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Data and AI Central

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Horizon AI

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Factored

Factored (backed by Andrew Ng's AI Fund and deeplearning.ai) helps leading tech companies select, upskill, and build world-class data science, machine learning and AI engineering teams much faster and more cost effectively. Our engineers have been personally vetted, educated, and mentored by some of the most talented and recognized AI educators and engineers from Silicon Valley, Stanford University and deeplearning.ai.

Data Analyst

Rappi

BI/Data Engineer

Indra

Junior Systems Engineer

Global Business Services Intern

MedaBots SAC

IT Project Intern

Data Analyst | Data Engineer | AI | GenAI | Mentor | Content Creator 👩🏻‍💻 | Google WTM Ambassador | Python | Tableau | Snowflake | Power BI | Business Intelligence

Bachelor in Systems Engineering and Information Technology from ESAN University. I hold a Specialization Program in Business Intelligence Analytics and Big Data from the Universidad Agraria La Molina. I have worked as a Data Engineer and Data Analyst in data-related projects in tech companies such as IBM, Indra, and Rappi. Currently, I work as a Data Analyst at Factored, a company founded by Andrew Ng and specialized in data science, data engineering, machine learning, and data analysis.

I have been recognized as IBM Champion 2022 and Google Women Techmakers Ambassador, I have also been a Mentor and Regional Ambassador at the Hackmakers World Innovation Day Hack 2022 International Hackathon organized by Hackmakers, where I mentored the winning team.

I have published two research papers, one of them in Springer, and I have been a speaker at the WorldCIST'20 International Conference. In addition, I have participated as a speaker in webinars held by IBM Champions and data and tech communities. 

👩🏻‍💻 I enjoy creating content about data-related topics and insights and sharing them on Linkedin and Twitter.

Remember 👇


👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#data #programming #coding #tech

Gina Acosta Gutiérrez

Daily Posts and Resources on Data Science, Data Engineering, and AI 📚 | Mentor | Google WTM Ambassador

Cool visualization of 14 sorting algorithms in just 60 seconds

Here's how some of the sorting algorithms in the video work: 👇

• 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗦𝗼𝗿𝘁
- Finds smallest item in unsorted part
- Moves it to sorted section
- Repeats for remaining items

• 𝗜𝗻𝘀𝗲𝗿𝘁𝗶𝗼𝗻 𝗦𝗼𝗿𝘁
- Takes one item at a time
- Places it in right spot in sorted list
- Works like sorting playing cards

• 𝗤𝘂𝗶𝗰𝗸 𝗦𝗼𝗿𝘁
- Picks a pivot element as reference
- Splits data into smaller/larger than pivot
- Keeps splitting until all items are sorted

• 𝗠𝗲𝗿𝗴𝗲 𝗦𝗼𝗿𝘁
- Breaks data into smaller chunks
- Sorts each small part separately
- Combines sorted pieces back together

• 𝗛𝗲𝗮𝗽 𝗦𝗼𝗿𝘁
- Creates a binary heap structure
- Swaps root with last item
- Rebuilds heap after each swap

• 𝗕𝘂𝗯𝗯𝗹𝗲 𝗦𝗼𝗿𝘁
- Compares pairs of numbers side by side
- Swaps them if they're in wrong order
- Repeats until list is sorted

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #data #ai #programming #coding

Explain to me how an API works like I'm 5 👇


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#softwareengineering #data #ai #programming #coding

Legacy software companies adding an AI chatbot to their product


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#data #programming #coding #tech

At just 19, Sam Altman left Stanford and raised an incredible $30 million to start Loopt 🤯

Started in 2005 by Sam Altman, Alok Deshpande, and Nick Sivo, Loopt was a leader in the location-based social networking world. It let users share their location, find nearby spots, and connect with friends. 

Even with strong competition from big names like Facebook, Loopt made a lasting mark. In 2012, Green Dot Corporation bought Loopt for $43.4 million, but its impact didn't end there. Loopt set the stage for future location-based apps and was a key step in Sam Altman's amazing path. 

In 2014, Altman became the president of Y Combinator, shaping many startups' futures. Later, he became the CEO of OpenAI, a leader in the AI field, pushing the limits of what's possible with artificial intelligence. 

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#data #ai #technology #innovation

22 FREE Data and AI courses 👇

365 Data Science is offering free courses for a limited time. Here’s the list of Python, SQL and Data courses:

1. SQL: https://lnkd.in/e9jGTszC
2. Advanced SQL: https://lnkd.in/e4qym_AK 
3. SQL + Tableau: https://lnkd.in/eu78SjmH
4. Introduction to Jupyter: https://lnkd.in/ee2Cm5jT
5. Introduction to Python: https://lnkd.in/enj73xMW
6. Python Programmer Bootcamp: https://lnkd.in/e_Fi6GfY
7. Intermediate Python: https://lnkd.in/eW3S3UnF
8. Git and GitHub: https://lnkd.in/eceu4hWn
9. Introduction to R: https://lnkd.in/ebKRS6BT
10. Data Preprocessing with NumPy: https://lnkd.in/eu3-tSCK
11. Data Cleaning & Preprocessing: https://lnkd.in/eQ39wTn6
12. Dates and Times in Python: https://lnkd.in/euHbWuKd
13. Working with Text Files in Python: https://lnkd.in/eptN4Piz
14. Power Query and Data Modeling: https://lnkd.in/eQPD-u9C
15. Introduction to DAX: https://lnkd.in/ej8JHNxE
16. Intro to Vector Databases with Pinecone: https://lnkd.in/eEmvc2pa
17. SQL + Tableau + Python: https://lnkd.in/eWabiXK5
18. Advanced SQL for Data Engineering: https://lnkd.in/enQja8gb
19. Python for Social Media Analytics: https://lnkd.in/eR3iYpAC
20. A/B Testing in Python: https://lnkd.in/edgEJCNk
21. SQL for Data Science Interviews: https://lnkd.in/eGFBZeSX
22. Web Scraping & API Fundamentals: https://lnkd.in/eiz26cmr

👉 Find all courses here, plus real-world data projects and career-focused training: https://bit.ly/3YEvxvZ
*Don't miss out! These courses are free until November 21 ⏳

#data #ai #datascience #programming

"In a gold rush, sell shovels" 

In the AI boom, Nvidia dominates the AI chip market, holding a market share of 70% to 95%. In 2024, they reported $30 billion in revenue during fiscal Q2, which was a 122% increase from the prior-year period. 

🤝 46% of Nvidia's $30 billion in Q2 revenue came from 4 Amazon, Microsoft, Google, and Tesla.

📈 The AI chip market is projected to reach $400 billion annually within five years, driven by the increasing demand for powerful GPUs essential for training complex models like ChatGPT.

🎯 Nvidia’s strategic partnerships and innovation keep it ahead of competitors like AMD and Intel, who are still striving to catch up.

If AI is a gold rush, Nvidia is selling shovels

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #data #ai #programming #coding

All the free Kaggle Data Science courses + Certificates 👇

Learn about Feature Engineering, Data Cleaning, Time Series, and more ...

1. Python - https://lnkd.in/eMZAhANT

2. Intro to Machine Learning - https://lnkd.in/ezYxipjp

3. Pandas - https://lnkd.in/eC6VPUzM

4. Intermediate Machine Learning - https://lnkd.in/exY_DUHN

5. Data Visualization - https://lnkd.in/eFtZt7QZ

6. Feature Engineering - https://lnkd.in/esnSzHhk

7. Intro to SQL - https://lnkd.in/ebTWM7FS

8. Advanced SQL - https://lnkd.in/eb8qyFmB

9. Intro to Deep Learning - https://lnkd.in/eXfREFR2

10. Computer Vision - https://lnkd.in/eHaVz7AK

11. Time Series - https://lnkd.in/etzxU5bV

12. Data Cleaning - https://lnkd.in/e-3TMtUw

13. Geospatial Analysis - https://lnkd.in/eHKVgU3e

14. Machine Learning Explainability - https://lnkd.in/e4Yhh3mh

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #data #ai #programming #coding

Python just dethroned JavaScript as the most used language on GitHub after its 10-year reign! 👇

Github just dropped the Octoverse 2024 and the numbers show how coding is changing in 2024.

Key takeaways:
- Python overtook JavaScript as the most used language on GitHub for the first time.
- TypeScript entered the top 3 programming languages, overtaking Java.
- 92% spike in Jupyter Notebooks usage, indicating growth in data science and ML.

And check this out, AI is completely changing the game 🤖:
- There's been a 59% increase in contributions to generative AI projects. 
- 98% increase in new AI projects overall.
- Developers using AI fixed code vulnerabilities 3x faster 📈

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #data #ai #programming #coding

Data structures are the backbone of efficient programming. Here's a simple guide to help you understand them 👇


• 𝗔𝗿𝗿𝗮𝘆: 
 - Like a row of boxes holding similar items
 - You can grab any item quickly if you know its position
 - Great for storing a fixed number of elements

• 𝗤𝘂𝗲𝘂𝗲:
 - Works like a line at a store
 - New items join at the back, items leave from the front
 - Useful for managing tasks in order

• 𝗧𝗿𝗲𝗲:
 - Branches out from a main "root" into smaller parts
 - Helps organize info in a family tree-like structure
 - Good for representing hierarchies

• 𝗠𝗮𝘁𝗿𝗶𝘅:
 - A grid of numbers or data
 - Organized in rows and columns
 - Handy for tables or image processing

• 𝗚𝗿𝗮𝗽𝗵:
 - Shows how different things connect
 - Uses dots (nodes) and lines (edges) to map relationships
 - Perfect for social networks or maps

• 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁:
 - A chain of items, each pointing to the next
 - Easy to add or remove items anywhere in the list
 - Flexible for changing data

• 𝗠𝗮𝘅 𝗛𝗲𝗮𝗽:
 - A tree where the biggest value is always on top
 - Keeps things partially sorted
 - Efficient for finding the largest item quickly

• 𝗦𝘁𝗮𝗰𝗸:
 - Last in, first out - like a stack of plates
 - Used in undo functions or for tracking program execution
 - Helps manage temporary data in a structured way

• 𝗧𝗿𝗶𝗲:
 - A tree made for storing words or strings
 - Shares common prefixes to save space
 - Fast for spell-checking or autocomplete

• 𝗛𝗮𝘀𝗵𝗠𝗮𝗽:
 - Stores data using unique keys for quick retrieval
 - Like a super-fast dictionary lookup
 - Used in caches or for storing user data in apps

• 𝗛𝗮𝘀𝗵𝗦𝗲𝘁:
 - Keeps a collection of unique items
 - Checks if something is in the set very quickly
 - Useful for removing duplicates or tracking membership

Data structures help organize and store data in a way that makes it easy to access and modify. Understanding these structures can make your coding more efficient and effective.

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck


#python #data #ai #programming #coding

There's no such thing as overnight success - just ask SpaceX.

Their journey to reusable rockets was filled with explosions and failures, but...

But they never quit.
Each failure taught them something new.
Today, their landings revolutionized space travel.

Learn from every mistake. Stay consistent. The results will come.

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#data #ai #softwareengineering #programming

Pandas is a key library in Python for data science. It helps you handle and analyze data with ease. Let's break down the main methods you need to know 👇

1. First, let's talk about data structures. Pandas uses Series and DataFrame. A Series is like a list, but with more features. A DataFrame is like a table with rows and columns.

2. Next, 𝗱𝗮𝘁𝗮 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻 is important. You can read data from different sources like CSV, Excel, or SQL. Methods like 𝚛𝚎𝚊𝚍_𝚌𝚜𝚟() and 𝚛𝚎𝚊𝚍_𝚎𝚡𝚌𝚎𝚕() help you bring data into your project.

3. 𝗗𝗮𝘁𝗮 𝗶𝗻𝘀𝗽𝗲𝗰𝘁𝗶𝗼𝗻 is about understanding your data. Methods like 𝚑𝚎𝚊𝚍() and 𝚒𝚗𝚏𝚘() let you see what your data looks like. You can check the first few rows or get a summary of your data.

4. 𝗗𝗮𝘁𝗮 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 is where you change your data. You can use 𝚊𝚜𝚜𝚒𝚐𝚗() to add new columns or 𝚍𝚛𝚘𝚙() to remove them. Sorting and renaming are also part of this.

4. 𝗧𝗶𝗺𝗲 𝘀𝗲𝗿𝗶𝗲𝘀 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 help you work with dates and times. You can convert strings to dates with 𝚝𝚘_𝚍𝚊𝚝𝚎𝚝𝚒𝚖𝚎() or create date ranges with 𝚍𝚊𝚝𝚎_𝚛𝚊𝚗𝚐𝚎().

5. For 𝗴𝗿𝗼𝘂𝗽𝗶𝗻𝗴 and 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻, methods like 𝚐𝚛𝚘𝚞𝚙𝚋𝚢() and 𝚊𝚐𝚐() are useful. They let you group data and perform calculations on each group.

5. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 is about making charts. You can use 𝚙𝚕𝚘𝚝() to create simple graphs or 𝚑𝚒𝚜𝚝() for histograms.

6. 𝗜𝗻𝗽𝘂𝘁 𝗮𝗻𝗱 𝗼𝘂𝘁𝗽𝘂𝘁 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 help you save your work. You can write data to CSV or Excel with 𝚝𝚘_𝚌𝚜𝚟() and 𝚝𝚘_𝚎𝚡𝚌𝚎𝚕().

7. 𝗗𝗮𝘁𝗮 𝗰𝗹𝗲𝗮𝗻𝗶𝗻𝗴 is key. You can fill missing values with 𝚏𝚒𝚕𝚕𝚗𝚊() or remove duplicates with 𝚍𝚛𝚘𝚙_𝚍𝚞𝚙𝚕𝚒𝚌𝚊𝚝𝚎𝚜().

8. 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 give you insights into your data. Methods like 𝚖𝚎𝚊𝚗() and 𝚖𝚎𝚍𝚒𝚊𝚗() help you understand the numbers.

9. Finally, 𝗱𝗮𝘁𝗮 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 lets you pick specific parts of your data. You can use 𝚕𝚘𝚌[] and 𝚒𝚕𝚘𝚌[] to select rows and columns.

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #data #ai #datascience

This free, open-source tool lets you create neural network architecture diagrams parametrically, fast and seamlessly 👇 


NN-SVG is an open-source tool that helps you create clean, publication-ready neural network architecture diagrams. 

It allows you to create three types of diagrams:

✔ Fully Connected Networks (FCNN)
✔ Convolutional Neural Network (CNN)
✔ Deep Neural Networks (following AlexNet)

You can adjust colors and sizes, and export your work as high-quality SVG files, suitable for inclusion in academic papers or web pages.

Perfect for students, teachers, and AI professionals who need quick, good-looking network diagrams.

Link to the repo: https://lnkd.in/e8X6kFCX

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👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #data #ai #programming #coding

Understanding Data Analytics: A Simple Guide 👇


I've created this infographic that breaks down the main types of data analytics that drive modern business decisions. Let's explore each type and how they can help companies:

𝟭. 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 📝 looks at past data to understand what happened. It uses techniques like data aggregation, data mining, and data visualization. Examples include sales reports and customer demographics.

𝟮. 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 🔍 digs into data to understand why something happened. It uses techniques like drill-down, data discovery, and correlation analysis. Examples include root cause analysis and customer churn analysis.

𝟯. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 📈 forecasts future outcomes. It uses techniques like regression analysis, time series analysis, and machine learning. Examples include sales forecasting and predicting equipment failure.

𝟰. 𝗣𝗿𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 📊 suggests actions to achieve desired outcomes. It uses techniques like optimization, simulation, and decision analysis. Examples include supply chain and marketing campaign optimization.

𝟱. 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 🤖 mimics human thought processes to automate and improve decision-making. It uses techniques like natural language processing, image recognition, and social media sentiment analysis. Examples include chatbots and virtual assistants.

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #data #ai #analytics

Roadmap to Master SQL + FREE Resources 👇

I've compiled this handy roadmap to guide you on your journey from beginner to advanced SQL user.

📌 Intro to SQL - Kaggle
https://lnkd.in/ebTWM7FS

📌 Learn SQL - CodeAcademy
https://lnkd.in/eaABTh5C

📌 Intermediate SQL Tutorials - Alex The Analyst
https://lnkd.in/enKMd7Mz

📌 SQL for Data Analysis - Udacity
https://lnkd.in/eSifcBY4

📌 SQL For Data Analysis: Music Store Data Analysis Project using SQL
https://lnkd.in/ea26PRJ5

📌 Cracking The SQL Interview - Sajjad Salaria (xoraus - Github)
https://lnkd.in/eWAEz7fz

📌 SQL Interview Questions - DataLemur
https://datalemur.com/

📌 Solving Complex SQL Query from HackerRank - TechTFQ
https://lnkd.in/esKnC-jq

📌 Practice Writing SQL Queries using Real Dataset
https://lnkd.in/eD7UASAz

👉 Get more free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#sql #data #ai #datascience #analytics

Cool Visualization of How the K-Means Algorithm Works 👇

K-means is an unsupervised learning algorithm used for data clustering. It partitions n data points into k clusters, with each data point assigned to the cluster whose mean is closest.

Here's how it works:
1. Pick K number of cluster centers randomly 
2. Assign data points to nearest center 
3. Update cluster centers to the average of assigned points 
4. Repeat steps 2-3 until convergence

Video: Jacob Bumgarner

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#python #data #datascience #programming

When the project is not ready but the client wants to see Demo ✈️😂


👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#data #programming #python #coding #tech

Sr Developer: Just read the documentation

The documentation: ...


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#python #data #ai #programming #coding

Machine Learning and Deep Learning: Stanford's CS229 Course Notes 👇

The "CS229 Lecture Notes" by Andrew Ng and Tengyu Ma is an essential resource for anyone interested in machine learning, covering everything from the basics of supervised learning to the complexities of reinforcement learning.

This guide covers the following topics:

𝟭. 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: This section explains ways to teach computers using labeled data. It includes topics like linear regression, logistic regression, and support vector machines.

𝟮. 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Here, the authors explain how neural networks work and how we can use them to solve complex problems.

𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗥𝗲𝗴𝘂𝗹𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻: This section is all about making sure our machine learning models work well on new data, not just the data we used to train them.

𝟰. 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Covers methods for finding patterns in data without labels, like clustering and dimensionality reduction.

𝟱. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: The last part explores how we can teach computers to make decisions and control systems.


👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #data #datascience #machinelearning

CS229 Lecture Notes by Andrew Ng

The truth about senior developers:

They're not the ones who know everything, but ...

They're the ones who:
- Know what questions to ask
- Understand business impact
- Consider long-term consequences
- Share knowledge freely
- Admit when they don't know

Experience is about wisdom, not just knowledge.


👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#data #ai #softwareengineering #programming

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