Data Analysis, Data Models and Data Preparation

Data science revolution 101 – Unleashing the power of data in the digital age

Data Science Dojo

JUNE 7, 2023

Data Science is a field that encompasses various disciplines, including statistics, machine learning, and data analysis techniques to extract valuable insights and knowledge from data. It is divided into three primary areas: data preparation, data modeling, and data visualization.

Data Science

Data Science Data Visualization Data Scientist Machine Learning

Empower your career – Discover the 10 essential skills to excel as a data scientist in 2023

Data Science Dojo

MARCH 7, 2023

These skills include programming languages such as Python and R, statistics and probability, machine learning, data visualization, and data modeling. This includes sourcing, gathering, arranging, processing, and modeling data, as well as being able to analyze large volumes of structured or unstructured data.

Data Scientist

Data Scientist Exploratory Data Analysis Data Visualization Machine Learning

Transform your data into insights: The data analyst’s guide to Power BI

Data Science Dojo

FEBRUARY 9, 2023

It allows users to connect to a variety of data sources, perform data preparation and transformations, create interactive visualizations, and share insights with others. The platform includes features such as data modeling, data discovery, data analysis, and interactive dashboards.

Power BI

Power BI Data Analyst Data Visualization Data Analysis

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The Key to Sustainable Energy Optimization: A Data-Driven Approach for Manufacturing

From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

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LLMOps demystified: Why it’s crucial and best practices for 2023

Data Science Dojo

AUGUST 28, 2023

Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production. Exploratory Data Analysis (EDA) Data collection: The first step in LLMOps is to collect the data that will be used to train the LLM.

Exploratory Data Analysis

Exploratory Data Analysis Data Preparation Machine Learning Machine Learning

2024’s top Power BI interview questions simplified

Pickl AI

MARCH 4, 2024

With its intuitive interface, Power BI empowers users to connect to various data sources, create interactive reports, and share insights effortlessly. Optimising Power BI reports for performance ensures efficient data analysis. What is Power BI, and how does it differ from other data visualisation tools?

Power BI

Power BI Data Analysis Data Analysis Data Modeling

Introduction to Power BI Datamarts

ODSC - Open Data Science

JUNE 12, 2023

This article is an excerpt from the book Expert Data Modeling with Power BI, Third Edition by Soheil Bakhshi, a completely updated and revised edition of the bestselling guide to Power BI and data modeling. in an enterprise data warehouse. What is a Datamart? A replacement for datasets.

Power BI

Power BI Data Warehouse ETL Data Preparation

How can Data Scientists use ChatGPT for developing Machine Learning Models

Pickl AI

OCTOBER 17, 2023

Learn how Data Scientists use ChatGPT, a potent OpenAI language model, to improve their operations. ChatGPT is essential in the domains of natural language processing, modeling, data analysis, data cleaning, and data visualization. It facilitates exploratory Data Analysis and provides quick insights.

Data Scientist

Data Scientist Machine Learning Machine Learning Data Analysis

Why SQL is important for Data Analyst?

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.

Data Analyst

Data Analyst SQL Data Analysis Data Analysis

Introducing our New Book: Implementing MLOps in the Enterprise

Iguazio

DECEMBER 14, 2023

There are 6 high-level steps in every MLOps project The 6 steps are: Initial data gathering (for exploration). Exploratory data analysis (EDA) and modeling. Data and model pipeline development (data preparation, training, evaluation, and so on). Deploy according to various strategies.

ML

ML ML Data Science Data Preparation

What is Data-Centric Architecture in AI?

Pickl AI

JUNE 23, 2023

Data Collection The process begins with the collection of relevant and diverse data from various sources. This can include structured data (e.g., databases, spreadsheets) as well as unstructured data (e.g., Data Preparation Once collected, the data needs to be preprocessed and prepared for analysis.

AI

AI AI Data Governance Data Quality

What is a data fabric?

Tableau

APRIL 18, 2022

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Orchestration.

Tableau

Tableau Data Quality Analytics Analytics

What is a data fabric?

Tableau

APRIL 18, 2022

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Orchestration.

Tableau

Tableau Data Quality Analytics Analytics

How to Choose MLOps Tools: In-Depth Guide for 2024

DagsHub

APRIL 21, 2024

You need to make that model available to the end users, monitor it, and retrain it for better performance if needed. Similar to TensorFlow, PyTorch is also an open-source tool that allows you to develop deep learning models for free.

Machine Learning

Machine Learning Machine Learning ML ML

How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

SEPTEMBER 23, 2021

The right tooling will help you put your governance framework into practice, providing the necessary guardrails and data visibility that your teams need to boost trust and confidence in their data analysis. . Data modeling. Data migration . Data architecture. Metadata management.

Data Governance

Data Governance Analytics Analytics Tableau

How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

SEPTEMBER 23, 2021

The right tooling will help you put your governance framework into practice, providing the necessary guardrails and data visibility that your teams need to boost trust and confidence in their data analysis. . Data modeling. Data migration . Data architecture. Metadata management.

Data Governance

Data Governance Analytics Analytics Tableau

Inside the release: Tableau 2022.1 for analysts and business users

Tableau

APRIL 12, 2022

introduces a wide range of capabilities designed to improve every stage of data analysis—from data preparation to dashboard consumption. In the case of a failed run, backup flows can be set up to ensure that data is refreshed efficiently, without the need to over-schedule flow runs. Bronwen Boyd. April 13, 2022.

Tableau

Tableau Data Preparation Data Modeling Data Models

Inside the release: Tableau 2022.1 for analysts and business users

Tableau

APRIL 12, 2022

introduces a wide range of capabilities designed to improve every stage of data analysis—from data preparation to dashboard consumption. In the case of a failed run, backup flows can be set up to ensure that data is refreshed efficiently, without the need to over-schedule flow runs. Bronwen Boyd. April 13, 2022.

Tableau

Tableau Data Preparation Data Modeling Data Models

How to Build an End-To-End ML Pipeline

The MLOps Blog

MAY 9, 2023

A typical machine learning pipeline with various stages highlighted | Source: Author Common types of machine learning pipelines In line with the stages of the ML workflow (data, model, and production), an ML pipeline comprises three different pipelines that solve different workflow stages. They include: 1 Data (or input) pipeline.

ML

ML ML Machine Learning Machine Learning

Data Science Current

Data science revolution 101 – Unleashing the power of data in the digital age

Empower your career – Discover the 10 essential skills to excel as a data scientist in 2023

Webinars

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Transform your data into insights: The data analyst’s guide to Power BI

Webinars

LLMOps demystified: Why it’s crucial and best practices for 2023

2024’s top Power BI interview questions simplified

Introduction to Power BI Datamarts

How can Data Scientists use ChatGPT for developing Machine Learning Models

Why SQL is important for Data Analyst?

Introducing our New Book: Implementing MLOps in the Enterprise

What is Data-Centric Architecture in AI?

What is a data fabric?

What is a data fabric?

How to Choose MLOps Tools: In-Depth Guide for 2024

How to: Focus on three areas for a holistic data governance approach for self-service analytics

How to: Focus on three areas for a holistic data governance approach for self-service analytics

Inside the release: Tableau 2022.1 for analysts and business users

Inside the release: Tableau 2022.1 for analysts and business users

How to Build an End-To-End ML Pipeline

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