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The Journey of a Senior Data Scientist and Machine Learning Engineer at Spice Money

Analytics Vidhya

Introduction Meet Tajinder, a seasoned Senior Data Scientist and ML Engineer who has excelled in the rapidly evolving field of data science. Tajinder’s passion for unraveling hidden patterns in complex datasets has driven impactful outcomes, transforming raw data into actionable intelligence.

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The Journey of a Senior Data Scientist and Machine Learning Engineer in Fintech Domain

Analytics Vidhya

Introduction Meet Tajinder, a seasoned Senior Data Scientist and ML Engineer who has excelled in the rapidly evolving field of data science. Tajinder’s passion for unraveling hidden patterns in complex datasets has driven impactful outcomes, transforming raw data into actionable intelligence.

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NVIDIA RAPIDS in Cloudera Machine Learning

Cloudera

To try and predict this, an extensive dataset including anonymised details on the individual loanee and their historical credit history are included. As a machine learning problem, it is a classification task with tabular data, a perfect fit for RAPIDS. Get the Dataset. The dataset can be downloaded from: [link].

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Unlock the Power of Your Marketing Data with Snowflake Connector for Google Analytics

Snowflake

Bring your raw Google Analytics data to Snowflake with just a few clicks The Snowflake Connector for Google Analytics makes it a breeze to get your Google Analytics data, either aggregated data or raw data, into your Snowflake account. Here’s a quick guide to get started: 1.

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Simplifying BI pipelines with Snowflake dynamic tables

ThoughtSpot

When created, Snowflake materializes query results into a persistent table structure that refreshes whenever underlying data changes. These tables provide a centralized location to host both your raw data and transformed datasets optimized for AI-powered analytics with ThoughtSpot.

BI 111
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From Schemaless Ingest to Smart Schema: Enabling SQL on Raw Data

Rockset

The application you're implementing needs to analyze this data, combining it with other datasets, to return live metrics and recommended actions. But how can you interrogate the data and frame your questions correctly if you don't understand the shape of your data? Where do you begin?

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The Downfall of the Data Engineer

Maxime Beauchemin

Traditionalists would suggest starting a data stewardship and ownership program, but at a certain scale and pace, these efforts are a weak force that are no match for the expansion taking place.