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🚀Here are 5 fresh Project ideas for Data Analysts 👇

🎯 𝗔𝗶𝗿𝗯𝗻𝗯 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 🏠
https://www.kaggle.com/datasets/arianazmoudeh/airbnbopendata

💡This dataset describes the listing activity of homestays in New York City

🎯 𝗧𝗼𝗽 𝗦𝗽𝗼𝘁𝗶𝗳𝘆 𝘀𝗼𝗻𝗴𝘀 𝗳𝗿𝗼𝗺 𝟮𝟬𝟭𝟬-𝟮𝟬𝟭𝟵 🎵

https://www.kaggle.com/datasets/leonardopena/top-spotify-songs-from-20102019-by-year

🎯𝗪𝗮𝗹𝗺𝗮𝗿𝘁 𝗦𝘁𝗼𝗿𝗲 𝗦𝗮𝗹𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 📈

https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
💡Use historical markdown data to predict store sales

🎯 𝗡𝗲𝘁𝗳𝗹𝗶𝘅 𝗠𝗼𝘃𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗩 𝗦𝗵𝗼𝘄𝘀 📺

https://www.kaggle.com/datasets/shivamb/netflix-shows
💡Listings of movies and tv shows on Netflix - Regularly Updated

🎯𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗷𝗼𝗯𝘀 𝗹𝗶𝘀𝘁𝗶𝗻𝗴𝘀 💼

https://www.kaggle.com/datasets/cedricaubin/linkedin-data-analyst-jobs-listings
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Top Platforms for Building Data Science Portfolio

Build an irresistible portfolio that hooks recruiters with these free platforms.

Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.

1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace

7 Websites to Learn Data Science for FREE🧑‍💻

✅ w3school
✅ datasimplifier
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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://news.1rj.ru/str/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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Creating a data science portfolio is a great way to showcase your skills and experience to potential employers. Here are some steps to help you create a strong data science portfolio:

1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions.

2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis.

3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization.

4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs.

5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis.

6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented.

7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail.

8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills.

By following these steps, you can create a comprehensive and visually appealing data science portfolio that will impress potential employers and help you stand out in the competitive job market.

Top 10 Python Project Ideas 💡

Hey Guys👋,

The Average Salary Of a Data Scientist is 14LPA 

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CS229 Lecture Notes
Andrew Ng and Tengyu Ma

📚 Link

🔟 Free useful resources to learn Machine Learning

👉 Google

https://developers.google.com/machine-learning/crash-course

👉 Leetcode

https://leetcode.com/explore/featured/card/machine-learning-101

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https://www.hackerrank.com/domains/ai/machine-learning

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👉 FreeCodeCamp

https://www.freecodecamp.org/learn/machine-learning-with-python/

👉 Machine learning projects

https://news.1rj.ru/str/datasciencefun/392

👉 Kaggle

https://www.kaggle.com/learn/intro-to-machine-learning

https://www.kaggle.com/learn/intermediate-machine-learning

👉 Geeksforgeeks

https://www.geeksforgeeks.org/machine-learning/

👉 Create ML Models

https://docs.microsoft.com/en-us/learn/paths/create-machine-learn-models/

👉 Machine Learning Test Cheat Sheet

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