LLM prompting, visualization frameworks, and GIS in data science
Welcome to the Cutting Edge of Data Science!
As every Monday we will announce the top 5 articles of the week.
The posts selected in this week’s newsletter cover a wide range of topics, from visualization to ETL and LLM engineering.
Let´s go through the top five picks!
(If you´d like to get your article featured in The Deep Hub, read the submission guidelines here).
1. LLM Prompt Engineering for Beginners: What It Is and How to Get Started — Sahin Ahmed
This tutorial by Sahin Ahmed, Data Scientist breaks down the essential aspects of prompt engineering, including understanding LLMs’ capabilities, setting clear and precise goals, and using an iterative process to refine prompts.
You will learn about the basic types of prompts: direction instructions, task completions, few-shot learning, story continuation, and question-answering.
In addition, Sahin will guide you through advanced prompt engineering techniques that you will find useful.
Some of them include:
- Chain of Thought (CoT) prompting
- Tree of Thought (ToT) prompting
(Read the article here).
2. Deciphering the Ideal Dashboard Framework: Navigating Power BI, Plotly, Dash, Streamlit, and Grafana — M Muneeb Ur Rehman
If you want to pursue a career in data science, data visualization is an essential skill you should possess.
In his article, M Muneeb Ur Rehman will introduce you to popular dashboard frameworks used in actuality.
- Power BI: Recommended for non-programmers due to its user-friendly interface and drag-and-drop functionality.
- Grafana: Ideal for users with some programming skills, especially those familiar with SQL or database management. It allows for sophisticated real-time data visualization and is highly customizable, though it can be complex for beginners.
- Streamlit and Dash: Streamlit is highlighted for its simplicity and ease of use, making it a good choice for quick dashboard setups. Dash, on the other hand, offers extensive customization, suited for creating interactive dashboards.
(Read the article here).
3. Streamlining Data Transfers: Python’s Guide to Amazon S3 Cloud Object Storage — Ty Rawls
The next article, published by Ty Rawls talks about Cloud Object Storage on Amazon S3. A popular, web-based cloud storage service.
In the article, Rawls outlines the benefits of using Amazon S3, which include scalability, high durability and availability, robust security features, cost-effectiveness, and support for versioning of objects.
He then provides a step-by-step tutorial on setting up an S3 bucket, creating access keys, and configuring Python to interface with S3 using packages like AWS CLI, Boto3, and Pandas.
The article is intended for users who want to leverage Amazon S3 for data storage and retrieval.
If you`re looking to expand or improve your AWS skills this post is great for you!
(Read the article here).
4. Stellar Algorithms: How AI is Charting the Cosmos of Data in Kepler’s Footsteps — Everton Gomede, PhD
In this post, Everton Gomede, PhD uses the historical context of Kepler’s revolutionary laws of planetary motion as a metaphor to illustrate how AI today navigates vast datasets to uncover patterns and insights across various fields.
The article encapsulates this relationship with a Python-based example to model planetary orbits using machine learning, showcasing how AI can be programmed to replicate a process akin to Kepler’s analysis but applied to synthetic data.
This serves to demonstrate the predictive power and pattern recognition capabilities of AI, echoing Kepler’s work in a modern context.
(Read the article here).
5. Effective Soft Skills Tools That Will Make You An Efficient GIS Data Scientist — Stephen Chege-Terra
We´ll conclude the newsletter with a non-technical article about GIS in data science by Stephen Chege-Tierra Insights.
A GIS (Geographic Information Systems) data scientist is a professional who specializes in analyzing and interpreting complex geographical or spatial data using a combination of data science techniques and GIS technology.
In his post, Stephen talks about the effective soft skills you should have in order to become an efficient GIS data scientist.
Some of them include: problem-solving, the ability to learn new things, collaboration, proper time management, and clear communication.
If you´re concerned about Geographic Information Systems in the field of machine learning, make sure to read Stephen’s article to learn more about it.
(Read the article here).
Thanks for your support! Do you have an article you´d like to share with us? Make sure to send us your piece and we will read it.
Until the next week,
The Deep Hub authors.
