#Data Set

The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore

Top 20 SQL Concepts That Power Databases ! . في مدينة ساحلية لا تُذكر على الخرائط، كان هناك مبنى مهجور يُعرف بين الناس باسم البيت الذي لا يُضاء. لا أحد يعرف متى بُني، ولا من سكنه أولًا، لكن النوافذ كانت دائمًا سوداء، حتى في عزّ النهار. آدم، في السادسة والعشرين من عمره، لم يكن يؤمن بالقصص التي يرددها الناس. كل ما سمعه عن اختفاءات وهمسات ليلية اعتبره مبالغة رخيصة. لكن الشيء الوحيد الذي أقلقه فعلًا: أن اسم والده كان مكتوبًا داخل ملف قديم في أرشيف البلدية… وتحت خانة «آخر عنوان»: ذلك البيت. في ليلة ثقيلة الرطوبة، قرر آدم الدخول. لم يحمل مصباحًا، فقط هاتفه وفضوله. ما إن أغلق الباب خلفه حتى شعر أن الهواء تغيّر. كان البيت يتنفس ببطء. الجدران لم تكن متشققة كما توقع، بل ملساء بشكل غير طبيعي، وكأنها لم تُمس منذ سنوات. في الممر الطويل، وجد صورًا معلّقة، جميعها لأشخاص ينظرون مباشرة إلى العدسة… والشيء المرعب؟ كانوا يشبهونه. في الطابق العلوي، غرفة واحدة فقط كانت مفتوحة. على الطاولة دفتر جلدي. وعندما لمسه، انفتح من تلقاء نفسه. الصفحة الأولى حملت تاريخًا قديمًا، وتحتها جملة واحدة: «إن كنت تقرأ هذا، فقد حان دورك.» بدأت الصفحات تتحرك، تكتب نفسها بنفسها، تحكي قصة رجل حاول الهرب من شيء يسكنه…

Real world data > Kaggle 👏🏻 time to get dirty with data #dataset #machinelearning

comment “AI” for my full synthetic data tutorial Youtube video! save for later & follow for more! Save for later & follow for more! You can customize any dataset for any industry, business problem, or project and get way more interesting data than Kaggle. Plus, you can ask for imperfect data with inconsistent values, duplicates, or nulls to make it feel more realistic to the real world. You just have to know how to specify your requirements and constraints when prompt engineering. Here’s what you should specify: ✨ size of dataset(s) (rows / columns) ✨ column names and data types ✨ primary keys and foreign keys ✨ distribution and allowed values ✨ variation of datapoints ✨ downloadable as CSVs ✨ anything else that may impact your project! Full example below: You are a data engineer generating a realistic synthetic dataset for [INDUSTRY] and [PROJECT TYPE OR PURPOSE].Can you generate [NUMBER] realistic datasets with the following requirements.Create an [TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns you think would be useful. [PRIMARY KEY] is the primary key. [FOREIGN KEY 1] and [FOREIGN KEY 2] are foreign keys that connect to the [RELATED TABLE NAME] table. Ensure that [NUMBER] foreign key values exist in the related table but do not appear in this table (to simulate missing relationships).Create a [DIMENSION TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns. [PRIMARY KEY] is the primary key and connects to the first table. Ensure that [NUMBER] records in this table have no matching rows in the first table.For both tables, include high variation across values, non-even category distributions, and realistic data patterns. All ID fields should be random numeric values only (no letters).[Add in any other requirements, constraints, or behavior rules]Return each table as a separate, downloadable CSV file. Have you tried this hack and said goodbye to Kaggle yet?

Learn Tableau: ✨Templates✨ #tableau #tutorial #data #analytics #tech #datavisualization #mavenanalytics

Comment "DATA" for the links. You Will Never Struggle With Data Science Again 📌 Learn the most important foundations with these beginner-friendly resources: 1️⃣ Learn Python for Data Science – FreeCodeCamp’s full beginner course 2️⃣ Essence of Linear Algebra – 3Blue1Brown’s visual, intuitive playlist 3️⃣ Statistics – A Full Lecture (2025) – step-by-step breakdown of core stats concepts Stop feeling overwhelmed by Python, statistics, or linear algebra. These tutorials simplify the fundamentals of Data Science with clear explanations, visuals, and real-world examples. Whether you’re preparing for a career in Data Science, getting into machine learning, or just curious about data analysis, this is the fastest way to finally understand how it all fits together. Save this post, share it, and turn confusion into clarity with Python, Stats, and Linear Algebra for Data Science 📊

Data Science is art paired with logic 😊. In my soft girl in tech era 💕. Calm lights, chaotic datasets #datascience #stem #womeninstem #study #computerscience #coding #programming #tech #explore #ai #python #dev #tools #study #datascientist #data #design #software #codinglife #programmer #datascience #build #learning #growth #technology #information

Types of Data Structure . Video by @codingwithjd . . . #coding #cppproject #cplusplusprogramming #codinglife #codingbootcamp #codingisfun #codingninjas #coder #coderlife #coderslife #codersofinstagram #programming #programmingproblems #programmers #codingdays #codingchallenge #assembly #instagramgrowth #asciiart #cmd #cmdprompt #batchprocessing #aiartcommunity #artificialintelligence #deepseek #openai #meta #metaverse

I won’t be mad if you copy this entire roadmap… #dataanalyst #dataanalysis #dataanalytics #data #analyst #techjobs #breakintotech #wfh #workfromhome #wfhjobs #remotejobs #remotework #excel #sql #tableau #python

Comment ‘Projects’ to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist in 2026 and beyond 📈 the original video was 4 min Andi had to cut it down to 3 because instagram. Should I do a part 3v what are other skills that you would add to the list and let me know what I should cover in the next video 👩🏻‍💻 #datascientist #datascience #python #machinelearning #sql #ai

A data warehouse is a single source of truth that helps business functions perform their data analysis operations easier. Here's what a simple data warehouse looks like: 1. Data sources 2. Bronze layer 3. Silver layer 4. Gold layer 5. Analytics There's so much more that goes into a data warehouse (e.g. ingestion frequency, data governance policies, data validation checks etc), but this is a high level design you can start with. Different companies may configure the stages in different ways according to their users' unique requirements, but the generic workflow applies to all! #dataanalytics #dataengineering #datascience #techtok #dejavu

Thinking about applying to dataannotation.tech? Here’s what you need to know before you waste time. I’ve applied twice and still haven’t passed their test. And I’m not the only one—Reddit and YouTube comments are full of people saying the same. But if you do get in? The work is real. You’re annotating text—basically labeling or correcting AI-generated answers. It’s not hard once you know what to do, but passing the initial test? That’s the tricky part. Most people say the pay is decent for side income—nothing crazy—but flexible and remote. Searches for “data annotation” are blowing up right now because it feels like easy money. But you have to pass the exam. No shortcuts. No guarantees. I’ll share what I’ve learned from the process so far and what I’ve found online about the kind of work they assign. Let me know if you’ve passed the test or if you want me to keep documenting this journey.