Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources

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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.

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“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/ ✅ Free Courses with Certificate: https://t.me/free4unow_backup

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! 👍👍 Share your responses to this 👍❤️

Data Lake vs Data Warehouse

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