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What is Data Management and Why is it Important?

Analytics Vidhya

Introduction Data is, somewhat, everything in the business world. To state the least, it is hard to imagine the world without data analysis, predictions, and well-tailored planning! 95% of C-level executives deem data integral to business strategies.

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10 Best Data Engineering Books [Beginners to Advanced]

Pickl AI

Data Transformation: Converting, cleaning, and enriching raw data into a structured and consistent format suitable for analysis and reporting. Data Processing: Performing computations, aggregations, and other data operations to generate valuable insights from the data.

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What is Hadoop and How Does It Work?

Pickl AI

Hadoop has become a highly familiar term because of the advent of big data in the digital world and establishing its position successfully. The technological development through Big Data has been able to change the approach of data analysis vehemently. Let’s find out from the blog! What is Hadoop?

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

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The Age of Health Informatics: Part 1

Heartbeat

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

Top 50+ Interview Questions for Data Analysts Technical Questions SQL Queries What is SQL, and why is it necessary for data analysis? SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. How would you segment customers based on their purchasing behaviour?

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The Age of BioInformatics: Part 2

Heartbeat

The field demands a unique combination of computational skills and biological knowledge, making it a perfect match for individuals with a data science and machine learning background. Developing robust data integration and harmonization methods is essential to derive meaningful insights from heterogeneous datasets.