Data Science Projects
Perfect channel for Data Scientists Learn Python, AI, R, Machine Learning, Data Science and many more Admin: @love_data
نمایش بیشتر📈 تحلیل کانال تلگرام Data Science Projects
کانال Data Science Projects (@pythonspecialist) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 56 173 مشترک است و جایگاه 3 022 را در دسته آموزش و رتبه 6 100 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 56 173 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 05 سپتامبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 299 و در ۲۴ ساعت گذشته برابر 1 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 5.28% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً N/A% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 0 بازدید دریافت میکند. در اولین روز معمولاً 0 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 0 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند learning, |--, sql, analyst, algorithm تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Perfect channel for Data Scientists
Learn Python, AI, R, Machine Learning, Data Science and many more
Admin: @love_data”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 06 سپتامبر, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کردهاند.
در حال بارگیری داده...
| تاریخ | رشد مشترکین | اشارات | کانالها | |
| 06 سپتامبر | +1 | |||
| 05 سپتامبر | +1 | |||
| 04 سپتامبر | +17 | |||
| 03 سپتامبر | +17 | |||
| 02 سپتامبر | +6 | |||
| 01 سپتامبر | +16 |
| 2 | 🚀 𝗙𝗥𝗘𝗘 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗯𝘆 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀🔥
Get FREE access to company-specific interview kits, previous questions, preparation strategies, and important resources! 👇
Google :- https://pdlink.in/4xtUyIG
Amazon :- https://pdlink.in/45Q0YWR
Microsoft :- https://pdlink.in/3Up1bha
Wipro :- https://pdlink.in/4fMo1rA
Infosys :- https://pdlink.in/3TRn8p0
📌 share it with friends preparing for placements | 2 831 |
| 3 | Myths About Data Science:
✅ Data Science is Just Coding
Coding is a part of data science. It also involves statistics, domain expertise, communication skills, and business acumen. Soft skills are as important or even more important than technical ones
✅ Data Science is a Solo Job
I wish. I wanted to be a data scientist so I could sit quietly in a corner and code. Data scientists often work in teams, collaborating with engineers, product managers, and business analysts
✅ Data Science is All About Big Data
Big data is a big buzzword (that was more popular 10 years ago), but not all data science projects involve massive datasets. It’s about the quality of the data and the questions you’re asking, not just the quantity.
✅ You Need to Be a Math Genius
Many data science problems can be solved with basic statistical methods and simple logistic regression. It’s more about applying the right techniques rather than knowing advanced math theories.
✅ Data Science is All About Algorithms
Algorithms are a big part of data science, but understanding the data and the business problem is equally important. Choosing the right algorithm is crucial, but it’s not just about complex models. Sometimes simple models can provide the best results. Logistic regression! | 3 071 |
| 4 | 🚨 BREAKING: PW Skills x Microsoft just launched The Complete Live Gen AI Engineering Program
Generative AI isn't the future anymore, it's the present. And now you can master it live, with Microsoft's backing behind you.
Learn Agentic AI, LLMOps & real-world AI Development, taught through live interactive classes, in Hinglish, over a structured 5-month journey.
🎓 Bonus: Includes a Premium Microsoft Module, added credibility, added skills, added career value.
🎁 Use code GENAI20 and get 20% OFF instantly.
💰 Starting at just ₹4,999.
📅 Batch starts 20th August 2026, seats are limited, and this launch price won't last.
Don't just watch the AI wave. Build it.
👉 Reserve your seat now: https://pwskills.com/generative-ai/gen-ai-engineering-course-654105/?source=pwskills.com&position=course_dropdown&from=course_description | 2 934 |
| 5 | 🚀 Data Analyst Roadmap
First things first 👇
❌ Don’t buy expensive courses to become a Data Analyst.
💡 Consistency > Certifications > Courses
Skills and practice are what actually get you hired.
✅ Mandatory Skills for a Data Analyst
1️⃣ SQL
Practice as much as possible.
This is the most important skill for any Data Analyst.
📚 Resource
YouTube Channel: Ankit Bansal
Playlist: SQL Practice / SQL Interview Questions
2️⃣ Excel
Advanced Excel is required.
Focus on:
• Formulas
• Pivot Tables
• Power Query Basics
• Data Cleaning
• Data Analysis functions
3️⃣ BI Tools
Choose ONE:
• Power BI
• Tableau
❌ Do NOT learn both at the same time.
If you choose Power BI, learn these deeply:
• Power Query
• DAX
• M Code
📚 Resources
YouTube Channel: Learnit Training
Video: Power BI DAX Full Tutorial for Beginners
YouTube Channel: Enterprise DNA
Playlist: DAX Practice Series
YouTube Channel: Goodly (Chandeep Chhabra)
Playlists: Power Query Tutorials and M Code Tutorials
4️⃣ Python
Focus mainly on:
• NumPy
• Pandas
• Basic visualization libraries (Matplotlib / Seaborn)
You don’t need deep ML knowledge for Data Analyst roles.
⭐ Good-to-Have Skills
These are not mandatory but help in career growth:
• Machine Learning (basic understanding)
• PySpark
• Databricks (becoming popular in data teams)
• Cloud platforms
Cloud options:
• Azure
• GCP
🎓 Certifications (Optional)
Certifications can help but are not required.
Useful ones:
• Microsoft Power BI Certification – PL-300
• Tableau Certification
• Azure Cloud Certification
❌ No other certifications are required.
Save your money.
Focus on skills, projects, and practice.
Credit: Mohan | 2 920 |
| 6 | 9 tips to get started with Data Analysis:
Learn Excel, SQL, and a programming language (Python or R)
Understand basic statistics and probability
Practice with real-world datasets (Kaggle, Data.gov)
Clean and preprocess data effectively
Visualize data using charts and graphs
Ask the right questions before diving into data
Use libraries like Pandas, NumPy, and Matplotlib
Focus on storytelling with data insights
Build small projects to apply what you learn
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍 | 3 577 |
| 7 | 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 (𝗡𝗼 𝗖𝗼𝗱𝗶𝗻𝗴 𝗡𝗲𝗲𝗱𝗲𝗱)
Apply Now👉:- https://pdlink.in/4aYWald
By E&ICT Academy, IIT Roorkee
Batch Closing Soon - 18th July 2026 | 3 892 |
| 8 | Data Science Benefits | 3 615 |
| 9 | 🚀 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 💻🔥
These FREE courses can help you learn Data Analytics, Power BI & Excel skills that companies actually hire for 🚀
✨ What you’ll learn:
✔ Excel + Power BI 📊
✔ Data Cleaning with Power Query
✔ Interactive Dashboards
✔ Modern Analytics Skills
💯 Beginner Friendly + FREE Learning
𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
https://pdlink.in/4tkPNyM
🎓 Perfect for Students, Freshers & Career Switchers | 3 693 |
| 10 | ✅ Step-by-Step Guide to Create a Data Science Portfolio 🎯📊
✅ 1️⃣ Pick Your Focus Area
Decide what kind of data scientist you want to be:
• Data Analyst → Excel, SQL, Power BI/Tableau 📈
• Machine Learning → Python, Scikit-learn, TensorFlow 🧠
• Data Engineer → Python, Spark, Airflow, Cloud ⚙️
• Full-stack DS → Mix of analysis + ML + deployment 🧑💻
✅ 2️⃣ Plan Your Portfolio Sections
Your portfolio should include:
• Home Page – Quick intro about you 👋
• About Me – Education, tools, skills 📝
• Projects – With code, visuals & explanations 📊
• Blog (optional) – Share insights & tutorials ✍️
• Contact – Email, LinkedIn, GitHub, etc. ✉️
✅ 3️⃣ Build the Portfolio Website
Options to build:
• Use Jupyter Notebook + GitHub Pages 🌐
• Create with Streamlit or Gradio (for interactive apps) ✨
• Full site: HTML/CSS or React + deploy on Netlify/Vercel 🚀
✅ 4️⃣ Add 2–4 Quality Projects
Project ideas:
• EDA on real-world datasets 🔍
• Machine learning prediction model 🔮
• NLP app (e.g., sentiment analysis) 💬
• Dashboard in Power BI/Tableau 📈
• Time series forecasting ⏳
Each project should include:
• Problem statement ❓
• Dataset source 📁
• Visualizations 📊
• Model performance ✅
• GitHub repo + live app link (if any) 🔗
• Brief write-up or blog 📄
✅ 5️⃣ Showcase on GitHub
• Create clean repos with README files 🌟
• Add visuals, summaries, and instructions 📸
• Use Jupyter notebooks or Markdown ✏️
✅ 6️⃣ Deploy and Share
• Use Streamlit Cloud, Hugging Face, or Netlify 🚀
• Share on LinkedIn & Kaggle 🤝
• Use Medium/Hashnode for blogs 📝
• Create a resume link to your portfolio 🔗
💡 Pro Tips:
• Focus on storytelling: Why the project matters 📖
• Show your thought process, not just code 🤔
• Keep UI simple and clean ✨
• Add certifications and tools logos if needed 🏅
• Keep your portfolio updated every 2–3 months 🔄
🎯 Goal: When someone views your site, they should instantly see your skills, your projects, and your ability to solve real-world data problems.
💬 Tap ❤️ if this helped you! | 3 649 |
| 11 | 🚀 𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 - 𝗟𝗮𝘂𝗻𝗰𝗵 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿
If you’re serious about starting your career in tech, this is one opportunity you shouldn’t miss 🚀
✅ 2000+ Students Already Placed
🤝 500+ Hiring Partners
💼 Salary: ₹7.4 LPA
🚀 Highest Package: ₹41 LPA
💻 Get trained in in-demand tech skills
👨🏫 Learn from industry experts
📈 Get dedicated placement support
💸 Pay only after you land a job
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰 👇:-
https://pdlink.in/42WOE5H
Hurry! Limited seats are available.🏃♂️ | 2 407 |
| 12 | 🎓 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗶𝗻 𝟮𝟬𝟮𝟲
Boost your resume with Industry-recognized certifications without spending a single rupee 🌟
📚 Available from:
✅ Google
✅ Microsoft
✅ Cisco
✅ IBM
✅ HP
✅ Qualcomm
✅ TCS
✅ Infosys
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:
https://pdlink.in/3SNiXKz
🚀 Don't miss these FREE certification opportunities in 2026! | 2 327 |
| 13 | 🤝 Types of Machine Learning | 2 054 |
| 14 | GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model
The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.
What’s inside:
🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale;
🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer;
🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features;
🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load;
🔘Two MTP heads, enabling up to 2.2x faster generation;
🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels;
🔘A new online RL stage after SFT and DPO.
Results:
🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks:
🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size;
🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.
The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.
➡️ HuggingFace | 2 189 |
| 15 | Useful AI channels on WhatsApp 🤖
Artificial Intelligence: https://whatsapp.com/channel/0029VbBDFBI9Gv7NCbFdkg36
Python Programming: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
AI Tricks: https://whatsapp.com/channel/0029Vb6xxJGGk1FnoCYE660N
AI Discovery: https://whatsapp.com/channel/0029VbBHlc7H5JLuv8L9d72T
AI Magic: https://whatsapp.com/channel/0029VbBA1z1JuyAH7BNeT43b
OpenAI: https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
Tech News: https://whatsapp.com/channel/0029VbBo9qY1t90emAy5P62s
ChatGPT for Education: https://whatsapp.com/channel/0029Vb6r21H9hXFFoxvWR32C
ChatGPT Tips: https://whatsapp.com/channel/0029Vb6ZoSzBA1f3paReKB3B
AI for Leaders: https://whatsapp.com/channel/0029VbB9LO872WTwyqNlB63R
AI For Business: https://whatsapp.com/channel/0029VbBn5bn0rGiLOhM3vi1v
AI For Teachers: https://whatsapp.com/channel/0029Vb7LGgLCRs1mp86TH614
How to AI: https://whatsapp.com/channel/0029VbBHQZM7z4khHBTVtI0Q
AI For Students: https://whatsapp.com/channel/0029VbBIV47I7Be9BZMAJq3s
Copilot: https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l
Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
ChatGPT: https://whatsapp.com/channel/0029Vb6R8PI6WaKwRzLKKI0r
Deepseek: https://whatsapp.com/channel/0029Vb9js9sGpLHJGIvX5g1w
Finance & AI: https://whatsapp.com/channel/0029Vax0HTt7Noa40kNI2B1P
Google Facts: https://whatsapp.com/channel/0029VbBnkGm6LwHriVjB5I04
Perplexity AI: https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U
Grok AI: https://whatsapp.com/channel/0029VbAU3pWChq6T5bZxUk1r
Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t
AI Discovery: https://whatsapp.com/channel/0029VbBHlc7H5JLuv8L9d72T
AI News: https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
Machine Learning: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O
Jobs: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Double Tap ❤️ for more | 2 147 |
| 16 | 📊 Data Science Essentials: What Every Data Enthusiast Should Know!
1️⃣ Understand Your Data
Always start with data exploration. Check for missing values, outliers, and overall distribution to avoid misleading insights.
2️⃣ Data Cleaning Matters
Noisy data leads to inaccurate predictions. Standardize formats, remove duplicates, and handle missing data effectively.
3️⃣ Use Descriptive & Inferential Statistics
Mean, median, mode, variance, standard deviation, correlation, hypothesis testing—these form the backbone of data interpretation.
4️⃣ Master Data Visualization
Bar charts, histograms, scatter plots, and heatmaps make insights more accessible and actionable.
5️⃣ Learn SQL for Efficient Data Extraction
Write optimized queries (SELECT, JOIN, GROUP BY, WHERE) to retrieve relevant data from databases.
6️⃣ Build Strong Programming Skills
Python (Pandas, NumPy, Scikit-learn) and R are essential for data manipulation and analysis.
7️⃣ Understand Machine Learning Basics
Know key algorithms—linear regression, decision trees, random forests, and clustering—to develop predictive models.
8️⃣ Learn Dashboarding & Storytelling
Power BI and Tableau help convert raw data into actionable insights for stakeholders.
🔥 Pro Tip: Always cross-check your results with different techniques to ensure accuracy!
Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
DOUBLE TAP ❤️ IF YOU FOUND THIS HELPFUL! | 2 200 |
| 17 | Data Analytics Projects List✨! 💼📊
Beginner-Level Projects 🏁
(Focus: Excel, SQL, data cleaning)
1️⃣ Sales performance dashboard in Excel
2️⃣ Customer feedback summary using text data
3️⃣ Clean and analyze a CSV file with missing data
4️⃣ Product inventory analysis with pivot tables
5️⃣ Use SQL to query and visualize a retail dataset
6️⃣ Create a revenue tracker by month and category
7️⃣ Analyze demographic data from a survey
8️⃣ Market share analysis across product lines
9️⃣ Simple cohort analysis using Excel
🔟 User signup trends using SQL GROUP BY and DATE
Intermediate-Level Projects 🚀
(Focus: Python, data visualization, EDA)
1️⃣ Churn analysis from telco dataset using Python
2️⃣ Power BI sales dashboard with filters & slicers
3️⃣ E-commerce data segmentation with clustering
4️⃣ Forecast site traffic using moving averages
5️⃣ Analyze Netflix/Bollywood IMDB datasets
6️⃣ A/B test results evaluation for marketing campaign
7️⃣ Customer lifetime value prediction
8️⃣ Explore correlations in vaccination or health datasets
9️⃣ Predict loan approval using logistic regression
🔟 Create a Tableau dashboard highlighting HR insights
Advanced-Level Projects 🔥
(Focus: Machine learning, big data, real-world scenarios)
1️⃣ Fraud detection using anomaly detection on banking data
2️⃣ Real-time dashboard using streaming data (Power BI + API)
3️⃣ Predictive model for sales forecasting with ML
4️⃣ NLP sentiment analysis of product reviews or tweets
5️⃣ Recommender system for e-commerce products
6️⃣ Build ETL pipeline (Python + SQL + cloud storage)
7️⃣ Analyze and visualize stock market trends
8️⃣ Big data analysis using Spark on a large dataset
9️⃣ Create a data compliance audit dashboard
🔟 Geospatial heatmap of business locations vs revenue
📂 Pro Tip: Host these on GitHub, add visuals, and explain your process—great for impressing recruiters! 🙌 | 2 406 |
| 18 | Step-by-Step Roadmap to Learn Data Science in 2025:
Step 1: Understand the Role
A data scientist in 2025 is expected to:
Analyze data to extract insights
Build predictive models using ML
Communicate findings to stakeholders
Work with large datasets in cloud environments
Step 2: Master the Prerequisite Skills
A. Programming
Learn Python (must-have): Focus on pandas, numpy, matplotlib, seaborn, scikit-learn
R (optional but helpful for statistical analysis)
SQL: Strong command over data extraction and transformation
B. Math & Stats
Probability, Descriptive & Inferential Statistics
Linear Algebra & Calculus (only what's necessary for ML)
Hypothesis testing
Step 3: Learn Data Handling
Data Cleaning, Preprocessing
Exploratory Data Analysis (EDA)
Feature Engineering
Tools: Python (pandas), Excel, SQL
Step 4: Master Machine Learning
Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forests, XGBoost
Unsupervised Learning: K-Means, Hierarchical Clustering, PCA
Deep Learning (optional): Use TensorFlow or PyTorch
Evaluation Metrics: Accuracy, AUC, Confusion Matrix, RMSE
Step 5: Learn Data Visualization & Storytelling
Python (matplotlib, seaborn, plotly)
Power BI / Tableau
Communicating insights clearly is as important as modeling
Step 6: Use Real Datasets & Projects
Work on projects using Kaggle, UCI, or public APIs
Examples:
Customer churn prediction
Sales forecasting
Sentiment analysis
Fraud detection
Step 7: Understand Cloud & MLOps (2025+ Skills)
Cloud: AWS (S3, EC2, SageMaker), GCP, or Azure
MLOps: Model deployment (Flask, FastAPI), CI/CD for ML, Docker basics
Step 8: Build Portfolio & Resume
Create GitHub repos with well-documented code
Post projects and blogs on Medium or LinkedIn
Prepare a data science-specific resume
Step 9: Apply Smartly
Focus on job roles like: Data Scientist, ML Engineer, Data Analyst → DS
Use platforms like LinkedIn, Glassdoor, Hirect, AngelList, etc.
Practice data science interviews: case studies, ML concepts, SQL + Python coding
Step 10: Keep Learning & Updating
Follow top newsletters: Data Elixir, Towards Data Science
Read papers (arXiv, Google Scholar) on trending topics: LLMs, AutoML, Explainable AI
Upskill with certifications (Google Data Cert, Coursera, DataCamp, Udemy)
Free Resources to learn Data Science
Kaggle Courses: https://www.kaggle.com/learn
CS50 AI by Harvard: https://cs50.harvard.edu/ai/
Fast.ai: https://course.fast.ai/
Google ML Crash Course: https://developers.google.com/machine-learning/crash-course
Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998
Data Science Books: https://t.me/datalemur
React ❤️ for more | 2 249 |
| 19 | Introduction to Databases with SQL: Free Harvard Course
👇👇
https://cs50.harvard.edu/sql/2023/ | 2 412 |
| 20 | 🎯 5 best YouTube videos to learn Claude in 2026 👇
1/ How to Use Claude AI in 2026: Complete Beginner's Guide:
https://www.youtube.com/watch?v=Nc46GvRXTjA
2/ FULL Claude Tutorial for Beginners in 2026 (Become a PRO):
https://www.youtube.com/watch?v=rRrBbyv3ChM
3/ Claude Code Tutorial for Beginners: Build App with AI:
https://www.youtube.com/watch?v=6q8joS_592k
4/ How to Build Claude Skills that Generate Revenue (Full Course):
https://www.youtube.com/watch?v=sduaTkhIm_w
5/ Claude Code for Beginners, Build Your First App with AI:
https://www.youtube.com/watch?v=s-Mc26Ytz10 | 2 918 |
