Industry Roundup #2: AI Agents for Data Work, Full-Stack Data Scientists & Old languages are Revived
Welcome to DataFramed Industry Roundups! In this series of episodes, Adel & Richie sit down to discuss the latest and greatest in data & AI. In this episode, we touch upon AI agents for data work, will the full-stack data scientist make a return, old languages making a comeback, Python's increase in performance, what they're both thankful for, and much more.
Links mentioned in the show:
Fractal’s Data Science Agent: Arya - https://fractal.ai/genesis-ai-agent-arya/
Article: What Makes a True AI Agent? Rethinking the Pursuit of Autonomy - https://towardsdatascience.com/what-makes-a-true-ai-agent-rethinking-the-pursuit-of-autonomy-547ab54f4995
Cassie Kozyrkov on DataFramed - https://www.datacamp.com/podcast/making-better-decisions-using-data-and-ai-with-cassie-kozyrkov-googles-first-chief-decision-scientist
TIOBE Index for November 2024 - https://www.tiobe.com/tiobe-index/
Community discussion on Fortran - https://www.reddit.com/r/fortran/comments/1gms1ls/how_did_fortran_become_a_top_10_language_again/
Tutorial: High Performance Data Manipulation in Python: pandas 2.0 vs. polars - https://www.datacamp.com/tutorial/high-performance-data-manipulation-in-python-pandas2-vs-polars
New to DataCamp?
Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile
Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business
0:00 Introduction
0:15 AI vs. Data
1:59 The Rise of Data Science Agents
4:25 Emerging AI Tools in Data Science
7:33 Changing Data Science Roles
13:36 Full-Stack Data Scientists
20:53 Programming Trends
26:34 Rust and the Future of Data Engineering
28:02 SQL’s Evolution
29:08 Closing Thoughts
31:15 Teasers for Future Episodes
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SQL Server Tutorial: Date manipulation
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R Tutorial: Intermediate Interactive Data Visualization with plotly in R
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R Tutorial: Adding aesthetics to represent a variable
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R Tutorial: Moving Beyond Simple Interactivity
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Python Tutorial: Why use ML for marketing? Strategies and use cases
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Python Tutorial: Preparation for modeling
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Python Tutorial: Machine Learning modeling steps
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R Tutorial: The prior model
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R Tutorial: An Introduction to plotly
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Python Tutorial: Customer Segmentation in Python
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Python Tutorial: Correlation of Two Time Series
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Python Tutorial: Customizing axes
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Python Tutorial: Legends, annotations, & styles
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Python Tutorial: Introduction to iterators
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Python Tutorial: Playing with iterators
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Python Tutorial: Using iterators to load large files into memory
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SQL Tutorial: Introduction to Relational Databases in SQL
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SQL Tutorial: Tables: At the core of every database
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SQL Tutorial: Update your database as the structure changes
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Python Tutorial: Classification-Tree Learning
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Python Tutorial: Decision-Tree for Classification
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Python Tutorial: Decision-Tree for Regression
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Python Tutorial: Census Subject Tables
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Python Tutorial: Census Geography
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Python Tutorial: Using the Census API
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R Tutorial: A/B Testing in R
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Chapters (11)
Introduction
0:15
AI vs. Data
1:59
The Rise of Data Science Agents
4:25
Emerging AI Tools in Data Science
7:33
Changing Data Science Roles
13:36
Full-Stack Data Scientists
20:53
Programming Trends
26:34
Rust and the Future of Data Engineering
28:02
SQL’s Evolution
29:08
Closing Thoughts
31:15
Teasers for Future Episodes
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Tutor Explanation
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