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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
Mostrar más📈 Análisis del canal de Telegram Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
El canal Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 39 505 suscriptores, ocupando la posición 4 747 en la categoría Educación y el puesto 10 383 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 39 505 suscriptores.
Según los últimos datos del 11 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 205, y en las últimas 24 horas de 11, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.87%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.98% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 133 visualizaciones. En el primer día suele acumular 388 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- Intereses temáticos: El contenido se centra en temas clave como analytic, dataset, visualization, sql, learning.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 12 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
39 505
Suscriptores
+1124 horas
+367 días
+20530 días
Archivo de publicaciones
Day 1 Advanced Data Cleaning.pdf3.78 KB
Data Cleaning.pdf3.92 MB
Sales Data Analysis.pdf5.58 KB
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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20 high-income skills to learn in
2024 👇
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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ENJOY LEARNING 👍👍
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
Here, you will get free tutorials to learn SQL, Python, Power BI, Excel and many more
Hope you guys will like it 😄
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
