Smart Data Collective

DECEMBER 1, 2022

The world of big data is constantly changing and evolving, and 2021 is no different. As we look ahead to 2022, there are four key trends that organizations should be aware of when it comes to big data: cloud computing, artificial intelligence, automated streaming analytics, and edge computing.

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Data Science Dojo

OCTOBER 31, 2024

Key Skills: Mastery in machine learning frameworks like PyTorch or TensorFlow is essential, along with a solid foundation in unsupervised learning methods. Stanford AI Lab recommends proficiency in deep learning, especially if working in experimental or cutting-edge areas.

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Smart Data Collective

SEPTEMBER 23, 2020

While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Both data warehouses and data lakes are used when storing big data.

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Pickl AI

AUGUST 9, 2024

Summary: A comprehensive Big Data syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of Big Data Understanding the fundamentals of Big Data is crucial for anyone entering this field.

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Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. We would like to talk about data visualization and its role in the big data movement.

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ODSC - Open Data Science

MARCH 6, 2023

Big data analytics is evergreen, and as more companies use big data it only makes sense that practitioners are interested in analyzing data in-house. Deep learning is a fairly common sibling of machine learning, just going a bit more in-depth, so ML practitioners most often still work with deep learning.

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Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

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Pickl AI

MAY 7, 2023

But deploying conventional methods to extract insight from this data is not feasible. Here comes the role of Big Data. The Symbiotic Relationship Between Facebook and Big Data Facebook has been leveraging Big Data technology to extract meaningful insights. It’s actually Big Data technologies.

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AWS Machine Learning Blog

OCTOBER 5, 2023

He focuses on developing scalable machine learning algorithms. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering. Yida Wang is a principal scientist in the AWS AI team of Amazon.

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ODSC - Open Data Science

FEBRUARY 8, 2024

Deep Learning with PyTorch and TensorFlow Dr. Jon Krohn | Chief Data Scientist | Nebula.io Jon Krohn, for an immersive introduction to Deep Learning that brings high-level theory to life with interactive examples featuring all three of the principal Python libraries, PyTorch, TensorFlow 2, and Keras.

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Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered. It will continue to make them a favorable choice in this fast-paced digital world.

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Smart Data Collective

FEBRUARY 20, 2022

The analysis of tons of data for your SaaS business can be extremely time-consuming, and it could even be impossible if done manually. Rather, AWS offers a variety of data movement, data storage, data lakes, big data analytics, log analytics, streaming analytics, and machine learning (ML) services to suit any need.

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Heartbeat

OCTOBER 23, 2023

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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ODSC - Open Data Science

FEBRUARY 6, 2024

LLMs Meet Google Cloud: A New Frontier in Big Data Analytics Mohammad Soltanieh-ha, PhD | Clinical Assistant Professor | Boston University Dive into the world of cloud computing and big data analytics with Google Cloud’s advanced tools and big data capabilities.

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IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? Machine learning and deep learning are both subsets of AI.

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ODSC - Open Data Science

APRIL 4, 2024

Data Analytics in the Age of AI, When to Use RAG, Examples of Data Visualization with D3 and Vega, and ODSC East Selling Out Soon Data Analytics in the Age of AI Let’s explore the multifaceted ways in which AI is revolutionizing data analytics, making it more accessible, efficient, and insightful than ever before.

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ODSC - Open Data Science

OCTOBER 22, 2024

NVIDIA: Powering the AI Revolution NVIDIA is a global leader in AI computing, designing GPUs and software that accelerate deep learning, machine learning, and high-performance computing. This impressive lineup includes: DataRobot: A leader in automated machine learning platforms, helping businesses deploy AI at scale.

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ODSC - Open Data Science

OCTOBER 17, 2023

Her interests lie in software testing, cloud computing, big data analytics, systems engineering, and architecture. Tuli Nivas is a Software Engineering Architect at Salesforce with extensive experience in design and implementation of test automation and monitoring frameworks.

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Pickl AI

JULY 20, 2023

Defining clear objectives and selecting appropriate techniques to extract valuable insights from the data is essential. Here are some project ideas suitable for students interested in big data analytics with Python: 1. Here are a few business analytics big data projects: 1. ImageNet).

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ODSC - Open Data Science

JANUARY 26, 2024

It will also feature even more hands-on training sessions, expert-led workshops, and tutorials on topics like machine learning, NLP and LLMs, data engineering, big data analytics, MLOps, generative AI, and more for our in-person attendees.

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Heartbeat

OCTOBER 25, 2023

e) Big Data Analytics: The exponential growth of biological data presents challenges in storing, processing, and analyzing large-scale datasets. Traditional computational infrastructure may not be sufficient to handle the vast amounts of data generated by high-throughput technologies.

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phData

AUGUST 11, 2023

Capturing and maintaining data on a large population can help doctors chart the best course of action according to their previous diagnoses. The use of deep learning and machine learning in healthcare is also increasing. Data engineering in research helped to study vaccines better.

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Pickl AI

JULY 14, 2023

Big Data and Deep Learning (2010s-2020s): The availability of massive amounts of data and increased computational power led to the rise of Big Data analytics. Deep Learning, a subfield of ML, gained attention with the development of deep neural networks.

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Pickl AI

SEPTEMBER 5, 2024

Advanced Analytics: Tools like Azure Machine Learning and Azure Databricks provide robust capabilities for building, training, and deploying Machine Learning models. Unified Data Services: Azure Synapse Analytics combines big data and data warehousing, offering a unified analytics experience.

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ODSC - Open Data Science

JANUARY 31, 2024

Connect with some of the most innovative people and ideas in the world of data science, while learning first-hand from core practitioners and contributors.

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How to Learn Machine Learning

APRIL 23, 2023

It is a waste of time in machine learning and in adopting artificial intelligence in general if companies allow themselves to be dragged by failures from its past projects. By quickly acting after failure, companies reach the goal of building a solid machine learning strategy ideal to withstand changes.

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DagsHub

NOVEMBER 11, 2024

These vector databases store complex data by transforming the original unstructured data into numerical embeddings; this is enabled through deep learning models. As reiterated earlier, embeddings take the critical components of various kinds of data, like text, images, and audio, and project them into one vector space.

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IBM Journey to AI blog

MAY 17, 2024

Poorly run implementations of traditional or generative AI technology in commerce—such as deploying deep learning models trained on inadequate or inappropriate data—lead to bad experiences that alienate both consumers and businesses.  The applications of AI in commerce are vast and varied.

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Pickl AI

SEPTEMBER 11, 2024

Employers often look for candidates with a deep understanding of Data Science principles and hands-on experience with advanced tools and techniques. With a master’s degree, you are committed to mastering Data Analysis, Machine Learning, and Big Data complexities.

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Mlearning.ai

AUGUST 18, 2023

As a discipline that includes various technologies and techniques, data science can contribute to the development of new medications, prevention of diseases, diagnostics, and much more. Utilizing Big Data, the Internet of Things, machine learning, artificial intelligence consulting , etc.,

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ODSC - Open Data Science

FEBRUARY 15, 2024

Connect with some of the most innovative people and ideas in the world of data science, while learning first-hand from core practitioners and contributors.

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Pickl AI

APRIL 10, 2023

So, if you are eyeing your career in the data domain, this blog will take you through some of the best colleges for Data Science in India. There is a growing demand for employees with digital skills The world is drifting towards data-based decision making In India, a technology analyst can make between ₹ 5.5 Lakhs to ₹ 11.0

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Pickl AI

JANUARY 12, 2023

Additionally, it involves learning the mathematical and computational tools that form the core of Data Science. Besides, you will also learn how to use the tools that will eventually help in making data-driven decisions.

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AWS Machine Learning Blog

APRIL 19, 2023

About the Authors Mark Roy is a Principal Machine Learning Architect for AWS, helping customers design and build AI/ML solutions. Mark’s work covers a wide range of ML use cases, with a primary interest in feature stores, computer vision, deep learning, and scaling ML across the enterprise.

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The MLOps Blog

AUGUST 11, 2023

Standard ML pipeline | Source: Author Advantages and disadvantages of directed acyclic graphs architecture Using DAGs provides an efficient way to execute processes and tasks in various applications, including big data analytics, machine learning, and artificial intelligence, where task dependencies and the order of execution are crucial.

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Dataconomy

FEBRUARY 11, 2025

AI summers, such as those driven by advancements in deep learning, increased computational power, and big data analytics, have repeatedly revived interest and funding.

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Dataconomy

FEBRUARY 26, 2025

This capability bridges various disciplines, leveraging techniques from statistics, machine learning, and artificial intelligence. Some key areas include: Big Data analytics: It helps in interpreting vast amounts of data to extract meaningful insights.

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