DaTee Analytics

Data and Analytics Scientist with speciality in organizing and optimizing information for businesses.

[Shraddha Bharuka @BharukaShraddha]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/kyDon6qLrb

2. LLMs from Scratch: https://t.co/2hyMhuKoiI

3. Agentic AI Overview (Stanford): https://t.co/FXu6cAqITC

4. Building and Evaluating Agents: https://t.co/ZigR1tdOFL

5. Building Effective Agents: https://t.co/uYwfwO55mO

6. Building Agents with MCP: https://t.co/4arFTW1b3i

7. Building an Agent from Scratch: https://t.co/eOmveyM9Hz

8. Philo Agents: https://t.co/zLu7x1tx9m

🗂️ Repos

1. GenAI Agents: https://t.co/eXCl2YaRPv

2. Microsoft's AI Agents for Beginners: https://t.co/3CSW4zPAwf

3. Prompt Engineering Guide: https://t.co/GVzvxPYDVO

4. Hands-On Large Language Models: https://t.co/0rgDvhx3pI

5. AI Agents for Beginners: https://t.co/3CSW4zPAwf

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/9z5KHF9DMe

8. Hands-On AI Engineering:https://t.co/dldAj5Xkr6

9. Awesome Generative AI Guide: https://t.co/U2WZhT4ERV

10. Designing Machine Learning Systems: https://t.co/sYAZX34YdQ

11. Machine Learning for Beginners from Microsoft: https://t.co/NjFxHbC9jZ

12. LLM Course: https://t.co/N34YTPu1OK

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/bW3Ov3vMW0

2. Google's Agent Companion: https://t.co/wredwWAbBA

3. Building Effective Agents by Anthropic: https://t.co/fxtE4alVrJ.

4. Claude Code Best Agentic Coding practices: https://t.co/lLSwJ9pG7C

5. OpenAI's Practical Guide to Building Agents: https://t.co/xgkEIogGfh

📚Books:

1. Understanding Deep Learning: https://t.co/CjcKpTemmV

2. Building an LLM from Scratch: https://t.co/DaWBxOx8o3

3. The LLM Engineering Handbook: https://t.co/ZA1n0N41Mf

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/boLkl1VlKb

5. Building Applications with AI Agents - Michael Albada: https://t.co/H1Xf5EkJLL

6. AI Agents with MCP - Kyle Stratis: https://t.co/JI3ELQZE6a

7. AI Engineering: https://t.co/Xk0JzMIf7o

📜 Papers

1. ReAct: https://t.co/QNqE4UU55w

2. Generative Agents: https://t.co/CwEpoJgY1U.

3. Toolformer: https://t.co/5m9xZd5teZ

4. Chain-of-Thought Prompting: https://t.co/KjVlgdWi77.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/7FSUYKxIdG

2. MCP with Anthropic: https://t.co/IkZGiWm2yS

3. Building Vector Databases with Pinecone: https://t.co/2YRoMfLdXd

4. Vector Databases from Embeddings to Apps: https://t.co/23A50ixbHJ

5. Agent Memory: https://t.co/uc3L9BrNF7

Repost for your network ♻️

[DaTee Analytics @TeeGanzi]

It's not just theory—building ETL pipelines, data lakes, and streaming workflows with these will set you apart. Spot-on list for anyone leveling up! #DataEngineering

[DaTee Analytics @TeeGanzi]

This take nails it: In data engineering, hands-on mastery of tools like Python, SQL, PySpark, Apache Spark, Airflow, Kafka, AWS Glue, Snowflake, and cloud platforms (AWS, GCP, Azure) is non-negotiable for tackling real-world projects.

[Darshil | Data Engineer👨🏻‍🔧 @parmardarshil07]

20 Real-World Data Engineering Project (Master these tech) 🎯

🔹 Programming & Querying
- Python
- SQL
- PySpark

🔹 Big Data & Processing
- Apache Spark
- Spark Streaming
- Databricks

🔹 Orchestration & Workflow
- Apache Airflow
- Cloud Composer

🔹 Streaming & Messaging
- Apache Kafka
- AWS Kinesis
- CDC (Change Data Capture)

🔹 Data Integration & ETL
- AWS Glue
- Azure Data Factory
- dbt
- AWS Lambda

🔹 Cloud Platforms
- AWS (S3, EC2, IAM, Lambda, Glue, Redshift)
- Google Cloud Platform (GCP)
- Microsoft Azure

🔹 Data Warehousing & Analytics
- Snowflake
- BigQuery
- Amazon Redshift
- Synapse Analytics

🔹 Storage & Data Lakes
- Amazon S3
- Azure Data Lake
- Google Cloud Storage
- Delta Lake

🔹 Databases & Sources
- MySQL
- PostgreSQL
- APIs
- Yahoo Finance API
Alpha Vantage API

🔹 Visualization & BI
- Power BI
- Looker / Looker Studio

🔹 DevOps & Environment
- Docker
- Git / GitHub
- Linux

🔹 Monitoring & Data Quality
- Logging & Alerting
- Data Validation
- Pipeline Monitoring

🔹 Architecture & Modeling
- Data Modeling
- Star Schema
- SCD (Slowly Changing Dimensions)
- Fact & Dimension Tables

[Hasan Toor @hasantoxr]

Best YouTube Channels To Learn in 2026

1. Cybersecurity - NetworkChuck
2. Artificial Intelligence - Two Minute Papers
3. Web Development - The Net Ninja
4. Python Programming - Corey Schafer
5. DevOps - TechWorld with Nana
6. Cloud Computing - AWS Developers
7. Data Analytics - Luke Barousse
8. Digital Marketing - Brian Dean
9. UI / UX Design - Jesse Showalter
10. Blockchain Development - Dapp University
11. React Development - JavaScript Mastery
12. Java Programming - Amigoscode
13. Networking - Jeremy's IT Lab
14. Personal Branding - Dan Koe

[DaTee Analytics @TeeGanzi]

@darshal_ Send

[Swapna Kumar Panda @swapnakpanda]

11 Completely FREE Books on AI & ML from MIT:

1. Foundations of Machine Learning
https://t.co/78p57EBbL8

2. Introduction to Machine Learning Systems
https://t.co/hkaYi0dd1k

3. Algorithms for ML
https://t.co/lntuD4Q19H

4. Understanding Deep Learning
https://t.co/D2oyRrXqcE

5. Deep Learning
https://t.co/vCHVIZQYTI

6. Reinforcement Learning
https://t.co/JNWhFCuCkH

7. Distributional Reinforcement Learning
https://t.co/GXpkV4BDZi

8. Multi Agent Reinforcement Learning
https://t.co/T8zVmQVutO

9. Agents in the Long Game of AI
https://t.co/HeD3Nsm5zz

10. Fairness and Machine Learning
https://t.co/csAjhdf7Lb

11. Probabilistic Machine Learning

❯ Part 1 : https://t.co/5Leef9ypGj
❯ Part 2 : https://t.co/vRbF0rEIuh

[Jackson @Jackson_00j]

I canceled my ChatGPT subscription.

This AI tool does more — faster and smarter.

You’ve got to see it for yourself 👇

[DaTee Analytics @TeeGanzi]

Exciting insights from Matt Dancho on the rise of the 'AI Data Scientist' in 2025! With tools like LangChain, LangGraph, and OpenAI, you can automate business processes and boost your career—all for virtually free. #AI #DataScience #LangChain

[Matt Dancho (Business Science) @mdancho84]

2024: Death of the Data Scientist
2025: Birth of the AI Data Scientist

Start learning AI. It's virtually free.

Just learn these:

[DaTee Analytics @TeeGanzi]

@ajitcodes Free

[Sasi 📊📈 @freest_man]

📊 SQL for Data Science Complete Study Plan 📊

Timeline of 28 days and you have to dedicate at least 1.5 hours a day.

/🧵/

[DaTee Analytics @TeeGanzi]

The overlapping skills—like programming, math, and statistics—highlight how these roles intersect to map journeys, develop hypotheses, and monetize data effectively. A must-see for anyone looking to understand the dynamic interplay in data science teams!

[DaTee Analytics @TeeGanzi]

Discover how Data Engineers, Scientists, & Business Stakeholders collaborate for data-driven success! Engineers build robust systems (APIs, Hadoop), Scientists drive insights with ML & storytelling, and Stakeholders connect it all with critical thinking. #DataScience

[Dvz | Python Architect @PythonDvz]

Ever wondered how Data Engineers, Data Scientists, and Business Stakeholders work together? 🤝

Here's a breakdown of their unique core skills and where they overlap to create data-driven success:
🔧 Data Engineer:
🔹Data Ingest, APIs, Warehousing
🔹Hadoop Ecosystem
🔹Database Systems

📈 Data Scientist:
🔹Machine & Deep Learning
🔹Predictive Modeling
🔹Python, R, and Storytelling

💡 Business Stakeholders:
🔹Critical Thinking
🔹Business Intelligence
🔹Communicating Findings

#python #datascience #machinelearning #deeplearning #ai #artificialintelligence #programming #developer #morioh #softwaredeveloper #computerscience #calculus #linearalgebra #algebra #maths #mathematics

[DaTee Analytics @TeeGanzi]

At the center, Business Stakeholders bridge the gap, relying on critical thinking, business intelligence, and clear communication to drive value through data-driven decisions.

[DaTee Analytics @TeeGanzi]

Data Scientists dive deep into analysis, leveraging Python, R, and advanced machine learning for predictive modeling and storytelling through data visualization.

[DaTee Analytics @TeeGanzi]

The world of data science thrives on collaboration, and this guide beautifully illustrates the synergy between key roles. Data Engineers focus on the backbone—building robust data ingestion systems, databases, and data warehousing with tools like Hadoop.

[DaTee Analytics @TeeGanzi]

Practice Platforms: Beyond Kaggle, DataCamp’s interactive exercises hit the spot for coding practice, and LeetCode’s data science track sharpens your algorithmic chops—handy for interviews.

[DaTee Analytics @TeeGanzi]

For data scientists looking to sharpen their skills or dive into new tools, here’s a rundown of some standout resources based on what’s been buzzing lately:

[DaTee Analytics @TeeGanzi]

Communities & Blogs: Kaggle’s a goldmine—datasets, competitions, and kernels to learn from real-world pros. Towards Data Science on Medium has solid articles, though you’ll sift through some noise. X posts from folks like @rasbt or
@jeremyphoward often drop gems on new tech.

[DaTee Analytics @TeeGanzi]

Tools & Libraries: Python’s ecosystem dominates—pandas, NumPy, and SciPy for data crunching; Scikit-learn for classic ML; TensorFlow or PyTorch for deep learning. Jupyter Notebooks are clutch for experimenting and sharing.

[DaTee Analytics @TeeGanzi]

Online Courses: Coursera’s "Data Science Specialization" from Johns Hopkins is still a solid pick—covers R, stats, and the full pipeline from scratch. For something more cutting-edge, try https://t.co/2EZqBNoHrS’s "Practical Deep Learning for Coders"—it’s free.