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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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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems.

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Decoding Data Analyst Job Description: Skills, Tools, and Career Paths

FineReport

Diagnostic analytics: Uncovering the reasons behind specific occurrences through pattern analysis. Descriptive analytics: Assessing historical trends, such as sales and revenue. Predictive analytics: Forecasting likely outcomes based on patterns and trends to facilitate proactive decision-making.

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Python for Business: Optimize Pre-Processing Data for Decision-Making

Smart Data Collective

Therefore, if you don’t preprocess the data before applying it in the machine learning or AI algorithms, you are most likely to get wrong, delayed, or no results at all. Hence, data preprocessing is essential and required. Python as a Data Processing Technology.

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Using IBM Watson to Answer Two Important Questions about your Customers

Business Over Broadway

IBM is using the power of its Watson Studio platform to extend the power of AI to people who fall outside the realm of data science, machine learning and AI experts. IBM Watson Studio is an end-to-end analytics solution to help you gain insights from your data. The level of satisfaction is indexed by a summary statistic.

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Alation Connected Sheets Brings Trust to Spreadsheets

Alation

Or they don’t have the technical skill to extract, cleanse, or transform data they need. Spreadsheets dominate the activities of gathering and preparing data, and performing descriptive analytics. 78 million data workers are advanced spreadsheet users. Spreadsheets are dark matter.

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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. There is no clear end state. ref: [link].