Remove Clean Data Remove Data Visualization Remove ML
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Journeying into the realms of ML engineers and data scientists

Dataconomy

They employ statistical and mathematical techniques to uncover patterns, trends, and relationships within the data. Data scientists possess a deep understanding of statistical modeling, data visualization, and exploratory data analysis to derive actionable insights and drive business decisions.

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ML | Data Preprocessing in Python

Pickl AI

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. Matplotlib/Seaborn: For data visualization.

Python 52
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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

Flipboard

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. Next, we present the data preprocessing and other transformation methods applied to the dataset.

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Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

Flipboard

Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics. Data scientist experience In this section, we cover how data scientists can connect to Snowflake as a data source in Data Wrangler and prepare data for ML.

AWS 123
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Self-Service Analytics for Google Cloud, now with Looker and Tableau

Tableau

“This partnership makes data more accessible and trusted. With Looker’s secure, trusted and highly performant data governance capabilities, we can augment Tableau’s world-class data visualization capabilities to enable data-driven decisions across the enterprise. Operationalizing Tableau Prep flows to BigQuery.

Tableau 138
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Self-Service Analytics for Google Cloud, now with Looker and Tableau

Tableau

“This partnership makes data more accessible and trusted. With Looker’s secure, trusted and highly performant data governance capabilities, we can augment Tableau’s world-class data visualization capabilities to enable data-driven decisions across the enterprise. Operationalizing Tableau Prep flows to BigQuery.

Tableau 98
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Data Wrangling with Python

Mlearning.ai

Pandas is a powerful data manipulation library in Python, which we'll be using to load, transform and analyze the data. We'll also use numpy and matplotlib libraries for numerical computations and data visualization. data = data.dropna() We can also use the drop_duplicates() method to remove duplicated rows.