Data Science Blog

JULY 23, 2023

Companies use Business Intelligence (BI), Data Science , and Process Mining to leverage data for better decision-making, improve operational efficiency, and gain a competitive edge. Data Mesh on Azure Cloud with Databricks and Delta Lake for Applications of Business Intelligence, Data Science and Process Mining.

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Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Read more to know.

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phData

AUGUST 11, 2023

Data engineering in healthcare is taking a giant leap forward with rapid industrial development. However, data collection and analysis have been commonplace in the healthcare sector for ages. Data Engineering in day-to-day hospital administration can help with better decision-making and patient diagnosis/prognosis.

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Smart Data Collective

MAY 29, 2023

With the introduction and use of machine learning, AI tech is enabling greater efficiencies with respect to data and the insights embedded in the information. Before moving into the hiring process though, it would be helpful to narrow down what type of data your business is managing. Here are the differences, generally speaking.

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phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

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ODSC - Open Data Science

MARCH 30, 2023

Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a GPU to a Container Using Azure ML to Train a Serengeti Data Model for Animal Identification In this article, we will cover how you can train a model using Notebooks in Azure Machine Learning Studio.

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Dataconomy

MAY 2, 2023

By acquiring expertise in statistical techniques, machine learning professionals can develop more advanced and sophisticated algorithms, which can lead to better outcomes in data analysis and prediction. Data modeling involves identifying underlying data structures, identifying patterns, and filling in gaps where data is nonexistent.

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Pickl AI

APRIL 10, 2023

Data Analysis is one of the most crucial tasks for business organisations today. SQL or Structured Query Language has a significant role to play in conducting practical Data Analysis. Data Analysts need deeper knowledge on SQL to understand relational databases like Oracle, Microsoft SQL and MySQL.

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Pickl AI

JANUARY 16, 2024

Summary: The blog delves into the 2024 Data Analyst career landscape, focusing on critical skills like Data Visualisation and statistical analysis. It identifies emerging roles, such as AI Ethicist and Healthcare Data Analyst, reflecting the diverse applications of Data Analysis.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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DataRobot

FEBRUARY 22, 2021

DataRobot’s team of elite data scientists and thought leaders have created, curated, and taught rigorous courses that empower 10X Academy students to take control of their future by gaining the skills required to solve complex problems.

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Mlearning.ai

JUNE 16, 2023

Game changer ChatGPT in Software Engineering: A Glimpse Into the Future | HackerNoon Generative AI for DevOps: A Practical View - DZone ChatGPT for DevOps: Best Practices, Use Cases, and Warnings. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API. The data would be interesting to analyze.

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Dataversity

FEBRUARY 3, 2022

Data Science is a diverse field with an array of career and job options out there to pursue. The modern economy is dependent on data and data analysis so, naturally, data scientists are in high demand and enjoy good salary and job security prospects.

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Pickl AI

DECEMBER 4, 2023

In the realm of Data Intelligence, the blog demystifies its significance, components, and distinctions from Data Information, Artificial Intelligence, and Data Analysis. Key Components of Data Intelligence In Data Intelligence, understanding its core components is like deciphering the secret language of information.

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Tableau

SEPTEMBER 23, 2021

For example, Tableau data engineers want a single source of truth to help avoid creating inconsistencies in data sets, while line-of-business users are concerned with how to access the latest data for trusted analysis when they need it most. Data modeling. Data migration . Data architecture.

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Tableau

SEPTEMBER 23, 2021

For example, Tableau data engineers want a single source of truth to help avoid creating inconsistencies in data sets, while line-of-business users are concerned with how to access the latest data for trusted analysis when they need it most. Data modeling. Data migration . Data architecture.

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Tableau

DECEMBER 1, 2021

April 2018), which focused on users who do understand joins and curating federated data sources. May 2020) shifted sheets to a multiple-table data model, where the sheet’s fields allow the computer to write much more efficient queries to the data sources. Another key data computation moment was Hyper in v10.5 (Jan

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Alation

FEBRUARY 20, 2020

Architecturally the introduction of Hadoop, a file system designed to store massive amounts of data, radically affected the cost model of data. Organizationally the innovation of self-service analytics, pioneered by Tableau and Qlik, fundamentally transformed the user model for data analysis.

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IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

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Iguazio

DECEMBER 14, 2023

Who This Book Is For This book is for practitioners in charge of building, managing, maintaining, and operationalizing the ML process end to end: Data science / AI / ML leaders: Heads of Data Science, VPs of Advanced Analytics, AI Lead etc. Exploratory data analysis (EDA) and modeling.

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Tableau

DECEMBER 1, 2021

April 2018), which focused on users who do understand joins and curating federated data sources. May 2020) shifted sheets to a multiple-table data model, where the sheet’s fields allow the computer to write much more efficient queries to the data sources. Another key data computation moment was Hyper in v10.5 (Jan

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Pickl AI

AUGUST 23, 2023

Consider enrolling in a “Data Science for stock market” course, which can provide insights into the specific techniques, tools, and datasets relevant to financial markets. Project-based Learning Hands-on experience is invaluable when it comes to Data Science.

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Pickl AI

JULY 25, 2024

Analysts and business users can easily comprehend and interact with the schema, which simplifies the process of generating reports and performing data analysis. Star Schema’s design prioritises simplicity and performance, making it advantageous for various data warehousing and business intelligence needs.

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DagsHub

OCTOBER 23, 2024

General Purpose Tools These tools help manage the unstructured data pipeline to varying degrees, with some encompassing data collection, storage, processing, analysis, and visualization. DagsHub's Data Engine DagsHub's Data Engine is a centralized platform for teams to manage and use their datasets effectively.

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Pickl AI

DECEMBER 10, 2024

Risk Management Strategies Across Data, Models, and Deployment Risk management begins with ensuring data quality , as flawed or biased datasets can compromise the entire system. Model validation and stress testing are crucial steps to identify weaknesses before deployment.

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Snorkel AI

MAY 12, 2023

Model-ready data refers to a feature library. For example, where verified data is present, the latencies are quantified. It enables users to aggregate, compute, and transform data in some scripted way, thereby promoting feature engineering, innovation, and reuse of data.

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Snorkel AI

MAY 12, 2023

Model-ready data refers to a feature library. For example, where verified data is present, the latencies are quantified. It enables users to aggregate, compute, and transform data in some scripted way, thereby promoting feature engineering, innovation, and reuse of data.

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