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
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp

“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

https://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

https://xviewdataset.org/#dataset

https://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php

https://image-net.org/

https://cocodataset.org/

https://visualgenome.org/

https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html?m=1

https://vis-www.cs.umass.edu/lfw/

https://vision.stanford.edu/aditya86/ImageNetDogs/

https://web.mit.edu/torralba/www/indoor.html

https://www.cs.jhu.edu/~mdredze/datasets/sentiment/

https://ai.stanford.edu/~amaas/data/sentiment/

https://nlp.stanford.edu/sentiment/code.html

https://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/

https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm

https://code.google.com/archive/p/wiki-links/downloads

https://www.dt.fee.unicamp.br/~tiago/smsspamcollection/

https://www.yelp.com/dataset

https://t.iss.one/DataPortfolio/2

https://archive.ics.uci.edu/ml/datasets/Spambase

https://bdd-data.berkeley.edu/

https://apolloscape.auto/

https://archive.org/details/comma-dataset

https://www.cityscapes-dataset.com/

https://aplicaciones.cimat.mx/Personal/jbhayet/ccsad-dataset

https://www.vision.ee.ethz.ch/~timofter/traffic_signs/

https://cvrr.ucsd.edu/LISA/datasets.html

https://hci.iwr.uni-heidelberg.de/node/6132

https://www.lara.prd.fr/benchmarks/trafficlightsrecognition

https://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 💡

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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

👉 Hackerrank

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

👉 Hands-on Machine Learning

https://t.iss.one/datasciencefun/424

👉 FreeCodeCamp

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

👉 Machine learning projects

https://t.iss.one/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

https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/

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ENJOY LEARNING 👍👍

Sharing 20+ Diverse Datasets📊 for Data Science and Analytics practice!


1. How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview

2. Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand

3. Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction

4. Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data

5. Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction

6. Iris Dataset: https://archive.ics.uci.edu/ml/datasets/iris

7. Titanic Dataset: https://www.kaggle.com/c/titanic

8. Wine Quality Dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality

9. Heart Disease Dataset: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

10. Bengaluru House Price Dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data

11. Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

12. Credit Card Fraud Detection: https://www.kaggle.com/mlg-ulb/creditcardfraud

13. Netflix Movies and TV Shows: https://www.kaggle.com/shivamb/netflix-shows

14. Trending YouTube Video Statistics: https://www.kaggle.com/datasnaek/youtube-new

15. Walmart Store Sales Forecasting: https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting

16. FIFA 19 Complete Player Dataset: https://www.kaggle.com/karangadiya/fifa19

17. World Happiness Report: https://www.kaggle.com/unsdsn/world-happiness

18. TMDB 5000 Movie Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata

19. Students Performance in Exams: https://www.kaggle.com/spscientist/students-performance-in-exams

20. Twitter Sentiment Analysis Dataset: https://www.kaggle.com/kazanova/sentiment140

21. Digit Recognizer: https://www.kaggle.com/c/digit-recognizer


💻🔍 Don't miss out on these valuable resources for advancing your data science journey!

Hello everyone here are some tableau projects along with the datasets to work on

1. Sales Performance Dashboard:
   - Kaggle: [Sales dataset](https://www.kaggle.com/search?q=sales+dataset)
   - UCI Machine Learning Repository: [Sales Transactions Dataset](https://archive.ics.uci.edu/ml/datasets/sales_transactions_dataset_weekly)

2. Customer Segmentation Analysis:
   - Kaggle: [Customer dataset](https://www.kaggle.com/search?q=customer+dataset)
   - UCI Machine Learning Repository: [Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/Online+Retail)

3. Inventory Management Dashboard:
   - Kaggle: [Inventory dataset](https://www.kaggle.com/search?q=inventory+dataset)
   - Data.gov: [Inventory datasets](https://www.data.gov/)

4. Financial Analysis Dashboard:
   - Yahoo Finance API: [Yahoo Finance API](https://finance.yahoo.com/quote/GOOG/history?p=GOOG)
   - Quandl: [Financial datasets](https://www.quandl.com/)

5. Social Media Analytics Dashboard:
   - Twitter API: [Twitter API](https://developer.twitter.com/en/docs)
   - Facebook Graph API: [Facebook Graph API](https://developers.facebook.com/docs/graph-api/)

6. Website Analytics Dashboard:
   - Google Analytics API: [Google Analytics API](https://developers.google.com/analytics)
   - SimilarWeb API: [SimilarWeb API](https://www.similarweb.com/corp/developer/)

7. Supply Chain Analysis Dashboard:
   - Kaggle: [Supply chain dataset](https://www.kaggle.com/search?q=supply+chain+dataset)
   - Data.gov: [Supply chain datasets](https://www.data.gov/)

8. Healthcare Analytics Dashboard:
   - CDC Public Health Data: [CDC Public Health Data](https://www.cdc.gov/datastatistics/index.html)
   - HealthData.gov: [Healthcare datasets](https://healthdata.gov/)

9. Employee Performance Dashboard:
   - Kaggle: [Employee dataset](https://www.kaggle.com/search?q=employee+dataset)
   - Glassdoor API: [Glassdoor API](https://www.glassdoor.com/developer/index.htm)

10. Real-time Dashboard:
    - Real-time APIs: Various APIs provide real-time data, such as financial market APIs, weather APIs, etc.
    - Web scraping: Extract real-time data from websites using web scraping tools like BeautifulSoup or Scrapy.

All the best for your career ❤️

Breaking into Data Science doesn’t need to be complicated.

If you’re just starting out,

Here’s how to simplify your approach:

Avoid:
🚫 Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once.
🚫 Spending months on theoretical concepts without hands-on practice.
🚫 Overloading your resume with keywords instead of impactful projects.
🚫 Believing you need a Ph.D. to break into the field.

Instead:

✅ Start with Python or R—focus on mastering one language first.
✅ Learn how to work with structured data (Excel or SQL) - this is your bread and butter.
✅ Dive into a simple machine learning model (like linear regression) to understand the basics.
✅ Solve real-world problems with open datasets and share them in a portfolio.
✅ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests.

Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Like if you need similar content 😄👍

Hope this helps you 😊

#ai #datascience

61 steps to learn Machine Learning

How do you start AI and ML ?

Where do you go to learn these skills? What courses are the best?

There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.

What’s more important than how you start is why you start.

Start with why.

Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.

Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.

Got a why? Good. Time for some hard skills.

I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course

You can completed courses from (in order):

Treehouse / youtube( free) - Introduction to Python

Udacity - Deep Learning & AI Nanodegree

fast.ai - Part 1and Part 2

They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.

If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.

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All the best 👍👍

✅Here are 10 acronyms related to Data Science ✅