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πΉ SQL Series: Day 7οΈβ£ β Keys in Action
π Hey data detectives! Today we talk about keys. Here are explanation of each key and you can also find example for each key explained on basic sql table.
π Users Table
*Holds your people-data*
π Keys in `users`
* Primary Key:
* Surrogate Key:
* Natural Keys: real-world columns you could use as IDs (
* Candidate Keys: any minimal set that uniquely identifies a row β
* Alternate Keys: candidate keys not chosen as PK β
* Unique Keys: enforced via
* Super Keys: any superset of a candidate key β e.g.,
π Hey data detectives! Today we talk about keys. Here are explanation of each key and you can also find example for each key explained on basic sql table.
π Users Table
*Holds your people-data*
+----+-------------------+--------------+
| id | email | username |
+----+-------------------+--------------+
| 1 | [email protected] | alice_wonder |
| 2 | [email protected] | bob_builder |
+----+-------------------+--------------+
π Keys in `users`
* Primary Key:
id
β unique, NOT NULL, auto-indexed for β‘οΈ fast lookups* Surrogate Key:
id
is system-generated (no business meaning)* Natural Keys: real-world columns you could use as IDs (
email
, username
)* Candidate Keys: any minimal set that uniquely identifies a row β
{id}
, {email}
, {username}
* Alternate Keys: candidate keys not chosen as PK β
email
, username
* Unique Keys: enforced via
UNIQUE
on email
& username
* Super Keys: any superset of a candidate key β e.g.,
{id,email}
, {email,username}
β€6
π 15 Free MIT Courses to Kickstart Your Data Science Career π₯
Published by MIT Open Learning, this curated list brings you the foundational building blocks of Data Scienceβmath, stats, Python, ML, and more. All 100% free!
π Course List:
1οΈβ£ Linear Algebra
Explore linear algebra and matrix theory through multidisciplinary topics.
2οΈβ£ Single Variable Calculus
Master derivatives, integrals, coordinate systems, and infinite series.
3οΈβ£ Multivariable Calculus
Learn differential, integral, and vector calculus for multivariable functions.
4οΈβ£ Introduction to Probability and Statistics
Foundations of probability, Bayesian inference, and linear regression.
5οΈβ£ Probability: The Science of Uncertainty and Data
Part of MITx MicroMasters in Statistics & DSβrandom processes, statistical inference.
6οΈβ£ Fundamentals of Statistics
Estimation, hypothesis testing, prediction. Also part of MITx MicroMasters.
7οΈβ£ Understanding the World Through Data
Use basic data forms, tools & ML algorithms to make sense of the world.
8οΈβ£ Introduction to Computer Science and Programming Using Python
Solve real-world analytical problems with Python 3.5.
9οΈβ£ Introduction to Computational Thinking and Data Science
Learn to solve problems computationally & write small, effective programs.
π Data Analysis: Statistical Modeling and Computation in Applications
Analyze real-world data using stats & computation (also MicroMasters course).
1οΈβ£1οΈβ£ Introduction to Algorithms
Model computational problems and solve them using powerful algorithms.
1οΈβ£2οΈβ£ Introduction to Machine Learning
Explore ML principles, modeling, and predictive applications.
1οΈβ£3οΈβ£ Matrix Methods in Data Analysis, Signal Processing, and ML
Linear algebra meets neural networks, probability, and optimization.
1οΈβ£4οΈβ£ Mathematics of Big Data and Machine Learning
Understand D4M (Dynamic Distributed Dimensional Data Model) using graph theory and databases.
1οΈβ£5οΈβ£ Machine Learning with Python: from Linear Models to Deep Learning
Hands-on ML with linear models, deep learning, and reinforcement learning in Python.
---
π Source: MIT Open Learning
π https://openlearning.mit.edu/news/15-free-mit-data-science-courses
πΈ 100% Free | π Self-Paced | π§ Taught by Top MIT Professors
#datascience
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
Published by MIT Open Learning, this curated list brings you the foundational building blocks of Data Scienceβmath, stats, Python, ML, and more. All 100% free!
π Course List:
1οΈβ£ Linear Algebra
Explore linear algebra and matrix theory through multidisciplinary topics.
2οΈβ£ Single Variable Calculus
Master derivatives, integrals, coordinate systems, and infinite series.
3οΈβ£ Multivariable Calculus
Learn differential, integral, and vector calculus for multivariable functions.
4οΈβ£ Introduction to Probability and Statistics
Foundations of probability, Bayesian inference, and linear regression.
5οΈβ£ Probability: The Science of Uncertainty and Data
Part of MITx MicroMasters in Statistics & DSβrandom processes, statistical inference.
6οΈβ£ Fundamentals of Statistics
Estimation, hypothesis testing, prediction. Also part of MITx MicroMasters.
7οΈβ£ Understanding the World Through Data
Use basic data forms, tools & ML algorithms to make sense of the world.
8οΈβ£ Introduction to Computer Science and Programming Using Python
Solve real-world analytical problems with Python 3.5.
9οΈβ£ Introduction to Computational Thinking and Data Science
Learn to solve problems computationally & write small, effective programs.
π Data Analysis: Statistical Modeling and Computation in Applications
Analyze real-world data using stats & computation (also MicroMasters course).
1οΈβ£1οΈβ£ Introduction to Algorithms
Model computational problems and solve them using powerful algorithms.
1οΈβ£2οΈβ£ Introduction to Machine Learning
Explore ML principles, modeling, and predictive applications.
1οΈβ£3οΈβ£ Matrix Methods in Data Analysis, Signal Processing, and ML
Linear algebra meets neural networks, probability, and optimization.
1οΈβ£4οΈβ£ Mathematics of Big Data and Machine Learning
Understand D4M (Dynamic Distributed Dimensional Data Model) using graph theory and databases.
1οΈβ£5οΈβ£ Machine Learning with Python: from Linear Models to Deep Learning
Hands-on ML with linear models, deep learning, and reinforcement learning in Python.
---
π Source: MIT Open Learning
π https://openlearning.mit.edu/news/15-free-mit-data-science-courses
πΈ 100% Free | π Self-Paced | π§ Taught by Top MIT Professors
#datascience
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
openlearning.mit.edu
15 free MIT data science courses | Open Learning
By Katherine Ouellette Jumpstart your data science journeyβββone of the worldβs fastest growing career paths! Build foundational skills and knowledge with these free online courses from MIT Open Learning. Linear Algebra Explore linear algebra and matrix theoryβ¦
β€10π1π₯1
Forwarded from Big Data Specialist
Machine Learning Free Courses
Machine Learning with Python β FreeCodeCamp
π¬ 1 video lesson (full course)
Duration β°: 4 hours
πββοΈ Self paced
Resource: freecodecamp
π Course Link
Machine Learning for Beginners β 26-Lesson ML Video Series
π Free Online Course
π¬ Video lectures
β° 10 hours
πββοΈ Self paced
Teacher π¨βπ« : Microsoft Cloud Advocates Team
Source: Microsoft Learn
π Course Link
STAT 451: Introduction to Machine Learning β Sebastian Raschka (UWβMadison)
π Free Online Course
π¬ Video lectures
β° ~20 hours
πββοΈ Self paced
Teacher π¨βπ« : Sebastian Raschka (UβWisconsinβMadison)
Source: UWβMadison STAT 451 YouTube & course materials
π Course Link
Intro to Machine Learning with Python (Kaggle)
Rating βοΈ: 4.5 out of 5
Students π¨βπ: 125,000+
Duration β°: 3hrs 30min
Created by: Kaggle (Dan Becker)
π Course Link
Googleβs Machine Learning Crash Course
β³Modules: 25+
Duration β°: 15 hours
πββοΈ Self paced
Resource: Google AI
π Course Link
Machine Learning Specialization β DeepLearning.AI (Audit Free)
Rating βοΈ: 4.8 out of 5
Students π¨βπ: 900,000+
Duration β°: ~30 hours (3 courses)
Created by: Andrew Ng (DeepLearning.AI)
π Course Link
Introduction to Machine Learning β CMU (10-301/601)
π Free Online Course
π¬ Video lectures
π Lecture notes (PDF)
β° ~30 hours
πββοΈ Self-paced
Teacher π¨βπ«: CMU Faculty (varies by year)
Source: Carnegie Mellon University
π Lecture Notes
Machine Learning Full Course β Edureka (YouTube)
π¬ 1 video lesson (full course)
Durationβ°: 10 hours
πββοΈ Self paced
Resource: YouTube
π Course Link
#machinelearning #ml
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
Machine Learning with Python β FreeCodeCamp
π¬ 1 video lesson (full course)
Duration β°: 4 hours
πββοΈ Self paced
Resource: freecodecamp
π Course Link
Machine Learning for Beginners β 26-Lesson ML Video Series
π Free Online Course
π¬ Video lectures
β° 10 hours
πββοΈ Self paced
Teacher π¨βπ« : Microsoft Cloud Advocates Team
Source: Microsoft Learn
π Course Link
STAT 451: Introduction to Machine Learning β Sebastian Raschka (UWβMadison)
π Free Online Course
π¬ Video lectures
β° ~20 hours
πββοΈ Self paced
Teacher π¨βπ« : Sebastian Raschka (UβWisconsinβMadison)
Source: UWβMadison STAT 451 YouTube & course materials
π Course Link
Intro to Machine Learning with Python (Kaggle)
Rating βοΈ: 4.5 out of 5
Students π¨βπ: 125,000+
Duration β°: 3hrs 30min
Created by: Kaggle (Dan Becker)
π Course Link
Googleβs Machine Learning Crash Course
β³Modules: 25+
Duration β°: 15 hours
πββοΈ Self paced
Resource: Google AI
π Course Link
Machine Learning Specialization β DeepLearning.AI (Audit Free)
Rating βοΈ: 4.8 out of 5
Students π¨βπ: 900,000+
Duration β°: ~30 hours (3 courses)
Created by: Andrew Ng (DeepLearning.AI)
π Course Link
Introduction to Machine Learning β CMU (10-301/601)
π Free Online Course
π¬ Video lectures
π Lecture notes (PDF)
β° ~30 hours
πββοΈ Self-paced
Teacher π¨βπ«: CMU Faculty (varies by year)
Source: Carnegie Mellon University
π Lecture Notes
Machine Learning Full Course β Edureka (YouTube)
π¬ 1 video lesson (full course)
Durationβ°: 10 hours
πββοΈ Self paced
Resource: YouTube
π Course Link
#machinelearning #ml
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
www.freecodecamp.org
Learn to Code β For Free
β€7
HandsβOn Intro to Data Science with Python (2025, Univ. of Applied Sciences DΓΌsseldorf)
π Topic: Pythonβbased data science workflow from scratch
π Format: Jupyter notebooks + datasets + PDF book
π Release: 2025
π¨βπ« Created by Huber et al. at DΓΌsseldorf UAS & ZDD
β° Duration: Selfβpaced (~40 hrs)
π Link: https://florian-huber.github.io/data_science_course/book/cover.html
π Description: A modern, project-oriented course teaching Pandas, Matplotlib, scikitβlearn via real datasets. Perfect for early-stage data scientists.
#datascience #python
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
π Topic: Pythonβbased data science workflow from scratch
π Format: Jupyter notebooks + datasets + PDF book
π Release: 2025
π¨βπ« Created by Huber et al. at DΓΌsseldorf UAS & ZDD
β° Duration: Selfβpaced (~40 hrs)
π Link: https://florian-huber.github.io/data_science_course/book/cover.html
π Description: A modern, project-oriented course teaching Pandas, Matplotlib, scikitβlearn via real datasets. Perfect for early-stage data scientists.
#datascience #python
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
β€7
Deep R Programming
An open-access textbook
by Marek Gagolewski
https://deepr.gagolewski.com/
#datascience #rprogramming
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
An open-access textbook
by Marek Gagolewski
https://deepr.gagolewski.com/
#datascience #rprogramming
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
gagolews/deepr
Deep R Programming
Deep R Programming is a comprehensive and in-depth introductory course on one of the most popular languages for data science. It equips ambitious students, professionals, and researchers with the knowledge and skills to become independent users of this potentβ¦
π2
SQL/MySQL Learning Series: Day 8οΈβ£
πΉ CREATE TABLE β Defining Structure
π§± Everything starts with structure! In SQL,
---
π Letβs create a sample table:
As you can see first you write table name, and inside brackets you put column names and type of each column name. Id is also set as primary key (we talked about primary keys in day 6οΈβ£).
---
π What this means:
*
*
*
*
*
---
π This will define a table like:
You now have a skeleton ready to fill in!
Make sure to leave reactions β€οΈπ― if you liked this post.
Next: Weβll add some real data with
#SQL #MySQL #SqlLearningSeries
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
πΉ CREATE TABLE β Defining Structure
π§± Everything starts with structure! In SQL,
CREATE TABLE
lets you define your data blueprint β columns, data types, and constraints.---
π Letβs create a sample table:
CREATE TABLE employees (
id INT PRIMARY KEY,
name VARCHAR(100),
department VARCHAR(50),
salary DECIMAL(10,2),
hire_date DATE
);
As you can see first you write table name, and inside brackets you put column names and type of each column name. Id is also set as primary key (we talked about primary keys in day 6οΈβ£).
---
π What this means:
*
id
: Unique identifier for each employee.*
name
: Stores up to 100 characters.*
department
: Like "HR", "IT", etc.*
salary
: Precise amount with decimals.*
hire_date
: Date the person joined.---
π This will define a table like:
+----+------------+-------------+----------+------------+
| id | name | department | salary | hire_date |
+----+------------+-------------+----------+------------+
| | | | | |
+----+------------+-------------+----------+------------+
You now have a skeleton ready to fill in!
Make sure to leave reactions β€οΈπ― if you liked this post.
Next: Weβll add some real data with
INSERT INTO
π#SQL #MySQL #SqlLearningSeries
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
β€14
Introduction to Computer Science and Programming Using Python
πββ Instructor-paced
β° 9 weeks - 14-16 hours per week
Instructorsπ¨βπ«: John Guttag and Eric Grimson
π Course Link
#computerscience #python
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
πββ Instructor-paced
β° 9 weeks - 14-16 hours per week
Instructorsπ¨βπ«: John Guttag and Eric Grimson
π Course Link
#computerscience #python
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
edX
MITx: Introduction to Computer Science and Programming Using Python. | edX
An introduction to computer science as a tool to solve real-world analytical problems using Python 3.5.
β€2π1
Forwarded from ChatGPT | LLM mastery
π§ New open-source LLMs: gpt-oss-120B & 20B
OpenAI has just launched those two new models from the gpt-oss project.
I have already tested them!
Theyβre positioned as open, local alternatives to GPT-4-class models - and yes:
This of course means they are completely free β
Hereβs the breakdown:
πΉ gpt-oss-20B β smaller, easier to run locally (with a beefy GPU). Decent for coding, Q&A, and experimentation.
πΉ gpt-oss-120B β huge model aiming for GPT-4-like reasoning. Needs serious hardware (128GB+ VRAM), but shows promising results for a fully offline model.
β οΈ Letβs be honest:
Theyβre not better than GPT-4o - slower, less nuanced, and less aligned out of the box. But for a local, no-API setup, theyβre a huge step forward for open source LLMs.
Some facts:
β’ First openβweight release since GPTβ2- theyβre fully downloadable under Apache 2.0.
β’ 20B runs on a 16 GB GPU, 120B needs something massive (80 GB+).
β’ They actually think step by step. Not as sharp as GPTβ4, but surprisingly solid - 120B competes with o3/o4βmini.
If you want to know more: https://openai.com/index/introducing-gpt-oss/
OpenAI has just launched those two new models from the gpt-oss project.
I have already tested them!
Theyβre positioned as open, local alternatives to GPT-4-class models - and yes:
they actually run on your own machine (with enough hardware).
This of course means they are completely free β
Hereβs the breakdown:
πΉ gpt-oss-20B β smaller, easier to run locally (with a beefy GPU). Decent for coding, Q&A, and experimentation.
πΉ gpt-oss-120B β huge model aiming for GPT-4-like reasoning. Needs serious hardware (128GB+ VRAM), but shows promising results for a fully offline model.
β οΈ Letβs be honest:
Theyβre not better than GPT-4o - slower, less nuanced, and less aligned out of the box. But for a local, no-API setup, theyβre a huge step forward for open source LLMs.
Some facts:
β’ First openβweight release since GPTβ2- theyβre fully downloadable under Apache 2.0.
β’ 20B runs on a 16 GB GPU, 120B needs something massive (80 GB+).
β’ They actually think step by step. Not as sharp as GPTβ4, but surprisingly solid - 120B competes with o3/o4βmini.
If you want to know more: https://openai.com/index/introducing-gpt-oss/
Openai
Introducing gpt-oss
gpt-oss-120b and gpt-oss-20b push the frontier of open-weight reasoning models
β€6
I can't believe it, those guys just stole post i wrote π
I shared it few minutes ago βοΈ
All posts in all our channels are custom generated and written by me...
Sometimes I ask ChatGPT to format my sentences but all courses I share, all tricks and tips, all tech news etc... everything is written by me and few of you guys that step up to help me run our channels.
We spend many hours every day preparing content for you and all those big channels out there just stole posts like they created them.
And yet somehow they got 50-100k subs and our @chatgpt_bds channel where i initially shared it has only 2k π€·ββοΈ
Same keeps happening to @python_bds and many other channels of ours.
Confusing isn't it?
I shared it few minutes ago βοΈ
All posts in all our channels are custom generated and written by me...
Sometimes I ask ChatGPT to format my sentences but all courses I share, all tricks and tips, all tech news etc... everything is written by me and few of you guys that step up to help me run our channels.
We spend many hours every day preparing content for you and all those big channels out there just stole posts like they created them.
And yet somehow they got 50-100k subs and our @chatgpt_bds channel where i initially shared it has only 2k π€·ββοΈ
Same keeps happening to @python_bds and many other channels of ours.
Confusing isn't it?
π±6β€2π2
ChatGPT | LLM mastery
π§ New open-source LLMs: gpt-oss-120B & 20B OpenAI has just launched those two new models from the gpt-oss project. I have already tested them! Theyβre positioned as open, local alternatives to GPT-4-class models - and yes: they actually run on your own machineβ¦
Let me just correct myself here.
Its not open-source but its open-weight!
Open-weight means you can get access to the trained model itself, customize or deploy it however you like.
but it is not fully open source because you don't have access to everything behind the model.
One of our subscribers just corrected me in this post comment section, I thank her for that!
She even wrote a blog related to this topic π€―
https://reamby.substack.com/p/open-weight-open-source
I really like when our subs show some engagement and extensive knowledge. Good job!
Its not open-source but its open-weight!
Open-weight means you can get access to the trained model itself, customize or deploy it however you like.
but it is not fully open source because you don't have access to everything behind the model.
One of our subscribers just corrected me in this post comment section, I thank her for that!
She even wrote a blog related to this topic π€―
https://reamby.substack.com/p/open-weight-open-source
I really like when our subs show some engagement and extensive knowledge. Good job!
Substack
Open-Weight β Open-Source
Half-Open and Fully Applauded
β€11
OpenAI announced the GPT-5 models in their livestream yesterday!
I watched it live and one interesting statement Sam Altman said was:
Watch it here π https://openai.com/live/
Alt link: https://www.youtube.com/watch?v=0Uu_VJeVVfo
I watched it live and one interesting statement Sam Altman said was:
GPT-3 sort of felt to me like talking to a high school student...
GPT-4 felt like you're kind of talking to a college student.
GPT-5 is the first time that it really feels like talking to an expert in any topic, like a PhD-level expert.
Watch it here π https://openai.com/live/
Alt link: https://www.youtube.com/watch?v=0Uu_VJeVVfo
Openai
Livestream
π4β€2π₯1