Data Science Dojo

FEBRUARY 9, 2023

Defining Power BI Power BI provides a suite of data visualization and analysis tools to help organizations turn data into actionable insights. It allows users to connect to a variety of data sources, perform data preparation and transformations, create interactive visualizations, and share insights with others.

Power BI 221

Power BI Data Analyst Data Visualization Data Analysis 221

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. Product Marketing Associate, Tableau.

Tableau 98

Tableau Data Preparation Data Models Data Modeling 98

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. Product Marketing Associate, Tableau.

Tableau 98

Tableau Data Preparation Data Models Data Modeling 98

Smart Data Collective

SEPTEMBER 16, 2020

This stems, largely, from the fact that there are certain data regulations in place when it comes to marketing tech and predictive analytics software. Business users need to determine whether or not their predictive analytics are meeting key needs or if the raw data, customer responses, and analytics methods are providing false positives.

Predictive Analytics 139

Predictive Analytics Analytics Analytics Decision Trees 139

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. No-code/low-code experience using a diagram view in the data preparation layer similar to Dataflows.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

Smart Data Collective

APRIL 5, 2022

This feature helps automate many parts of the data preparation and data model development process. This significantly reduces the amount of time needed to engage in data science tasks. A text analytics interface that helps derive actionable insights from unstructured data sets.

Data Scientist 145

Data Scientist Data Science Machine Learning Machine Learning 145

IBM Journey to AI blog

MARCH 29, 2024

Enterprise applications serve as repositories for extensive data models, encompassing historical and operational data in diverse databases. Generative AI foundational models train on massive amounts of unstructured and structured data, but the orchestration is critical to success.

AI 104

AI AI Artificial Intelligence Artificial Intelligence 104

AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. We are happy to announce that SageMaker Data Wrangler now supports using Lake Formation with Amazon EMR to provide this fine-grained data access restriction.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

phData

SEPTEMBER 28, 2023

Dataflows represent a cloud-based technology designed for data preparation and transformation purposes. Dataflows have different connectors to retrieve data, including databases, Excel files, APIs, and other similar sources, along with data manipulations that are performed using Online Power Query Editor.

Power BI 78

Power BI Data Preparation Machine Learning Machine Learning 78

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.

Tableau 102

Tableau Data Quality Analytics Analytics 102

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.

Tableau 98

Tableau Data Quality Analytics Analytics 98

Pickl AI

MARCH 4, 2024

Additionally, Power BI can handle larger datasets more efficiently, providing users with more significant insights into their data. How does Power Query help in data preparation? They are computed during data refresh and stored in the data model. How do you optimise Power BI reports for better performance?

Power BI 52

Power BI Data Analysis Data Analysis Data Models 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly.

AWS 128

AWS Machine Learning Machine Learning ML 128

ODSC - Open Data Science

NOVEMBER 19, 2024

ODSC West 2024 showcased a wide range of talks and workshops from leading data science, AI, and machine learning experts. This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies.

Deep Learning 52

Deep Learning Deep Learning Data Science AI 52

ODSC - Open Data Science

OCTOBER 7, 2024

In today’s landscape, AI is becoming a major focus in developing and deploying machine learning models. It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance. Model Training: Running computations to learn from the data.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

AWS Machine Learning Blog

JULY 29, 2024

SageMaker Canvas also provides excellent model transparency by offering direct access to trained models, which you can deploy at your chosen location, along with numerous model insight reports, including access to validation data, model- and item-level performance metrics, and hyperparameters employed during training.

ML 124

ML ML Algorithm AWS 124

AWS Machine Learning Blog

JUNE 22, 2023

New machines are added continuously to the system, so we had to make sure our model can handle prediction on new machines that have never been seen in training. Data preprocessing and feature engineering In this section, we discuss our methods for data preparation and feature engineering.

AWS 118

AWS ML ML Machine Learning 118

Tableau

JANUARY 27, 2021

In 2020, we released some of the most highly-anticipated features in Tableau, including dynamic parameters , new data modeling capabilities , multiple map layers and improved spatial support, predictive modeling functions , and Metrics. We continue to make Tableau more powerful, yet easier to use.

Tableau 102

Tableau Business Intelligence Business Intelligence Analytics 102

Tableau

SEPTEMBER 23, 2021

While not exhaustive, here are additional capabilities to consider as part of your data management and governance solution: Data preparation. Data modeling. Data migration . Data architecture. Metadata management. Security and risk management. Regulatory compliance.

Data Governance 102

Data Governance Analytics Analytics Tableau 102

Tableau

SEPTEMBER 23, 2021

While not exhaustive, here are additional capabilities to consider as part of your data management and governance solution: Data preparation. Data modeling. Data migration . Data architecture. Metadata management. Security and risk management. Regulatory compliance.

Data Governance 97

Data Governance Analytics Analytics Tableau 97

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 52

ML ML Data Science Data Preparation 52

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?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

ODSC - Open Data Science

MAY 10, 2023

Although tabular data are less commonly required to be labeled, his other points apply, as tabular data, more often than not, contains errors, is messy, and is restricted by volume. One might say that tabular data modeling is the original data-centric AI!

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

AWS Machine Learning Blog

MAY 31, 2024

Data preparation Before creating a knowledge base using Knowledge Bases for Amazon Bedrock, it’s essential to prepare the data to augment the FM in a RAG implementation. Krishna Prasad is a Senior Solutions Architect in Strategic Accounts Solutions Architecture team at AWS.

AWS 136

AWS Machine Learning Machine Learning Database 136

Pickl AI

APRIL 10, 2023

In case of professional Data Analysts, who might be engaged in performing experiments on data, standard SQL tools are required. Data Analysts need deeper knowledge on SQL to understand relational databases like Oracle, Microsoft SQL and MySQL. Moreover, SQL is an important tool for conducting Data Preparation and Data Wrangling.

Data Analyst 52

Data Analyst SQL Data Analysis Data Analysis 52

Pickl AI

OCTOBER 10, 2024

Power Pivot, on the other hand, allows users to create data models with relationships between different tables and perform complex calculations using Data Analysis Expressions (DAX). Can you explain what macros are in Excel?

Data Analysis 52

Data Analysis Data Analysis Data Models Data Modeling 52

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 52

AI AI Data Governance Data Quality 52

Iguazio

FEBRUARY 8, 2024

Data Pipeline - Manages and processes various data sources. Application Pipeline - Manages requests and data/model validations. Multi-Stage Pipeline - Ensures correct model behavior and incorporates feedback loops. ML Pipeline - Focuses on training, validation and deployment.

ML 52

ML ML Data Scientist AI 52

AWS Machine Learning Blog

NOVEMBER 15, 2023

It installs and imports all the required dependencies, instantiates a SageMaker session and client, and sets the default Region and S3 bucket for storing data. Data preparation Download the California Housing dataset and prepare it by running the Download Data section of the notebook.

Algorithm 126

Algorithm AWS ML ML 126

Pickl AI

JUNE 18, 2024

It now allows users to clean, transform, and integrate data from various sources, streamlining the Data Analysis process. This eliminates the need to rely on separate tools for data preparation, saving time and resources. Ensure data consistency and accuracy for trustworthy insights.

Power BI 52

Power BI Data Analysis Data Analysis Data Visualization 52

phData

SEPTEMBER 25, 2024

This setting ensures that the data pipeline adapts to changes in the Source schema according to user-specific needs. Fivetran’s pre-built data models are pre-configured transformations that automatically organize and clean the User’s synced data, making it ready for analysis.

ETL 52

ETL Database Data Warehouse Analytics 52

phData

AUGUST 24, 2023

More recently, ensemble methods and deep learning models are being explored for their ability to handle high-dimensional data and capture complex patterns. Data Preparation The first step in the process is data collection and preparation. loan default or not).

ML 52

ML ML Machine Learning Machine Learning 52

Tableau

JANUARY 27, 2021

In 2020, we released some of the most highly-anticipated features in Tableau, including dynamic parameters , new data modeling capabilities , multiple map layers and improved spatial support, predictive modeling functions , and Metrics. We continue to make Tableau more powerful, yet easier to use.

Tableau 52

Tableau Business Intelligence Business Intelligence Analytics 52

Pickl AI

NOVEMBER 28, 2024

Model Evaluation and Tuning After building a Machine Learning model, it is crucial to evaluate its performance to ensure it generalises well to new, unseen data. Model evaluation and tuning involve several techniques to assess and optimise model accuracy and reliability.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

OCTOBER 17, 2023

Data Scientists can save time by using ChatGPT to discover errors and provide solutions for cleaning. ChatGPT can also automate data pre-processing operations, including feature engineering and normalization. This will enhance the data preparation stage of machine learning.

Data Scientist 52

Data Scientist Machine Learning Machine Learning Data Science 52

Heartbeat

NOVEMBER 28, 2023

MLOps is a set of principles and practices that combine software engineering, data science, and DevOps to ensure that ML models are deployed and managed effectively in production. MLOps encompasses the entire ML lifecycle, from data preparation to model deployment and monitoring. Why Is MLOps Important?

ML 52

ML ML Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 18, 2024

Challenges Learning Curve : Qlik’s unique Data Analysis approach requires a bit of a learning curve, especially for new users. Data Preparation : Preparing data in Qlik is not as intuitive as other BI tools, which may slow the time to actionable insights.

Power BI 52

Power BI Tableau Data Analysis Data Analysis 52