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Data analytics

Dataconomy

Data analytics vs. data science Differentiating analytics from data science highlights the applied focus of analytics compared to the broader, interdisciplinary approach encompassing machine learning and artificial intelligence. Apache Spark: A framework for processing large-scale data.

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Data Scientist Job Description – What Companies Look For in 2025

Pickl AI

As Indian companies across industries increasingly embrace data-driven decision-making, artificial intelligence (AI), and automation, the demand for skilled data scientists continues to surge. Data Visualization: Ability to create intuitive visualizations using Matplotlib, Seaborn, Tableau, or Power BI to convey insights clearly.

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10 Must-Have AI Engineering Skills in 2024

Data Science Dojo

Artificial Intelligence is reshaping industries around the world, revolutionizing how businesses operate and deliver services. Latest Advancements in AI Affecting Engineering Artificial Intelligence continues to advance at a rapid pace, bringing transformative changes to the field of engineering.

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Business Analytics vs Data Science: Which One Is Right for You?

Pickl AI

Dashboards, such as those built using Tableau or Power BI , provide real-time visualizations that help track key performance indicators (KPIs). Big data platforms such as Apache Hadoop and Spark help handle massive datasets efficiently. Data Scientists require a robust technical foundation.

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A Comprehensive Guide to the main components of Big Data

Pickl AI

These frameworks facilitate the efficient processing of Big Data, enabling organisations to derive insights quickly.Some popular frameworks include: Apache Hadoop: An open-source framework that allows for distributed processing of large datasets across clusters of computers. It is known for its high fault tolerance and scalability.

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

Pickl AI

Together, data engineers, data scientists, and machine learning engineers form a cohesive team that drives innovation and success in data analytics and artificial intelligence. Data Visualization: Matplotlib, Seaborn, Tableau, etc. Big Data Technologies: Hadoop, Spark, etc. ETL Tools: Apache NiFi, Talend, etc.

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Top 5 Challenges faced by Data Scientists

Pickl AI

One way to solve Data Science’s challenges in Data Cleaning and pre-processing is to enable Artificial Intelligence technologies like Augmented Analytics and Auto-feature Engineering. Some of the tools used by Data Science in 2023 include statistical analysis system (SAS), Apache, Hadoop, and Tableau.