Data Science Dojo

SEPTEMBER 21, 2023

You’re excited, but there’s a problem – you need data, lots of it, and from various sources. You could spend hours, days, or even weeks scraping websites, cleaning data, and setting up databases. Or you could use APIs and get all the data you need in a fraction of the time. Sounds like a dream, right?

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AWS Machine Learning Blog

NOVEMBER 27, 2023

Most real-world data exists in unstructured formats like PDFs, which requires preprocessing before it can be used effectively. According to IDC , unstructured data accounts for over 80% of all business data today. This includes formats like emails, PDFs, scanned documents, images, audio, video, and more. read HTML).

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Towards AI

SEPTEMBER 10, 2023

Lesson #2: How to clean your data We are used to starting analysis with cleaning data. Surprisingly, fitting a model first and then using it to clean your data may be more effective. For example, scikit-learn documentation has at least a dozen approaches to Supervised ML.

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ODSC - Open Data Science

MAY 22, 2025

Tools like large language models and automated analytics platforms are helping them code faster, clean data more efficiently, and extract insights at scale. Automation of routine tasks like data cleaning, anomaly detection, and report generation saves hours eachweek. The result?

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Flipboard

MARCH 22, 2023

Data Wrangler simplifies the data preparation and feature engineering process, reducing the time it takes from weeks to minutes by providing a single visual interface for data scientists to select and clean data, create features, and automate data preparation in ML workflows without writing any code.

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Dataconomy

JUNE 14, 2023

These tools are equipped with all the required resources and documentation to assist in the smooth integration process. The Janitor AI API comes with a wealth of features, such as the ability to clean data, format data.frame column titles, swiftly count variable combinations, and cross-tabulate data.

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Tableau

OCTOBER 8, 2021

Our customers also need a way to easily clean, organize and distribute this data. Tableau Prep allows you to combine, reshape, and clean data using an easy-to-use, visual, and direct interface. Combining and analyzing Shopify and Google Analytics data helped eco-friendly retailer Koh improve customer retention by 25%.

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Smart Data Collective

APRIL 29, 2020

The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated. Data analytics and visualisation. Microsoft Azure.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

For the dataset in this use case, you should expect a “Very low quick-model score” high priority warning, and very low model efficacy on minority classes (charged off and current), indicating the need to clean up and balance the data. Refer to Canvas documentation to learn more about the data insights report.

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Mlearning.ai

JANUARY 26, 2023

It can be gradually “enriched” so the typical hierarchy of data is thus: Raw data ↓ Cleaned data ↓ Analysis-ready data ↓ Decision-ready data ↓ Decisions. For example, vector maps of roads of an area coming from different sources is the raw data.

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Pickl AI

FEBRUARY 27, 2023

Working with inaccurate or poor quality data may result in flawed outcomes. Hence it is essential to review the data and ensure its quality before beginning the analysis process. Ignoring Data Cleaning Data cleansing is an important step to correct errors and removes duplication of data.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Customers must acquire large amounts of data and prepare it. This typically involves a lot of manual work cleaning data, removing duplicates, enriching and transforming it. Unlike in fine-tuning, which takes a fairly small amount of data, continued pre-training is performed on large data sets (e.g.,

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Pickl AI

APRIL 21, 2025

Semi-Structured Data: Data that has some organizational properties but doesn’t fit a rigid database structure (like emails, XML files, or JSON data used by websites). Unstructured Data: Data with no predefined format (like text documents, social media posts, images, audio files, videos).

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Tableau

OCTOBER 8, 2021

Our customers also need a way to easily clean, organize and distribute this data. Tableau Prep allows you to combine, reshape, and clean data using an easy-to-use, visual, and direct interface. Combining and analyzing Shopify and Google Analytics data helped eco-friendly retailer Koh improve customer retention by 25%.

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Tableau Analytics Analytics Machine Learning 98

Snorkel AI

JULY 12, 2024

This approach can be particularly effective when dealing with real-world applications where data is often noisy or imbalanced. Model-centric AI is well suited for scenarios where you are delivered clean data that has been perfectly labeled. Raw Data: MinIO is the best solution for collecting and storing raw unstructured data.

AI 52

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Snorkel AI

JULY 12, 2024

This approach can be particularly effective when dealing with real-world applications where data is often noisy or imbalanced. Model-centric AI is well suited for scenarios where you are delivered clean data that has been perfectly labeled. Raw Data: MinIO is the best solution for collecting and storing raw unstructured data.

AI 52

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Mlearning.ai

JUNE 8, 2023

Organize the data into subfolders based on data sources or types. For example, you can have subfolders for raw data, cleaned data, and processed data. Make sure to include a README file specifying the data sources, formats, and any preprocessing steps performed.

Data Science 52

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DagsHub

OCTOBER 23, 2024

Now that you know why it is important to manage unstructured data correctly and what problems it can cause, let's examine a typical project workflow for managing unstructured data. Data Preprocessing Here, you can process the unstructured data into a format that can be used for the other downstream tasks. Unstructured.io

Machine Learning 52

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Tableau

JANUARY 27, 2021

We also reached some incredible milestones with Tableau Prep, our easy-to-use, visual, self-service data prep product. In 2020, we added the ability to write to external databases so you can use clean data anywhere. Tableau Prep can now be used across more use cases and directly in the browser.

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Heartbeat

NOVEMBER 6, 2023

Imagine, if this is a DCG graph, as shown in the image below, that the clean data task depends on the extract weather data task. Ironically, the extract weather data task depends on the clean data task. Weather Pipeline as a Directed Cyclic Graph (DCG) So, how does DAG solve this problem?

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Pickl AI

NOVEMBER 21, 2024

Extensive Documentation : Many of these tools have robust documentation and active communities, making it easier for users to troubleshoot and learn. Step 2: Numerical Computation in MATLAB Once the data is cleaned, you can use MATLAB for heavy numerical computations.

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Dataconomy

JULY 28, 2023

Data preprocessing is essential for preparing textual data obtained from sources like Twitter for sentiment classification ( Image Credit ) Influence of data preprocessing on text classification Text classification is a significant research area that involves assigning natural language text documents to predefined categories.

Power BI 91

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Pickl AI

JULY 12, 2023

Moreover, this feature helps integrate data sets to gain a more comprehensive view or perform complex analyses. Data Cleaning Data manipulation provides tools to clean and preprocess data. Thus, Cleaning data ensures data quality and enhances the accuracy of analyses.

Data Analysis 52

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ODSC - Open Data Science

SEPTEMBER 17, 2024

TensorFlow’s extensive community and robust documentation make it a go-to framework for software engineers exploring deep learning. It’s also one of the first frameworks that software engineers become familiar with due to its vast documentation and ease of use when it comes to integration.

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AWS Machine Learning Blog

MAY 13, 2024

Together, these components enabled both precise document retrieval and high-quality conditional text generation from the findings-to-impressions dataset. We also see how fine-tuning the model to healthcare-specific data is comparatively better, as demonstrated in part 1 of the blog series.

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Towards AI

FEBRUARY 11, 2025

2020) Scaling Laws for Neural Language Models [link] First formal study documenting empirical scaling laws Published by OpenAI The Data Quality Conundrum Not all data is created equal. Why Technical Band-Aids Fail These solutions work until they dont.

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Alation

FEBRUARY 1, 2022

Menninger states that modern data governance programs can provide a more significant ROI at a much faster pace. And simply finding and cleaning data gobbles the vast majority of the time of many analysts in large organizations.

Data Lakes 52

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IBM Journey to AI blog

AUGUST 15, 2023

Building and training foundation models Creating foundations models starts with clean data. This includes building a process to integrate, cleanse, and catalog the full lifecycle of your AI data. A hybrid multicloud environment offers this, giving you choice and flexibility across your enterprise.

Artificial Intelligence 93

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Pickl AI

JULY 24, 2024

Validate Data Perform a final quality check to ensure the cleaned data meets the required standards and that the results from data processing appear logical and consistent. Uniform Language Ensure consistency in language across datasets, especially when data is collected from multiple sources.

Data Quality 52

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DagsHub

AUGUST 2, 2023

ML engineers need access to a large and diverse data source that accurately represents the real-world scenarios they want the model to handle. Insufficient or poor-quality data can lead to models that underperform or fail to generalize well. Gathering high-quality and sufficient data can be time and effort-consuming.

ML 52

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Pickl AI

AUGUST 27, 2024

This community-driven approach ensures that there are plenty of useful analytics libraries available, along with extensive documentation and support materials. For Data Analysts needing help, there are numerous resources available, including Stack Overflow, mailing lists, and user-contributed code.

Data Analysis 52

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DagsHub

FEBRUARY 29, 2024

Data preparation involves multiple processes, such as setting up the overall data ecosystem, including a data lake and feature store, data acquisition and procurement as required, data annotation, data cleaning, data feature processing and data governance.

Data Preparation 52

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Pickl AI

NOVEMBER 2, 2023

Here, we’ll explore why Data Science is indispensable in today’s world. Understanding Data Science At its core, Data Science is all about transforming raw data into actionable information. It includes data collection, data cleaning, data analysis, and interpretation.

Data Scientist 52

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DagsHub

AUGUST 23, 2024

Data quality is crucial across various domains within an organization. For example, software engineers focus on operational accuracy and efficiency, while data scientists require clean data for training machine learning models. Without high-quality data, even the most advanced models can't deliver value.

Data Quality 52

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Pickl AI

DECEMBER 16, 2024

Documenting Objectives: Create a comprehensive document outlining the project scope, goals, and success criteria to ensure all parties are aligned. Cleaning Data: Address any missing values or outliers that could skew results. Techniques such as interpolation or imputation can be used for missing data.

AI 52

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Tableau

JANUARY 27, 2021

We also reached some incredible milestones with Tableau Prep, our easy-to-use, visual, self-service data prep product. In 2020, we added the ability to write to external databases so you can use clean data anywhere. Tableau Prep can now be used across more use cases and directly in the browser.

Tableau 52

Tableau Business Intelligence Business Intelligence Analytics 52

Pickl AI

DECEMBER 10, 2024

Although it disregards word order, it offers a simple and efficient way to analyse textual data. TF-IDF (Term Frequency-Inverse Document Frequency) TF-IDF builds on BoW by emphasising rare and informative words while minimising the weight of common ones. What is Feature Extraction?

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