Remove 2014 Remove Clustering Remove Data Visualization
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How to optimize your LinkedIn as a Data Scientist?

Pickl AI

Data Scientist LinkedIn Profile Example Marla Smith, Senior Data Scientist at ABC Company Summary: Experienced data scientist with a strong background in statistical analysis, machine learning, and data visualization. Passionate about leveraging data to drive business decisions and improve customer experience.

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Visualizing the Tour de France in the year I tackle the route

Cambridge Intelligence

In the lead up to this, my day job as a software developer gave me a break from hard training rides, but my love of cycling sparked a mini side project: building web apps with the data visualization tools I help to develop, and using them to analyze and visualize Tour de France data.

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How to choose a graph database: we compare 6 favorites

Cambridge Intelligence

That’s why our data visualization SDKs are database agnostic: so you’re free to choose the right stack for your application. Visualizing data with ArangoDB Azure Cosmos DB “A graph database service that can be used to store massive graphs with billions of vertices and edges.

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Linear Regression for tech start-up company Cars4U in Python

Mlearning.ai

These are common Python libraries used for data analysis and visualization. Year: More than half the cars in the data were manufactured in or after 2014. Brand: Most of the cars in the data belong to Maruti or Hyundai. I began by importing pandas, matplotlib, and seaborn into my notebook.

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Must-Have Prompt Engineering Skills for 2024

ODSC - Open Data Science

Data science methodologies and skills can be leveraged to design these experiments, analyze results, and iteratively improve prompt strategies. Using skills such as statistical analysis and data visualization techniques, prompt engineers can assess the effectiveness of different prompts and understand patterns in the responses.

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10 takeaways from 10 years of data science for social good

DrivenData Labs

Looking back ¶ When we started DrivenData in 2014, the application of data science for social good was in its infancy. There was rapidly growing demand for data science skills at companies like Netflix and Amazon. Deep learning - It is hard to overstate how deep learning has transformed data science.

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Analyzing the history of Tableau innovation

Tableau

VizQL’s powerful combination of query and visual encoding led me to the following six innovation vectors in my analysis of Tableau’s history: Falling under the category of query , we’ll discuss connectivity , multiple tables , and performance. The Data Tab was added in v8.2 Gestalt properties including clusters are salient on scatters.

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