Mlearning.ai

APRIL 25, 2023

Photo by Juraj Gabriel on Unsplash Data analysis is a powerful tool that helps businesses make informed decisions. In this blog, we’ll be using Python to perform exploratory data analysis (EDA) on a Netflix dataset that we’ve found on Kaggle. The type column tells us if it is a TV show or a movie. df.isnull().sum()

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Data Analysis Data Analysis Python Exploratory Data Analysis 52

Smart Data Collective

OCTOBER 17, 2022

Its underlying Singer framework allows the data teams to customize the pipeline with ease. It detaches from the complicated and computes heavy transformations to deliver clean data into lakes and DWHs. . K2View leaps at the traditional approach to ETL and ELT tools.

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Data Pipeline Data Warehouse ETL Exploratory Data Analysis 145

Pickl AI

FEBRUARY 27, 2023

Data quality is critical for successful data analysis. 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. However, ignoring this aspect can give you inaccurate results.

Data Analyst 52

Data Analyst Exploratory Data Analysis Data Scientist EDA 52

Pickl AI

DECEMBER 3, 2024

Raw data often contains inconsistencies, missing values, and irrelevant features that can adversely affect the performance of Machine Learning models. Proper preprocessing helps in: Improving Model Accuracy: Clean data leads to better predictions. Loading the dataset allows you to begin exploring and manipulating the data.

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Python ML ML Exploratory Data Analysis 52

Dataconomy

JULY 28, 2023

Data scientists must decide on appropriate strategies to handle missing values, such as imputation with mean or median values or removing instances with missing data. The choice of approach depends on the impact of missing data on the overall dataset and the specific analysis or model being used.

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Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Dataconomy

AUGUST 16, 2023

” The answer: they craft predictive models that illuminate the future ( Image credit ) Data collection and cleaning : Data scientists kick off their journey by embarking on a digital excavation, unearthing raw data from the digital landscape.

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Data Scientist Data Science Machine Learning Machine Learning 172

AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

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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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AWS Data Preparation Azure ML 123

phData

JUNE 26, 2024

Data engineers can prepare the data by removing duplicates, dealing with outliers, standardizing data types and precision between data sets, and joining data sets together. Using this cleaned data, our machine learning engineers can develop models to be trained and used to predict metrics such as sales.

Machine Learning 52

Machine Learning Machine Learning Data Engineering Data Engineering 52

Snorkel AI

JULY 3, 2023

While there are a lot of benefits to using data pipelines, they’re not without limitations. Traditional exploratory data analysis is difficult to accomplish using pipelines given that the data transformations achieved at each step are overwritten by the proceeding step in the pipeline. JG : Exactly.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Machine Learning Machine Learning 52

Snorkel AI

JULY 3, 2023

While there are a lot of benefits to using data pipelines, they’re not without limitations. Traditional exploratory data analysis is difficult to accomplish using pipelines given that the data transformations achieved at each step are overwritten by the proceeding step in the pipeline. JG : Exactly.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Data Scientist Machine Learning 52

Snorkel AI

JULY 3, 2023

While there are a lot of benefits to using data pipelines, they’re not without limitations. Traditional exploratory data analysis is difficult to accomplish using pipelines given that the data transformations achieved at each step are overwritten by the proceeding step in the pipeline. JG : Exactly.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Data Scientist Machine Learning 52

Pickl AI

DECEMBER 16, 2024

Step 3: Data Preprocessing and Exploration Before modeling, it’s essential to preprocess and explore the data thoroughly.This step ensures that you have a clean and well-understood dataset before moving on to modeling. Cleaning Data: Address any missing values or outliers that could skew results.

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AI AI Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Data Cleaning: Raw data often contains errors, inconsistencies, and missing values. Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Visualisation: Effective communication of insights is crucial in Data Science.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

JULY 20, 2023

Here are some project ideas suitable for students interested in big data analytics with Python: 1. Kaggle datasets) and use Python’s Pandas library to perform data cleaning, data wrangling, and exploratory data analysis (EDA).

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Mlearning.ai

JUNE 28, 2023

Finding the Best CEFR Dictionary This is one of the toughest parts of creating my own machine learning program because clean data is one of the most important parts. Exploratory Data Analysis This is one of the fun parts because we get to look into and analyze what’s inside the data that we have collected and cleaned.

Machine Learning 52

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Snorkel AI

MAY 12, 2023

To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance. This is to say that clean data can better teach our models. Another benefit of clean, informative data is that we may also be able to achieve equivalent model performance with much less data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance. This is to say that clean data can better teach our models. Another benefit of clean, informative data is that we may also be able to achieve equivalent model performance with much less data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Heartbeat

MAY 29, 2023

This step involves several tasks, including data cleaning, feature selection, feature engineering, and data normalization. It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model.

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Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Heartbeat

MARCH 13, 2023

It is important to experience such problems as they reflect a lot of the issues that a data practitioner is bound to experience in a business environment. We first get a snapshot of our data by visually inspecting it and also performing minimal Exploratory Data Analysis just to make this article easier to follow through.

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ML ML Exploratory Data Analysis Machine Learning 59

Dataconomy

MARCH 5, 2025

Roles and responsibilities of a data scientist Data scientists are tasked with several important responsibilities that contribute significantly to data strategy and decision-making within an organization. Analyzing data trends: Using analytic tools to identify significant patterns and insights for business improvement.

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Data Scientist Citizen Data Scientist Exploratory Data Analysis Machine Learning 91