Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
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Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. Ads/ Promo: @love_data
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الشبكة:📈 نظرة تحليلية على قناة تيليجرام Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
تُعد قناة Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 39 600 مشتركاً، محتلاً المرتبة 4 697 في فئة التعليم والمرتبة 9 545 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 39 600 مشتركاً.
بحسب آخر البيانات بتاريخ 29 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -48، وفي آخر 24 ساعة بمقدار -5، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 2.24%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 0.70% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 886 مشاهدة. وخلال اليوم الأول يجمع عادةً 276 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 3.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل analytic, dataset, visualization, sql, learning.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former.
Ads/ Promo: @love_data”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 30 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
39 600
المشتركون
-524 ساعات
-477 أيام
-4830 أيام
أرشيف المشاركات
XGboost.pdf4.03 MB
Machine Learning
S. Sridhar, 2021
D Mart Products sales Analysis project 🏪.pdf3.51 MB
Count number of Faces using
Python – OpenCV 🧠🚀
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1. Artificial intelligence (AI)
2. Cloud computing
3. Data science
4. Machine learning
5. Blockchain/ Crypto
6. Data analytics and business intelligence
7. Data engineering
8. Finance
9. Web Development
10. Software development
11. UX design
12. Web design
13. Graphic design
14. Video editing
15. Content marketing
16. Blogging
17. Tech skills
18. Business analysis
19. Cybersecurity
20. Entrepreneurship
21. Freelancing
22. Sales and marketing
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House Price Prediction Models.pdf1.25 MB
Are you a data science beginner?
Here are 5 beginner-friendly data science project ideas
Loan Approval Prediction
Predict whether a loan will be approved based on customer demographic and financial data. This requires data preprocessing, feature engineering, and binary classification techniques.
Credit Card Fraud Detection
Detect fraudulent credit card transactions with a dataset that contains transactions made by credit cards. This is a good project for learning about imbalanced datasets and anomaly detection methods.
Netflix Movies and TV Shows Analysis
Analyze Netflix's movies and TV shows to discover trends in ratings, popularity, and genre distributions. Visualization tools and exploratory data analysis are key components here.
Sentiment Analysis of Tweets
Analyze the sentiment of tweets to determine whether they are positive, negative, or neutral. This project involves natural language processing and working with text data.
Weather Data Analysis
Analyze historical weather data from the National Oceanic and Atmospheric Administration (NOAA) to look for seasonal trends, weather anomalies, or climate change indicators. This project involves time series analysis and data visualization.
Join for more: https://t.me/sqlproject
ENJOY LEARNING 👍👍
5 Best beginner-friendly data science projects!
1-Loan Approval Prediction
2-Credit Card Fraud Detection
3-Netflix Movies and TV Shows Analysis
4-Sentiment Analysis of Tweets 5-Weather Data Analysis
These projects are ideal for beginners who want to grasp the fundamentals and get closer to solving real-life projects.
How to choose the right portfolio project?
Here are my best tips:
Pick What You Like: Choose a topic you enjoy to keep the project fun.
Show Your Skills: Make sure your project shows off what you can do, like organizing data or making charts.
Keep It Simple: Start with a simple project that you can expand later.
Use Available Data: Choose a project with easy-to-find data.
Underrated Telegram Channel for Data Analysts 👇👇
https://t.me/sqlspecialist
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Credit_Risk_Analysis.pdf1.29 MB
“The Best Public Datasets for Machine Learning and Data Science” by Stacy Stanford
https://datasimplifier.com/best-data-analyst-projects-for-freshers/
https://toolbox.google.com/datasetsearch
https://www.kaggle.com/datasets
http://mlr.cs.umass.edu/ml/
https://www.visualdata.io/
https://guides.library.cmu.edu/machine-learning/datasets
https://www.data.gov/
https://nces.ed.gov/
https://www.ukdataservice.ac.uk/
https://datausa.io/
https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html
https://www.kaggle.com/xiuchengwang/python-dataset-download
https://www.quandl.com/
https://data.worldbank.org/
https://www.imf.org/en/Data
https://markets.ft.com/data/
https://trends.google.com/trends/?q=google&ctab=0&geo=all&date=all&sort=0
https://www.aeaweb.org/resources/data/us-macro-regional
http://xviewdataset.org/#dataset
http://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php
http://image-net.org/
http://cocodataset.org/
http://visualgenome.org/
https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html?m=1
http://vis-www.cs.umass.edu/lfw/
http://vision.stanford.edu/aditya86/ImageNetDogs/
http://web.mit.edu/torralba/www/indoor.html
http://www.cs.jhu.edu/~mdredze/datasets/sentiment/
http://ai.stanford.edu/~amaas/data/sentiment/
http://nlp.stanford.edu/sentiment/code.html
http://help.sentiment140.com/for-students/
https://www.kaggle.com/crowdflower/twitter-airline-sentiment
https://hotpotqa.github.io/
https://www.cs.cmu.edu/~./enron/
https://snap.stanford.edu/data/web-Amazon.html
https://aws.amazon.com/datasets/google-books-ngrams/
http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm
https://code.google.com/archive/p/wiki-links/downloads
http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/
https://www.yelp.com/dataset
https://t.me/DataPortfolio/2
https://archive.ics.uci.edu/ml/datasets/Spambase
https://bdd-data.berkeley.edu/
http://apolloscape.auto/
https://archive.org/details/comma-dataset
https://www.cityscapes-dataset.com/
http://aplicaciones.cimat.mx/Personal/jbhayet/ccsad-dataset
http://www.vision.ee.ethz.ch/~timofter/traffic_signs/
http://cvrr.ucsd.edu/LISA/datasets.html
https://hci.iwr.uni-heidelberg.de/node/6132
http://www.lara.prd.fr/benchmarks/trafficlightsrecognition
http://computing.wpi.edu/dataset.html
https://mimic.physionet.org/
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🖥 Fake news Detection Machine Learning Project with 92%Accuracy
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Learning MongoDB
Stack Overflow
In 2024, mastering data science is expected to blend conventional educational methods with innovative approaches.
Here are the steps to learn data science in 2024:
Enroll in a data science program: Whether it's at a university or through online platforms, find a program covering machine learning, statistics, and data visualization.
Take online courses: Utilize platforms like Udacity, Udemy, or DataCamp to learn specific data science skills.
Engage in data science competitions: Join competitions on platforms like Kaggle to apply your skills to real-world problems and learn from others.
Connect with data science communities: Participate in forums, attend meetups, or join social media groups to network and learn from experienced data scientists.
Keep up with industry trends: Stay informed about the latest developments in data science by following blogs, podcasts, and industry publications.
Build a portfolio: Create projects to showcase your data science abilities and demonstrate your skills to potential employers or clients.
Happy learning! 👍👍
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Graph Data Science For Dummies Book.pdf4.66 MB
Data Lake vs Data Warehouse
How to chose statistical test based on data
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