosting your career

CIO Business Intelligence

An education in data science can help you land a job as a data analyst , data engineer , data architect , or data scientist. Here are the top 15 data science boot camps to help you launch a career in data science, according to reviews and data collected from Switchup.

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The Shift toward Intelligent Automation

DataRobot

With these technologies, business users can easily build, deploy, and manage software robots that emulate humans actions interacting with digital systems and software. One of the leading methods for process automation involves robotic process automation (RPA).

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The quest for high-quality data

O'Reilly on Data

Since they consume a significant amount of time spent on most data science projects, we highlight these two main classes of data quality problems in this post: Data unification and integration. An important paradigm for solving both these problems is the concept of data programming.

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How to supercharge data exploration with Pandas Profiling

Domino Data Lab

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. The Importance of Exploratory Analytics in the Data Science Lifecycle. imputation of missing values). ref: [link].

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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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Data Exploration with Pandas Profiler and D-Tale

Domino Data Lab

In a previous blog , we have covered how Pandas Profiling can supercharge the data exploration required to bring our data into a predictive modelling phase. We covered the importance of Exploratory Analytics including observing the frequency of missing data and computing interactions of all features.