15 different Careers in AI

𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 vs 𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬

Selecting the right database depends on your data needs—vector databases excel in similarity searches and embeddings, while graph databases are best for managing complex relationships between entities.


𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Data Encoding: Vector databases encode data into vectors, which are numerical representations of the data.
- Partitioning and Indexing: Data is partitioned into chunks and encoded into vectors, which are then indexed for efficient retrieval.
- Ideal Use Cases: Perfect for tasks involving embedding representations, such as image recognition, natural language processing, and recommendation systems.
- Nearest Neighbor Searches: They excel in performing nearest neighbor searches, finding the most similar data points to a given query efficiently.
- Efficiency: The indexing of vectors enables fast and accurate information retrieval, making these databases suitable for high-dimensional data.

𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Relational Information Management: Graph databases are designed to handle and query relational information between entities.
- Node and Edge Representation: Entities are represented as nodes, and relationships between them as edges, allowing for intricate data modeling.
- Complex Relationships: They excel in scenarios where understanding and navigating complex relationships between data points is crucial.
- Knowledge Extraction: By indexing the resulting knowledge base, they can efficiently extract sub-knowledge bases, helping users focus on specific entities or relationships.
- Use Cases: Ideal for applications like social networks, fraud detection, and knowledge graphs where relationships and connections are the primary focus.

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:
Choosing between a vector and a graph database depends on the nature of your data and the type of queries you need to perform. Vector databases are the go-to choice for tasks requiring similarity searches and embedding representations, while graph databases are indispensable for managing and querying complex relationships.

Source: Ashish Joshi

Data Science Full Course For Beginners
⏰ 24 hours long

Created by IBM ✅

https://www.youtube.com/watch?v=WlLgysXJ0Ec

#datascience
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🔥 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝! 🔥


🚀 1. Array – Fixed-size collection of elements, perfect for fast lookups!
📦 2. Queue – First in, first out (FIFO). Think of a line at a grocery store!
🌳 3. Tree – Hierarchical structure, great for databases and file systems!
📊 4. Matrix – 2D representation, widely used in image processing and graphs!
🔗 5. Linked List – A chain of nodes, efficient for insertions & deletions!
🔗 6. Graph – Represents relationships, used in social networks & maps!
📈 7. Heap (Max/Min) – Optimized for priority-based operations!
🗂 8. Stack – Last in, first out (LIFO). Undo/Redo in action!
🔡 9. Trie – Best for search & autocomplete functionalities!
🔑 10. HashMap & HashSet – Fast lookups, perfect for key-value storage!
Understanding these will make you a better problem solver & efficient coder! 💡

𝐔𝐬𝐢𝐧𝐠 𝐁𝐢𝐠-𝐎 𝐢𝐧 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 𝐚𝐧𝐝 𝐄𝐯𝐞𝐫𝐲𝐝𝐚𝐲 𝐋𝐢𝐟𝐞.

Big-O notation is a mathematical notation that is used to describe the performance or complexity of an algorithm, specifically how long an algorithm takes to run as the input size grows.

Understanding Big-O notation is essential for software engineers, as it allows them to analyze and compare the efficiency of different algorithms and make informed decisions about which one to use in a given situation.

Here are famous Big-O notations with examples.

𝐇𝐨𝐰 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞?

Here are some of the top ways to improve database performance:

1. Indexing
Create the right indexes based on query patterns to speed up data retrieval.

2. Materialized Views
Store pre-computed query results for quick access, reducing the need to process complex queries repeatedly.

3. Vertical Scaling
Increase the capacity of the hashtag#database server by adding more CPU, RAM, or storage.

𝐓𝐨𝐩 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬

➡️ 1. API Gateway Pattern: Centralizes external access to your microservices, simplifying communication and providing a single entry point for client requests.

➡️ 2. Backends for Frontends Pattern (BFF): Creates dedicated backend services for each frontend, optimizing performance and user experience tailored to each platform.

➡️ 3. Service Discovery Pattern: Enables microservices to dynamically discover and communicate with each other, simplifying service orchestration and enhancing system scalability.

➡️ 4. Circuit Breaker Pattern: Implements a fault-tolerant mechanism for microservices, preventing cascading failures by automatically detecting and isolating faulty services.

➡️ 5. Retry Pattern: Enhances microservices' resilience by automatically retrying failed operations, increasing the chances of successful execution and minimizing transient issues.

CHOOSING THE RIGHT DATA ANALYTICS TOOLS

With so many data analytics tools available,
how do you pick the right one?

The truth is—there’s no one-size-fits-all answer.
The best tool depends on your needs, your data, and your goals.

Here’s how to decide:

🔹 For Data Exploration & Cleaning → SQL, Python (Pandas), Excel
🔹 For Dashboarding & Reporting → Tableau, Power BI, Looker
🔹 For Big Data Processing → Spark, Snowflake, Google BigQuery
🔹 For Statistical Analysis → R, Python (Statsmodels, SciPy)
🔹 For Machine Learning → Python (Scikit-learn, TensorFlow)

Ask yourself:
✅ What type of data am I working with?
✅ Do I need interactive dashboards?
✅ Is coding necessary, or do I need a no-code tool?
✅ What does my team/stakeholder prefer?

The best tool is the one that helps you solve problems efficiently.

BECOMING A DATA ANALYST IN 2025

Becoming a data analyst doesn’t have to be expensive in 2025.

With the right free resources and a structured approach,
you can become a skilled data analyst.

Here’s a roadmap with free resources to guide your journey:

1️⃣ Learn the Basics of Data Analytics
Start with foundational concepts like:
↳ What is data analytics?
↳ Types of analytics (descriptive, predictive, prescriptive).
↳ Basics of data types and statistics.

📘 Free Resources:
1. Intro to Statistics : https://www.khanacademy.org/math/statistics-probability
2. Introduction to Data Analytics by IBM (audit for free) :
https://www.coursera.org/learn/introduction-to-data-analytics


2️⃣ Master Excel for Data Analysis
Excel is an essential tool for data cleaning, analysis, and visualization.

📘 Free Resources:
1. Excel Is Fun (YouTube): https://www.youtube.com/user/ExcelIsFun
2. Chandoo.org: https://chandoo.org/

🎯 Practice: Learn how to create pivot tables and use functions like VLOOKUP, SUMIF, and IF.


3️⃣ Learn SQL for Data Queries
SQL is the language of data—used to retrieve and manipulate datasets.

📘 Free Resources:
1. W3Schools SQL Tutorial : https://www.w3schools.com/sql/
2. Mode Analytics SQL Tutorial : https://mode.com/sql-tutorial/

🎯 Practice: Write SELECT, WHERE, and JOIN queries on free datasets.


4️⃣ Get Hands-On with Data Visualization
Learn to communicate insights visually with tools like Tableau or Power BI.

📘 Free Resources:
1. Tableau Public: https://www.tableau.com/learn/training
2. Power BI Community Blog: https://community.fabric.microsoft.com/t5/Power-BI-Community-Blog/bg-p/community_blog

🎯 Practice: Create dashboards to tell stories using real datasets.

5️⃣ Dive into Python or R for Analytics
Coding isn’t mandatory, but Python or R can open up advanced analytics.

📘 Free Resources:
1. Google’s Python Course https://developers.google.com/edu/python
2. R for Data Science (free book) r4ds.had.co.nz

🎯 Practice: Use libraries like Pandas (Python) or dplyr (R) to clean and analyze data.


6️⃣ Work on Real Projects
Apply your skills to real-world datasets to build your portfolio.

📘 Free Resources:
Kaggle: Datasets and beginner-friendly competitions.
Google Dataset Search: Access datasets on any topic.

🎯 Project Ideas:
Analyze sales data and create a dashboard.
Predict customer churn using a public dataset.


7️⃣ Build Your Portfolio and Network
Showcase your projects and connect with others in the field.

📘 Tips:
→ Use GitHub to share your work.
→ Create LinkedIn posts about your learning journey.
→ Join forums like r/DataScience on Reddit or LinkedIn groups.

Final Thoughts
Becoming a data analyst isn’t about rushing—it’s about consistent learning and practice.

💡 Start small, use free resources, and keep building.
💡 Remember: Every small step adds up to big progress.

SNOWFLAKES AND DATABRICKS

Snowflake and Databricks are leading cloud data platforms, but how do you choose the right one for your needs?

🌐 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞

❄️ 𝐍𝐚𝐭𝐮𝐫𝐞: Snowflake operates as a cloud-native data warehouse-as-a-service, streamlining data storage and management without the need for complex infrastructure setup.

❄️ 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: It provides robust ELT (Extract, Load, Transform) capabilities primarily through its COPY command, enabling efficient data loading.
❄️ Snowflake offers dedicated schema and file object definitions, enhancing data organization and accessibility.

❄️ 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: One of its standout features is the ability to create multiple independent compute clusters that can operate on a single data copy. This flexibility allows for enhanced resource allocation based on varying workloads.

❄️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: While Snowflake primarily adopts an ELT approach, it seamlessly integrates with popular third-party ETL tools such as Fivetran, Talend, and supports DBT installation. This integration makes it a versatile choice for organizations looking to leverage existing tools.

🌐 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬

❄️ 𝐂𝐨𝐫𝐞: Databricks is fundamentally built around processing power, with native support for Apache Spark, making it an exceptional platform for ETL tasks. This integration allows users to perform complex data transformations efficiently.

❄️ 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: It utilizes a 'data lakehouse' architecture, which combines the features of a data lake with the ability to run SQL queries. This model is gaining traction as organizations seek to leverage both structured and unstructured data in a unified framework.

🌐 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬

❄️ 𝐃𝐢𝐬𝐭𝐢𝐧𝐜𝐭 𝐍𝐞𝐞𝐝𝐬: Both Snowflake and Databricks excel in their respective areas, addressing different data management requirements.

❄️ 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞’𝐬 𝐈𝐝𝐞𝐚𝐥 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: If you are equipped with established ETL tools like Fivetran, Talend, or Tibco, Snowflake could be the perfect choice. It efficiently manages the complexities of database infrastructure, including partitioning, scalability, and indexing.

❄️ 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞𝐬: Conversely, if your organization deals with a complex data landscape characterized by unpredictable sources and schemas, Databricks—with its schema-on-read technique—may be more advantageous.

🌐 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:

Ultimately, the decision between Snowflake and Databricks should align with your specific data needs and organizational goals. Both platforms have established their niches, and understanding their strengths will guide you in selecting the right tool for your data strategy.

AI Agents Course
by Hugging Face 🤗


This free course will take you on a journey, from beginner to expert, in understanding, using and building AI agents.

https://huggingface.co/learn/agents-course/unit0/introduction

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