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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
Больше📈 Аналитический обзор Telegram-канала Data science/ML/AI
Канал Data science/ML/AI (@datascience_bds) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 13 667 подписчиков, занимая 9 391 место в категории Технологии и приложения и 31 743 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 13 667 подписчиков.
Согласно последним данным от 08 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 150, а за последние 24 часа — 4, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 7.97%. В первые 24 часа после публикации контент обычно набирает 2.27% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 1 089 просмотров. В течение первых суток публикация набирает 310 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 5.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как panda, learning, row, api, ethic.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Data science and machine learning hub
Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources.
For beginners, data scientists and ML engineers
👉 https://rebrand.ly/bigdatachannels
DMCA: @disclosure_bds
Contact: @mldatasci...”
Благодаря высокой частоте обновлений (последние данные получены 09 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
13 667
Подписчики
+424 часа
+437 дней
+15030 день
Архив постов
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📚 Data Science Riddle
Why is data versioning(e.g., DVC, LakeFS) essential in ML workflows?
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Instead of starting every project from scratch, use this template to build AI apps with structure and speed
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📚 Data Science Riddle
In A/B testing, why is random assignment of users essential?
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⚡ Parallelism In Databricks ⚡
1️⃣ DEFINITION
Parallelism = running many tasks 🏃♂️🏃♀️ at the same time
(instead of one by one 🐢).
In Databricks (via Apache Spark), data is split into
📦 partitions, and each partition is processed
simultaneously across worker nodes 💻💻💻.
2️⃣ KEY CONCEPTS
🔹 Partition = one chunk of data 📦
🔹 Task = work done on a partition 🛠️
🔹 Stage = group of tasks that run in parallel ⚙️
🔹 Job = complete action (made of stages + tasks) 📊
3️⃣ HOW IT WORKS
✅ Step 1: Dataset ➡️ divided into partitions 📦📦📦
✅ Step 2: Each partition ➡️ assigned to a worker 💻
✅ Step 3: Workers run tasks in parallel ⏩
✅ Step 4: Results ➡️ combined into final output 🎯
4️⃣ EXAMPLES
# Increase parallelism by repartitioning
df = spark.read.csv("/data/huge_file.csv")
df = df.repartition(200) # ⚡ 200 parallel tasks
# Spark DataFrame ops run in parallel by default 🚀
result = df.groupBy("category").count()
# Parallelize small Python objects 📂
rdd = spark.sparkContext.parallelize(range(1000), numSlices=50)
rdd.map(lambda x: x * 2).collect()
# Parallel workflows in Jobs UI ⚡
# Independent tasks = run at the same time.
5️⃣ BEST PRACTICES
⚖️ Balance partitions → not too few, not too many
📉 Avoid data skew → partitions should be even
🗃️ Cache data if reused often
💪 Scale cluster → more workers = more parallelism
====================================================
📌 SUMMARY
Parallelism in Databricks = split data 📦 →
assign tasks 🛠️ → run them at the same time ⏩ →
faster results 🚀
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📚 Data Science Riddle
You train a CNN for image classification but loss stops decreasing early. What's your next step?
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Feature Engineering: The Hidden Skill That Makes or Breaks ML Models
Most people chase better algorithms. Professionals chase better features.
Because no matter how fancy your model is, if the data doesn’t speak the right language. it won’t learn anything meaningful.
🔍 So What Exactly Is Feature Engineering?
It’s not just cleaning data. It’s translating raw, messy reality into something your model can understand.
You’re basically asking:
“How can I represent the real world in numbers, without losing its meaning?”Example: ➖ “Date of birth” → Age (time-based insight) ➖ “Text review” → Sentiment score (emotional signal) ➖ “Price” → log(price) (stabilized distribution) Every transformation teaches your model how to see the world more clearly. ⚙️ Why It Matters More Than the Model You can’t outsmart bad features. A simple linear model trained on smartly engineered data will outperform a deep neural net trained on noise. Kaggle winners know this. They spend 80% of their time creating and refining features not tuning hyperparameters. Why? Because models don’t create intelligence, They extract it from what you feed them. 🧩 The Core Idea: Add Signal, Remove Noise Feature engineering is about sculpting your data so patterns stand out. You do that by: ✔️ Transforming data (scale, encode, log). ✔️ Creating new signals (ratios, lags, interactions). ✔️ Reducing redundancy (drop correlated or useless columns). Every step should make learning easier not prettier. ⚠️ Beware of Data Leakage Here’s the silent trap: using future information when building features. For example, when predicting loan default, if you include “payment status after 90 days,” your model will look brilliant in training and fail in production. Golden rule: 👉 A feature is valid only if it’s available at prediction time. 🧠 Think Like a Domain Expert Anyone can code transformations. But great data scientists understand context. They ask: ❔What actually influences this outcome in real life? ❔How can I capture that influence as a feature? When you merge domain intuition with technical precision, feature engineering becomes your superpower. ⚡️ Final Takeaway The model is the student. The features are the teacher. And no matter how capable the student if the teacher explains things poorly, learning fails.
Feature engineering isn’t preprocessing. It’s the art of teaching your model how to understand the world.
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📚 Data Science Riddle
You have messy CSVs arriving daily. What's your first production step?
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🚀 Databricks Tip: REPLACE vs MERGE
When updating Delta tables, you’ve got two powerful options:
🔹 REPLACE TABLE … ON
📚 Like throwing away the entire library and rebuilding it.
- Drops the old table & recreates it.
- Schema + data = fully replaced.
- ⚡ Super fast but destructive (old data gone).
- ✅ Best for full refreshes or schema changes.
🔹 MERGE
📖 Like updating only the books that changed.
- Works row by row.
- Updates, inserts, or deletes specific records.
- 🔍 Preserves unchanged data.
- ✅ Best for incremental updates or CDC (Change Data Capture).
⚖️ Key Difference
- REPLACE = Start fresh with a new table.
- MERGE = Surgically update rows without losing the rest.
👉 Rule of thumb:
Use REPLACE for full rebuilds,
Use MERGE for incremental upserts.
#Databricks #DeltaLake
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+3
🤖 AI that creates AI: ASI-ARCH finds 106 new SOTA architectures
ASI-ARCH — experimental ASI that autonomously researches and designs neural nets. It hypothesizes, codes, trains & tests models.
💡 Scale:
1,773 experiments → 20,000+ GPU-hours.
Stage 1 (20M params, 1B tokens): 1,350 candidates beat DeltaNet.
Stage 2 (340M params): 400 models → 106 SOTA winners.
Top 5 trained on 15B tokens vs Mamba2 & Gated DeltaNet.
📊 Results:
PathGateFusionNet: 48.51 avg (Mamba2: 47.84, Gated DeltaNet: 47.32).
BoolQ: 60.58 vs 60.12 (Gated DeltaNet).
Consistent gains across tasks.
🔍 Insights:
Prefers proven tools (gating, convs), refines them iteratively.
Ideas come from: 51.7% literature, 38.2% self-analysis, 10.1% originality.
SOTA share: self-analysis ↑ to 44.8%, literature ↓ to 48.6%.
📎 Project page | Arxiv | GitHub
#AI #ML #Research #ASIARCH@datascience_bds
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📚 Data Science Riddle
Your object detection model misses small objects. Easiest fix?
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Statistical Moments (M1, M2) for Data Analysis
Here are 5 curated PDFs diving into the mean (M1), variance (M2), and their applications in crafting research questions and sourcing data.
A channel member requested resources on this topic and we delivered.
If you have a topic you want resources on let us know, and we’ll make it happen!
@datascience_bds
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📚 Data Science Riddle
Model Accuracy improves after dropping half the features. Why?
