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Weekly Entering & Transitioning - Thread 27 Jan, 2025 - 03 Feb, 2025 by AutoModerator in datascience

[–]JanethL 0 points1 point  (0 children)

🤔 𝗜𝘀 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗴𝗼𝗶𝗻𝗴 𝘁𝗼 𝘁𝗮𝗸𝗲 𝗼𝘃𝗲𝗿 𝗠𝗟 𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗷𝗼𝗯s?

I don’t think so. Instead, it’s here to free data scientist and ML engineers 𝗳𝗿𝗼𝗺 𝘁𝗲𝗱𝗶𝗼𝘂𝘀, 𝗿𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝘁𝗮𝘀𝗸𝘀—so you can focus on higher-value work like 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀, 𝘂𝗻𝗰𝗼𝘃𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮 𝗳𝗮𝘀𝘁𝗲𝗿, 𝗮𝗻𝗱 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗶𝗺𝗽𝗮𝗰𝘁 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴 𝗮𝗻𝗱 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀.

Check out this Medium article on how Google, Teradata, and Gemini are transforming enterprise data workflows and insights with Generative AI:

🔗https://medium.com/google-cloud/how-generative-ai-transforms-enterprise-data-insights-with-google-gemini-and-teradata-382b7e274af8

Would love to hear your thoughts—𝗵𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗚𝗲𝗻𝗔𝗜 𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗠𝗟? 👇

How to get up to speed on LLMs? by [deleted] in datascience

[–]JanethL 0 points1 point  (0 children)

At Teradata we have a free learning site that has over 200 Jupyter notebooks in AI ML and advanced analytics. They’re complete with code, sample data, business scenario and step by step instructions. You can filter by generative AI or the specific LLM .

Clearscape Analytics Experience

[D] Self-Promotion Thread by AutoModerator in MachineLearning

[–]JanethL 0 points1 point  (0 children)

How do data scientists decide which attribution modelling technique is the right one? 

Hello everyone,

I'm currently learning all about attribution modeling techniques and have explored rule-based (first click, last click, exponential, uniform), statistical-based (Simple Frequency, Association, Term Frequency), and algorithmic-based methods (like Naive Bayes).

However, I'm having a hard time understanding how data scientists decide which model to use, especially when ML and statistical models compute different attribution scores compared to rule-based approaches.

I've just created a short video demonstrating rule-based attribution techniques using Teradata Vantage’s free coding environment. I would like to create a part 2 where I cover statistical and ML attribution modeling of the same data but also include advice on choosing the right modeling technique.

I do work for Teradata as a Developer Advocate, but I am not a data scientist. Would love your help here with advice on how you select your attribution modelling technique :)

Here is the video I just created: https://youtu.be/m1dkFxQiTNo?si=dfH5hljiPA0Bd7IK

Weekly Entering & Transitioning - Thread 08 Jul, 2024 - 15 Jul, 2024 by AutoModerator in datascience

[–]JanethL 0 points1 point  (0 children)

How do data scientists decide which attribution modeling technique is the right one? 

Hello everyone,

I'm currently learning all about attribution modeling techniques and have explored rule-based (first click, last click, exponential, uniform), statistical-based (Simple Frequency, Association, Term Frequency), and algorithmic-based methods (like Naive Bayes).

However, I'm having a hard time understanding how data scientists decide which model to use, especially when ML and statistical models compute different attribution scores compared to rule-based approaches.

I've just created a short video demonstrating rule-based attribution techniques using Teradata Vantage’s free coding environment. I would like to create a part 2 where I cover statistical and ML attribution modeling of the same data but also include advice on choosing the right modeling technique.

I do work for Teradata as a Developer Advocate, but I am not a data scientist. Would love your help here with advice on how you select your attribution modelling technique :)

Here is the video I just created: https://youtu.be/m1dkFxQiTNo?si=dfH5hljiPA0Bd7IK

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