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Rappi

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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.

𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗕𝗶𝗼𝗻𝗶𝗰: 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗟𝗶𝗺𝗯𝘀 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗟𝗶𝘃𝗲𝘀 🦾

Artificial intelligence is breathing new life into the world of prosthetics, creating limbs that are as close to humans as ever before. 

It's the dawn of a revolution in prosthetics, and a shining example of how AI can be harnessed for good. The future is bionic and I can't wait to see these intelligent prosthetics change more lives!

👉 Follow Gina Acosta Gutiérrez for more!

➡ Get the latest AI news, tools, tutorials and guides on using popular AI tools for FREE here: https://lnkd.in/ewfrvcy5

#ai #tech #artificialintelligence #learning #ai #health #innovation #technology 

Gina Acosta Gutiérrez

𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝘀𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 𝗙𝗿𝗼𝗺 𝗮 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗔𝗻𝗴𝗹𝗲

Let's dive in with some real-world analogies and examples!

🔹 𝗮𝗽𝗽𝗲𝗻𝗱() - 𝗧𝗵𝗲 𝗣𝗮𝗿𝘁𝘆 𝗣𝗹𝗮𝗻𝗻𝗲𝗿

Imagine you're organizing a party. As you think of more friends to invite, you keep adding them to your guest list. In Python, this is just like using 𝚊𝚙𝚙𝚎𝚗𝚍(). It adds an element to the end of the list.

guest_list = ['Alice', 'Bob']
guest_list.append('Charlie')
print(guest_list) # Output: ['Alice', 'Bob', 'Charlie']

🔹 𝗲𝘅𝘁𝗲𝗻𝗱() - 𝗧𝗵𝗲 𝗠𝗲𝗿𝗴𝗶𝗻𝗴 𝗼𝗳 𝗧𝗿𝗶𝗯𝗲𝘀

Think of two music bands deciding to perform together. They merge their members into one big band. Similarly, 𝚎𝚡𝚝𝚎𝚗𝚍() merges one list into another.

band_one = ['Drummer', 'Guitarist']
band_two = ['Singer', 'Bassist']
band_one.extend(band_two)
print(band_one) # Output: ['Drummer', 'Guitarist', 'Singer', 'Bassist']


🔹 𝗿𝗲𝗺𝗼𝘃𝗲() - 𝗦𝗽𝗿𝗶𝗻𝗴 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴

Ever cleaned your room and removed items you no longer need? 𝚛𝚎𝚖𝚘𝚟𝚎() does this for lists, eliminating the first occurrence of a specified element.

items = ['books', 'pen', 'notebook', 'pen']
items.remove('pen')
print(items) # Output: ['books', 'notebook', 'pen']


🔹 𝗽𝗼𝗽() - 𝗧𝗵𝗲 𝗦𝘂𝗿𝗽𝗿𝗶𝘀𝗲 𝗕𝗼𝘅

Imagine a box where you take out the topmost item without knowing what it is. 𝚙𝚘𝚙() removes and returns the last element of the list (or a specified index).


colors = ['red', 'green', 'blue']
print(colors.pop()) # Output: 'blue'
print(colors) # Output: ['red', 'green']


🔹 𝘀𝗼𝗿𝘁() - 𝗧𝗵𝗲 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗮𝗻’𝘀 𝗠𝗲𝘁𝗵𝗼𝗱

Think of a librarian arranging books in alphabetical order. 𝚜𝚘𝚛𝚝() arranges elements of the list in a specific order (default is ascending).


numbers = [3, 1, 4, 1, 5, 9]
numbers.sort()
print(numbers) # Output: [1, 1, 3, 4, 5, 9]


🔹 𝗿𝗲𝘃𝗲𝗿𝘀𝗲() - 𝗧𝗶𝗺𝗲 𝗧𝗿𝗮𝘃𝗲𝗹𝗲𝗿’𝘀 𝗚𝗹𝗶𝗺𝗽𝘀𝗲

Ever wished to rewind your day and revisit events in reverse? That's what 𝚛𝚎𝚟𝚎𝚛𝚜𝚎() does to a list.

timeline = ['morning', 'afternoon', 'evening']
timeline.reverse()
print(timeline) # Output: ['evening', 'afternoon', 'morning']

🔹 𝗶𝗻𝗱𝗲𝘅() - 𝗧𝗵𝗲 𝗧𝗿𝗲𝗮𝘀𝘂𝗿𝗲 𝗛𝘂𝗻𝘁

Finding a specific item in a cluttered room can be a challenge. Similarly, 𝚒𝚗𝚍𝚎𝚡() helps find the position of an element in the list.

fruits = ['apple', 'banana', 'cherry']
print(fruits.index('banana')) # Output: 1

🔹 𝗰𝗼𝘂𝗻𝘁() - 𝗧𝗵𝗲 𝗣𝗼𝗹𝗹 𝗧𝗮𝗸𝗲𝗿

Ever conducted a poll and counted votes for each option? 𝚌𝚘𝚞𝚗𝚝() does this for occurrences of an element in a list.

votes = ['yes', 'no', 'yes', 'yes', 'no']
print(votes.count('yes')) # Output: 3


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#python #data #machinelearning #ai #artificialintelligence #datascience #analytics #software #webdevelopment #developer #automation #technology #programming #coding #tech #learning

𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴: 𝗔 𝗩𝗶𝘀𝘂𝗮𝗹 𝗚𝘂𝗶𝗱𝗲 𝘁𝗼 𝗖𝗵𝗮𝗿𝘁 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗙𝗥𝗘𝗘 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 📊🤩

Data storytelling is the process of using data to communicate a story. It's a way to make data more understandable and engaging for your audience.

👉 Choosing the right chart is crucial for effective data storytelling. The type of chart you select should depend on the type of data you have and the narrative you aim to convey.

I've compiled a comprehensive list of my favorite free resources to help you learn and improve your data storytelling skills from top-notch experts and Google:

𝟭. 𝗛𝗼𝘄 𝘁𝗼 𝘁𝘂𝗿𝗻 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 - 𝗕𝗲𝘀𝘁-𝘀𝗲𝗹𝗹𝗶𝗻𝗴 𝗮𝘂𝘁𝗵𝗼𝗿 𝗖𝗼𝗹𝗲 𝗡𝘂𝘀𝘀𝗯𝗮𝘂𝗺𝗲𝗿 𝗞𝗻𝗮𝗳𝗹𝗶𝗰
https://lnkd.in/eztgahUP

𝟮. 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 - 𝗧𝗮𝗹𝗸𝘀 𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲
https://lnkd.in/eaWvehrv

𝟯. 𝗦𝘁𝗼𝗿𝘆 𝗧𝗲𝗹𝗹𝗶𝗻𝗴 𝗜𝗻 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 - 𝗣𝗶𝘅𝗮𝗿 𝗦𝘁𝗼𝗿𝘆 𝗧𝗲𝗹𝗹𝗲𝗿 𝗠𝗮𝘁𝗵𝗲𝘄 𝗟𝘂𝗵𝗻 𝗮𝘁 𝗖𝗜𝗠𝗖
https://lnkd.in/eTWwvxnf

𝟰. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 𝗨𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 - 𝗖𝗼𝗹𝘂𝗺𝗯𝗶𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻
https://lnkd.in/e3cVjssn

𝟱. 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴: 𝗛𝗼𝘄 𝘁𝗼 𝗧𝘂𝗿𝗻 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 - 𝗛𝗲𝗶𝗻𝗿𝗶𝗰𝗵 𝗥𝘂𝘀𝗰𝗵𝗲 (𝗘𝘅-𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆)
https://lnkd.in/eq_FSMbv

𝟲. 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 (𝗖𝗼𝗹𝗲 𝗡𝘂𝘀𝘀𝗯𝗮𝘂𝗺𝗲𝗿 𝗞𝗻𝗮𝗳𝗹𝗶𝗰) - 𝗧𝗮𝗹𝗸𝘀 𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲
https://lnkd.in/ecjutQ_9

𝟳. 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗧𝗮𝗯𝗹𝗲𝗮𝘂: 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻 𝗥𝗼𝗰𝗸𝘀𝘁𝗮𝗿
https://lnkd.in/e3im__aB

𝟴. 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜
https://lnkd.in/eAkGVg6V

𝟵. 𝗖𝗿𝗮𝗳𝘁𝗶𝗻𝗴 𝗦𝘁𝗼𝗿𝗶𝗲𝘀 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 - 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀
https://lnkd.in/eCGTYnAe

The most impactful chart type depends on the specific story you aim to tell. The optimal visual should be chosen based on how it can best highlight the key data insights for your audience.

_____

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#data #sql #python #datascience #businessintelligence #ai #dataanalytics #analytics #business #technology #programming #coding #tech #learning

The NBA Pacers used Snapchat AI filters to create the illusion that Los Angeles Lakers fans were crying during the game


📚 Steal these +1000 ChatGPT Prompt Templates https://lnkd.in/e3a-6C6x

#sports #ai #artificialintelligence #marketing #innovation #tech #technology #business

This week, NVIDIA introduced Project GR00T (Generalist Robot 00 Technology), an AI foundation model designed to revolutionize humanoid robot learning.

👉 Here's everything you need to know:

- Acting as the “mind” for advanced robots, GR00T enables them to quickly acquire skills like coordination and dexterity from just a few human demonstrations.

- The model takes multimodal instructions and past interactions as input and produces the actions for the robot to execute.

- Nvidia claims it is already working with some of the biggest names in robotics including Boston Dynamics, 1X Technologies, Agility Robotics, Apptronik, and Figure AI.

Nvidia aims to be a key player in shaping the future of robotics as competition heats up between different types of robots.

Follow Gina Acosta Gutiérrez 👩🏻‍💻 for more!

➡ Get the latest AI news, tools, tutorials and guides on using popular AI tools for FREE here: https://lnkd.in/ewfrvcy5

#robotics #data #ai #artificialintelligence #productivity #innovation #coding #tech #technology

FREE Generative AI Courses by Google + Completion Badges 🤖🧠

Generative AI is shaping our future, and it's moving fast! Google's making it easy for everyone with these 9 free courses:

Introduction to Generative AI - https://lnkd.in/eNu-_kgd

Introduction to Large Language Models - https://lnkd.in/esgJai37

Introduction to Responsible AI - https://lnkd.in/eMbycrw8

Introduction to Image Generation - https://lnkd.in/eYmTamdk

Encoder-Decoder Architecture - https://lnkd.in/eqQugaDQ

Transformer Models and BERT Model - https://lnkd.in/eZQS9e5G

Create Image Captioning Models - https://lnkd.in/ebq9rJQM

Introduction to Generative AI Studio - https://lnkd.in/eqJDSRPw

Responsible AI: Applying AI Principles with Google Cloud - https://lnkd.in/e6HgvynH

👉 Follow Gina Acosta Gutiérrez for more resources!

➡ Get the latest AI news, tools, tutorials and guides on using popular AI tools for FREE here: https://lnkd.in/ewfrvcy5

#google #python #data #machinelearning #ai #artificialintelligence #datascience #software #developer #technology #programming #coding #tech #learning

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗞𝗮𝗳𝗸𝗮?

Apache Kafka is a distributed, fault-tolerant, and highly scalable pub-sub messaging system. It enables applications to both publish and subscribe to real-time data feeds, boasting high throughput for both producers and consumers of record streams, and facilitating the real-time processing of these data streams.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗞𝗮𝗳𝗸𝗮 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴?

Kafka Streams is a powerful client library for stream processing, deeply integrated with Apache Kafka. It allows for the transformation and analysis of data stored in Kafka topics.

𝗞𝗮𝗳𝗸𝗮 𝘃𝘀. 𝗭𝗲𝗿𝗼-𝗖𝗼𝗽𝘆 𝗞𝗮𝗳𝗸𝗮: 𝗗𝗶𝘀𝘁𝗶𝗻𝗰𝘁 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀

Traditional Kafka is renowned for its rapid data processing capabilities. However, 'zero-copy' Kafka enhances this further by utilizing an optimized data transfer mechanism. It circumvents the traditional application layer, transferring data directly from the file system cache to the network socket buffer. This minimizes CPU overhead and reduces data copying (hence 'zero-copy'), resulting in significantly improved throughput and reduced latency. Essentially, Zero-Copy Kafka provides an accelerated version of Kafka, tailored for even more efficient data handling.

𝗧𝗼𝗽 𝟱 𝗞𝗮𝗳𝗸𝗮 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀:

1. 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴: Kafka acts as a real-time conduit for high-volume data streams, analogous to channeling a dynamic river of information. It empowers the creation of applications that are capable of immediate data transformation and reaction as the information flows between various systems.

2. 𝗟𝗼𝗴 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻: Kafka addresses the challenge of managing colossal volumes of log data generated every second. It provides a unified, high-throughput, and low-latency platform for handling real-time data feeds, thereby becoming an optimal solution for efficient log aggregation in distributed system architectures.

3. 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝗤𝘂𝗲𝘂𝗲: In modern architectures, particularly those employing microservices, Kafka serves as an advanced message queuing system. It offers significant advantages over traditional message queues, with its inherent scalability, built-in fault tolerance, replication capabilities, and high durability.

4. 𝗪𝗲𝗯 𝗔𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴: Kafka is adept at meticulously tracking web activities such as page views, searches, and uploads, in a real-time and large-scale manner. It acts as a continuous monitor of user interactions on websites, providing invaluable insights for user experience enhancements.

5. 𝗗𝗮𝘁𝗮 𝗥𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Kafka plays a pivotal role in the reliable and consistent transfer of data between different systems. It acts as a robust and fault-tolerant medium, ensuring seamless data replication and synchronization across distributed systems.

➡ Stay tuned and make sure to follow us at Data and AI Central 🚀 for more FREE resources!

#data #datascience #technology #programming #coding #tech #learning

𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 𝗠𝗮𝗽 🐍

Data structures are a way of organizing data so that it can be accessed more efficiently depending on the situation. Data structures are fundamentals of any programming language around which a program is built.

Here is a Concept map to learn Data Structures in Python:

- 𝗟𝗶𝘀𝘁𝘀: Think of a digital shopping list – that's a Python list! 🛒📝 It holds various data types and allows easy addition, removal, and modification.

- 𝗗𝗶𝗰𝘁𝗶𝗼𝗻𝗮𝗿𝗶𝗲𝘀: Like a word-reference, Python dictionaries pair "keys" and "values". 📖🔑 Perfect for storing related info, akin to a contact list.

- 𝗧𝘂𝗽𝗹𝗲𝘀: Similar to lists, but unchangeable once set 🔒. Ideal for fixed values, like coordinates.

- 𝗦𝗲𝘁𝘀: Imagine a drawer with unique items – that's a set! ✨🗄️ Great for finding common elements.

- 𝗦𝘁𝗮𝗰𝗸𝘀: Visualize a stack of plates – last in, first out. 🥞⌛ Used for orderly retrieval.

- 𝗤𝘂𝗲𝘂𝗲𝘀: Picture a movie ticket queue – first come, first served. 🎫👥 Manages items in sequence.

- 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁𝘀: Imagine linked chain links, each with data and a pointer. 🔗📜 Efficient for insertions and deletions.

Each structure has unique advantages, streamlining your code for efficiency. Remember, the choice depends on your goals!

👉 Follow Gina Acosta Gutiérrez 👩🏻‍💻 for more resources!

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#python #data #datascience #machinelearning #ai #artificialintelligence #programming #dataanalysis #analytics #productivity #innovation #coding #tech #softwareengineer #developer #technology

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