Senior Manager - Data Science and Engineering at Apple

I lead the data science and engineering team at Apple Finance - Services and Infra. The team focuses on translating business problems and requirements into data products using advanced statistical and machine learning models to support and help Apple leadership make data-driven decisions.

I am also an open-source contributor and the author of Hands-On Time Series Analysis with R (Packt publication) and several R packages for time series analysis and machine learning applications. Currently working on my next book, Applied Time Series Analysis and Forecasting, which focuses on forecasting at scale. 

Also available on:
Github: https://github.com/RamiKrispin
Medium: https://medium.com/@rami.krispin
Mastodon: https://mstdn.social/@ramikrispin

Rami Krispin

Imagine going to a restaurant and starting with dessert—then ordering the main course.

That’s roughly how university degrees are structured today, in my opinion: a heavy focus on theory, with far less emphasis on the practical tools students actually need in the job market.

Personally, I believe the very first course in any STEM track should be tooling—Git, the command line, shell, Bash, and related fundamentals. Having this foundation early would allow students to gain real, hands-on experience throughout their degree, not just after graduation.

A few years ago, three MIT PhD students recognized this gap and created a course called “The Missing Semester.” The course focuses on essential practical skills like the CLI, Git, Vim, and shell tools. It first launched in 2019, ran for two years, and last week the original instructors returned to MIT to teach an updated version.

The full course is available on YouTube:
https://lnkd.in/gh4KRiD8

Highly recommended—especially if you’re at the beginning of your journey.

#datascience #ai #mlops

Senior Manager - Data Science and Engineering at Apple | Docker Captain | LinkedIn Learning Instructor

Hi Bay Area data folks! 👋

Bay Area Data & AI is hosting an in-person, casual networking event focused on data, AI, careers, and products.

I’m excited to co-host this event with Manisha Arora, Devshree Bharatia, Vishakha Sadhwani,  Venkata Naga Sai Kumar Bysani, and  Sriram Arumelli.

📅 When: Jan 24 (Sat) at 10:00 AM PST
 📍 Where: Sunnyvale Baylands Park

👉 More details & RSVP: https://luma.com/kgsx6o61

Hope to see you there!

#ai #data #datascience

How to make your Python Docker container slimmer 🐳👇🏼
 
If you’ve ever deployed Python containers only to watch them balloon to several GBs, you’re not alone — I once hit a 5 GB Python image (thank you, PyTorch dependencies 😅). But with a few simple optimizations, you can dramatically reduce image sizes, speed up deployments, and lower cloud costs.

Here are practical tips that have worked well:
1️⃣ Choose a Lean Base Image
Start smaller:
 ✔️ python:3.x-slim instead of the full Python image
 ✔️ python:3.x-alpine if compatible with your dependencies (and you are a brave developer 😆) 
 Smaller base = smaller final image and faster pulls. 
2️⃣ Use Multi-Stage Builds (Big Win!)
Multi-stage builds let you install tools and build dependencies in one stage, then only copy what’s needed into a clean final image. This can reduce the size by 60-90% compared to single-stage builds. 
3️⃣ Minimize Layers
Every RUN / COPY / ADD adds a layer.
 ➡️ Combine related commands with && to reduce layers and eliminate intermediate files (although you won't gain much size reduction).
 Example:

RUN apt-get update && \
    apt-get install -y build-deps && \
    pip install -r requirements.txt && \
    apt-get purge -y build-deps && \
    rm -rf /var/lib/apt/lists/*

Fewer layers = smaller image. 

4️⃣ Use .dockerignore to Cut Build Context
Exclude unnecessary files (.git, logs, IDE folders, virtual environments) so Docker doesn’t send them into the build context — this keeps images lean and speeds up builds. 
5️⃣ Install Only What You Need
Separate dev dependencies from production deps.
 Use flags like --no-dev or careful requirements files so you install only what your app actually needs at runtime. 

💡 Why it matters: smaller images → faster CI/CD → less storage & network cost → reduced attack surface → happier developers.

If you want to learn more, please check my tutorial about multi-stage build:

https://lnkd.in/gHTpdt2S

#mlops #docker #devops #python

Open article: Introduction to Multi-Stage Image Build for Python by medium.com

Introduction to Multi-Stage Image Build for Python

Introduction to JAX and Flax NNX - the framework behind Google Gemini 🚀👇🏼

JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. Flax NNX is a neural network library and ecosystem built on top of JAX.

The following tutorial from Google for Developers provides an introduction to the JAX AI stack, and it covers topics such as:
✅ JAX’s functional transformations
✅ Debugging techniques
✅ Scaling models across distributed hardware
✅ Optax for optimization and checkpoints

📽️: https://lnkd.in/g8kfrFmg

#ai #llm #machinelearning

My weekly newsletter is out! 

This week’s agenda:
🔹 Open Source of the Week - Pandas 3 release
🔹 New learning resources - Introduction to JAX and Flax NNX, OpenCode crash course, introduction to FastAPI
🔹 Book of the week - Introduction to Machine Learning Systems by Prof. Vijay Janapa Reddi

📌 Subscribe to receive weekly updates: https://lnkd.in/gb3P8YdE
🎯 Learn how to build an SQL AI agent: https://lnkd.in/g4_W8na8

#python #datascience #machinelearning #ai

This week's agenda: Open Source of the Week - Pandas 3 release New learning resources - Introduction to JAX and Flax NNX, OpenCode crash course, intro

Open article: Pandas 3 is Out, Introduction to Machine Learning Systems, New Tutorials | Issue 72 by Rami Krispin • 3 min read

Pandas 3 is Out, Introduction to Machine Learning Systems, New Tutorials | Issue 72

My weekly newsletter is out! 

This week’s agenda:
🔹 Open Source of the Week - The SQLGlot project
🔹 New learning resources - Setting Memory for AI application, MIT deep learning course, Statistical Rethinking 2026, introduction to AI engineering, LlamaIndex crash course
🔹 Book of the week - Statistical Analysis with Python For Dummies by Joseph Schmuller

📌 Subscribe to receive weekly updates: https://lnkd.in/gb3P8YdE
🎯 Learn how to build an SQL AI agent: https://lnkd.in/g4_W8na8

#ai #python #sql

This week's agenda: Open Source of the Week - The SQLGlot project New learning resources - Setting Memory for AI application, MIT deep learning course

Open article: Statistical Analysis with Python, SQLGlot & MIT Deep Learning | Issue 71 by Rami Krispin • 5 min read

Statistical Analysis with Python, SQLGlot & MIT Deep Learning | Issue 71

Many of the AI concepts are simpler than they seem. Adding to an AI application's "memory" is one of those things. 

By default, LLMs are stateless, meaning each conversion is independent from the previous ones. Here is how you turn your AI application with less than five lines of code:
1️⃣ Set an empty list to store the previous conversions
2️⃣ Add to the prompt template this list as an argument
3️⃣ After every response of the LLM update the list with the user question and LLM response

This is, of course, not a production-ready method. There are a few limitations to this method, such as an increase in the number of tokens used and a potential drop in the context as the conversion takes a different direction.

The slides below, generated with NotebookLM, summarise this process and are based on a tutorial I created this week. The next tutorial will focus on efficient management of the token size.

This tutorial and others are available in the AIOps Newsletter: https://lnkd.in/gghdpeEF

#ai #llm #datascience

Building Memory to LLM Applications

I share an 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞 project every week in my 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫. This week’s focus is on the SQLGlot project 👇🏼

SQLGlot is a versatile library for reading, manipulating, formatting, and translating SQL across many dialects. It ingests SQL text, generates an abstract syntax tree (AST), and produces syntactically correct SQL for a target dialect such as DuckDB, Spark, Snowflake, BigQuery, Postgres, MySQL, and many others. It also includes features for query analysis, syntax error detection, and basic execution.

Why It’s Useful 🎯
✅ Dialect Translation: Simplifies migrating SQL across engines by automatically transpiling queries.
✅ Parser & Formatter: Can parse diverse SQL inputs, ensure correct syntax, and produce clean, standardized output.
✅ AST-Level Manipulation: Lets developers programmatically inspect and modify query structures, enabling tooling such as linters, analyzers, and automated refactors.
✅ Optimizer & Analyzer: Includes basic optimization rules and expression tree traversal utilities.
✅ Execution Engine: Although not designed for performance, SQLGlot can execute queries against Python data structures, useful for testing or in-memory analytics.

Key Features 🛠️ 
🔹Transpile between 30+ SQL dialects (including Postgres, Spark, Hive, Snowflake, BigQuery, SQLite, TSQL, and more).
🔹Detailed parser errors & warnings with context.
🔹Extensible dialect system, so you can plug in support for custom SQL syntaxes.
🔹 AST diffing, helping compare and transform queries semantically.
Benchmarks show performance that’s competitive with other Python SQL parsers.

More details are available on the project repo: https://lnkd.in/gb9UxetB

📌 Join 34k subscribers to receive weekly updates: https://lnkd.in/gb3P8YdE

VScode Dev Containers Extension 🐳👇🏼 

One of the main reasons I started to use VScode was the Dev Containers extension. This extension enables the seamless launch and development of a project inside a container.

Here are the main advantages of this extension:
✅ It is highly customizable and flexible provide you with complete control over your project settings. 
✅ Issolation - From the environment setting to the fonts and theme, it enables you to isolate your project VScode settings from your regular settings
✅ Reproducability - beyond your container, it enables you to reproduce your complete settings from fonts to extensions. This is great when you are working on multiple machines and cross-collaborating with others 
✅ Consistent environments — All developers use the exact same OS, tools, runtimes, and libraries. No more “works on my machine” problems. 
 ✅ Faster onboarding — New team members can be productive in minutes without manual setup. 
 ✅ Infrastructure as code — Dev container configs live with your project and can be version-controlled. 

Pro tip 🎯
Set GitHub templates for different use cases, for example, I have the following templates:
Python: https://lnkd.in/gSTcBbcK
R: https://lnkd.in/gKzaBC7T

If you want to get started with the Dev Containers extension, please check the following tutorial:
https://lnkd.in/gtCtrBqM 

#docker #mlops #datascience

Open article: Getting Started with the Dev Containers Extension by medium.com

Getting Started with the Dev Containers Extension

I help people and organizations understand cities, networks, and complex systems using geospatial data and network science.

I am the founder of Geospatial Data Consulting, a Data Science PhD, and the author of two #1 Amazon bestsellers (Connecting the Dots and Geospatial Data Science Essentials). I also teach spatial data science as a LinkedIn Learning instructor, reaching tens of thousands of learners worldwide.

My work has been featured in The New York Times, GQ, New Scientist, and Towards Data Science, and I have collaborated with institutions including Bell Labs, Oxford Economics, and the European Commission.
I am passionate about making complex data insights visual and accessible - from mapping global trends to modeling the invisible forces that shape everyday life.

Milan Janosov

[𝐂𝐎𝐔𝐑𝐒𝐄 𝐠𝐢𝐯𝐞-𝐚𝐰𝐚𝐲]

New year, new course give-away. Last year was extremely busy with new courses - and after so much hard work with @Linekdin Learning, now we have a decent learning path. Its nowhere near finished - yet covers all major pillars to get you started in geospatial data science - from the basics to  advanced visuailzations and ML models. 

To help with course accessibility, here I am doing the usual give-away: 


#share this post to join the raffle, closing one week from now. 


And for a teaser on what geospatial data science is good for: https://zurl.co/Jeg7S 
 


🧱 𝐂𝐨𝐫𝐞 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 

1. 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞
https://zurl.co/XsLZQ
If you are completely new to Python and would like to pick up the basics needed for spatial analytics in just a few hours, this one is for you. 

2. 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 
https://zurl.co/uo4ZU
If you are already on board with Python and want to start learning the basics of geospatial, then this course is for you!

3. 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐑𝐚𝐬𝐭𝐞𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧   
https://zurl.co/4s020 After the basics of vector data comes raster data in Python!

4. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 
https://zurl.co/wAmug
In this course, we are combining all that we learned throughout the previous ones and learning a lot more in a more advanced way. 

5. 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 
https://zurl.co/Zuo52
As a cherry on top, the last of my current core courses extends ML - spatial stats, regressions, classifications, and predictions to the spatial domain.  

6. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 https://zurl.co/IxhJW
And the latest piece - to level up your mapping skills in Python, from interactive to 3D maps! 
  


🎯 𝐓𝐨𝐩𝐢𝐜𝐚𝐥 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 

1. 𝐔𝐫𝐛𝐚𝐧 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 
https://zurl.co/ERVVM 
If you would like to explore the topic of urban analytics using example data from OSM, this is for you.

2. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 - 𝐀𝐧 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧  
https://zurl.co/SlLHU While not directly geospatial, it is related, and will soon connect directly, how to visualize networks effectively.

3.  𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 
https://zurl.co/HlI3W After visualizing networks, here we level up the game and do it all in Python!

Founder · Data Science PhD · TEDx Speaker · #1 Amazon Best-Selling Author · Forbes 30 Under 30 · LinkedIn Learning Instructor

No alternative text description for this image

GIS Learning journeys - Stanford University published the following courses:


1. 𝐆𝐨𝐨𝐠𝐥𝐞 𝐄𝐚𝐫𝐭𝐡 𝐄𝐧𝐠𝐢𝐧𝐞 101  - I https://lnkd.in/ehC2schr

2. 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐇𝐢𝐬𝐭𝐨𝐫𝐢𝐜𝐚𝐥 𝐌𝐚𝐩𝐬 𝐎𝐧𝐥𝐢𝐧𝐞  - https://lnkd.in/ekq9rAWG

3. 𝐀𝐫𝐜𝐆𝐈𝐒 𝐎𝐧𝐥𝐢𝐧𝐞 101  - https://lnkd.in/ewj29Z6E

4. 𝐆𝐞𝐨𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐢𝐧𝐠  - https://lnkd.in/eucMCwYv

5. 𝐐𝐆𝐈𝐒 101  - https://lnkd.in/eX25ASF6
 

While on LinkedIn Learning : 


1. 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠  -  https://lnkd.in/dH7qB-Ny 

2. 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐑𝐚𝐬𝐭𝐞𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 -  https://lnkd.in/dPPceQGd 

3. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 - https://lnkd.in/dwiSZBMz 

4. 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 - https://lnkd.in/dr59AVUD 

5. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 - https://lnkd.in/dndRaHEE

One of my favourite finds from 2025 was the 𝐜𝐢𝐭𝐲2𝐠𝐫𝐚𝐩𝐡  package giving a lightweight Python toolbox to analyze and visualize all sorts of urban networks really fast and clean.

The tool: https://lnkd.in/dtdVWHeS 

My tutorial: https://lnkd.in/dFhmJNfz

The applications of geospatial data science likely have one of the largest impacts on humanity when it comes to understanding and managing climate risk. If you are interested in this direction, check out 𝐂𝐥𝐢𝐦𝐚𝐭𝐞 𝐑𝐢𝐬𝐤 𝐈𝐧𝐝𝐞𝐱 2026 by Germanwatch e.V.: 

https://lnkd.in/dpharb_B

𝐂𝐥𝐢𝐦𝐚𝐭𝐞 𝐑𝐢𝐬𝐤 𝐈𝐧𝐝𝐞𝐱 2026

Did you know that all my courses are coming with certificates? 

Check-out the courses and the certificates below, and if you want to win a free seat - have a look at my post from yesterday!

And for a teaser on what geospatial data science is good for: https://lnkd.in/dBTUqctW 

🧱 𝐂𝐨𝐫𝐞 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 

1. 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞
https://lnkd.in/gwfh9Qim

2. 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 
https://lnkd.in/dH7qB-Ny 

3. 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐑𝐚𝐬𝐭𝐞𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 
https://lnkd.in/dPPceQGd

4. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 
https://lnkd.in/dwiSZBMz

5. 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 
https://lnkd.in/d3JWqmZn 

6. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧

https://lnkd.in/dndRaHEE
  

🎯 𝐓𝐨𝐩𝐢𝐜𝐚𝐥 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 

1. 𝐔𝐫𝐛𝐚𝐧 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 
https://lnkd.in/dDYukxaQ 

2. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 - 𝐀𝐧 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 
https://lnkd.in/gfEKTVsH

3. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 
https://lnkd.in/dabMznbp

Raster data formats - more details by  Lawrence X.:

https://lnkd.in/dtSyznJE

An exciting map from Esri last year - where are the 25 mill Christmas trees harvested in the U.S: https://ow.ly/WcIg50XMO2F

[𝐋𝐄𝐀𝐑𝐍𝐈𝐍𝐆 𝐉𝐎𝐔𝐑𝐍𝐄𝐘] 

I started in physics, moved into network and data science, and eventually arrived at geospatial.

The path may not look obvious, but it is actually very consistent. A big milestone for me on how I map the world, and in how I think we should learn and teach complex systems data, networks, and physical space: Building intuition rather than stock-piling technical tools. The structure and materials below are meant to make outline that across multiple platforms.


🎁 Also, as usual, I am #giving #away 5 free course seats and 5 book copies this time again. To enter the raffle, share this post.


🔴 𝐓𝐄𝐃𝐱 𝐭𝐚𝐥𝐤- How I think about data, networks, and maps - bringing together ideas that previously lived across many projects.
https://lnkd.in/dBEEixpx

➡️ 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 - Technical walkthroughs, coding sessions, and map all over - and in Python
https://lnkd.in/dBTUqctW


📚 𝐁𝐨𝐨𝐤𝐬

𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐝𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐜𝐞 - Looking for the quickest entry to geospatial analytics? 
https://lnkd.in/dy-7m_zz

𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐨𝐭𝐬 - The big picture - from networks to maps
https://lnkd.in/dPtjcxX9


🧱 𝐂𝐨𝐫𝐞 𝐜𝐨𝐮𝐫𝐬𝐞𝐬
 
1. 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 - If you are completely new to Python
 https://lnkd.in/gwfh9Qim

2. 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 - If you are already on board with Python and want to start learning the basics of geospatial
https://lnkd.in/dH7qB-Ny

3. 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐑𝐚𝐬𝐭𝐞𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 - After the basics of vector data comes raster data in Python
https://lnkd.in/dPPceQGd

4. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 - Advanced spatial data processing techniques here
https://lnkd.in/dwiSZBMz

5. 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 - Regressions, classifications, predictions and more in the spatial domain
https://lnkd.in/d3JWqmZn

 
🎯 𝐓𝐨𝐩𝐢𝐜𝐚𝐥 𝐜𝐨𝐮𝐫𝐬𝐞𝐬

1. 𝐔𝐫𝐛𝐚𝐧 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 - Urban analytics using example data from OSM
https://lnkd.in/dDYukxaQ 

2. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 - While not directly geospatial, it is related, and will soon connect directly, how to visualize networks effectively.
https://lnkd.in/gfEKTVsH

3. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 - After visualizing networks, here we move on to do it all in Python
https://lnkd.in/dabMznbp

4. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 - Leveling up your mapping viz skill from the #30DayMapChallenge to the next client meetings
https://lnkd.in/dndRaHEE



#datascience 
#geospatial
#gis

Just kidding, I love excel!

Happy Monday!

River maps never cease to amaze me. I caught this one shared by Esri created by @Adam Thomas from Johns Hopkins University:  𝗪𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗪𝗮𝘁𝗲𝗿 𝗙𝗹𝗼𝘄𝘀 

Interactive map: https://ow.ly/c9Xm50WTz9b


My Python river map tutorial: https://lnkd.in/d5kyw7sK

𝐀𝐥𝐥 𝐭𝐡𝐞 𝐑𝐨𝐦𝐞𝐬 𝐋𝐞𝐚𝐝 𝐭𝐨 𝐑𝐨𝐚𝐝𝐬: 𝐔.𝐒. 𝐆𝐍𝐈𝐒 𝐓𝐨𝐩𝐨𝐧𝐲𝐦𝐲 𝐌𝐚𝐩 | #30𝐃𝐚𝐲𝐌𝐚𝐩𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 (24/30)

Here I explored U.S. toponymy using the Geographic Names Information System (GNIS) to make a practical joke and reverse the age-old saying using city names from the US: showing how all the Romes can lead to Roads.


𝐖𝐚𝐥𝐤-𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐨𝐧 𝐘𝐨𝐮𝐭𝐮𝐛𝐞: https://lnkd.in/dnZBXHiN

𝐂𝐨𝐝𝐞 𝐨𝐧 𝐒𝐮𝐛𝐬𝐭𝐚𝐜𝐤: https://lnkd.in/dbJ36anZ



#30DayMapChallenge #Toponymy #datasscience #gis

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Rami Krispin's LinkedIn Performance

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