Python Basics for Data Science
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𝗛𝗼𝘄 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to break into Financial Data Analytics but don’t know where to start?
Here’s your ultimate step-by-step roadmap to landing a job in this high-demand field.
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/42aGUwb
🎯 🚀 Ready to Start?
Want to break into Financial Data Analytics but don’t know where to start?
Here’s your ultimate step-by-step roadmap to landing a job in this high-demand field.
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/42aGUwb
🎯 🚀 Ready to Start?
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🚨30 FREE Dataset Sources for Data Science Projects🔥
Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/
US Government Dataset: https://www.data.gov/
Open Government Data (OGD) Platform India: https://data.gov.in/
The World Bank Open Data: https://data.worldbank.org/
Data World: https://data.world/
BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics
The Humanitarian Data Exchange (HDX): https://data.humdata.org/
Data at World Health Organization (WHO): https://www.who.int/data
FBI’s Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/
AWS Open Data Registry: https://registry.opendata.aws/
FiveThirtyEight: https://data.fivethirtyeight.com/
IMDb Datasets: https://www.imdb.com/interfaces/
Kaggle: https://www.kaggle.com/datasets
UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php
Google Dataset Search: https://datasetsearch.research.google.com/
Nasdaq Data Link: https://data.nasdaq.com/
Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html
Reddit - Datasets: https://www.reddit.com/r/datasets/
Open Data Network by Socrata: https://www.opendatanetwork.com/
Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/
Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/
IEEE Data Port: https://ieee-dataport.org/
Wikipedia: Database: https://dumps.wikimedia.org/
BuzzFeed News: https://github.com/BuzzFeedNews/everything
Academic Torrents: https://academictorrents.com/
Yelp Open Dataset: https://www.yelp.com/dataset
The NLP Index by Quantum Stat: https://index.quantumstat.com/
Computer Vision Online: http://www.computervisiononline.com/dataset
Visual Data Discovery: https://www.visualdata.io/
Roboflow Public Datasets: https://public.roboflow.com/
Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets
Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/
US Government Dataset: https://www.data.gov/
Open Government Data (OGD) Platform India: https://data.gov.in/
The World Bank Open Data: https://data.worldbank.org/
Data World: https://data.world/
BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics
The Humanitarian Data Exchange (HDX): https://data.humdata.org/
Data at World Health Organization (WHO): https://www.who.int/data
FBI’s Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/
AWS Open Data Registry: https://registry.opendata.aws/
FiveThirtyEight: https://data.fivethirtyeight.com/
IMDb Datasets: https://www.imdb.com/interfaces/
Kaggle: https://www.kaggle.com/datasets
UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php
Google Dataset Search: https://datasetsearch.research.google.com/
Nasdaq Data Link: https://data.nasdaq.com/
Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html
Reddit - Datasets: https://www.reddit.com/r/datasets/
Open Data Network by Socrata: https://www.opendatanetwork.com/
Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/
Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/
IEEE Data Port: https://ieee-dataport.org/
Wikipedia: Database: https://dumps.wikimedia.org/
BuzzFeed News: https://github.com/BuzzFeedNews/everything
Academic Torrents: https://academictorrents.com/
Yelp Open Dataset: https://www.yelp.com/dataset
The NLP Index by Quantum Stat: https://index.quantumstat.com/
Computer Vision Online: http://www.computervisiononline.com/dataset
Visual Data Discovery: https://www.visualdata.io/
Roboflow Public Datasets: https://public.roboflow.com/
Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets
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6 Tips for Building a Robust Machine Learning Model
1. Understand the problem thoroughly before jumping into the model.
➝ Taking time to understand the problem helps build a solution aligned with business needs and goals.
2. Focus on feature engineering to improve accuracy.
➝ Well-engineered features make a big difference in model performance. Collaborating with data engineers on clean and well-structured data can simplify feature engineering.
3. Start simple, test assumptions, and iterate.
➝ Begin with straightforward models to test ideas quickly. Iteration and experimentation will lead to stronger results.
4. Keep track of versions for reproducibility.
➝ Documenting versions of data and code helps maintain consistency, making it easier to reproduce results.
5. Regularly validate your model with new data.
➝ Models should be updated and validated as new data becomes available to avoid performance degradation.
6. Always prioritize interpretability alongside accuracy.
➝ Building interpretable models helps stakeholders understand and trust your results, making insights more actionable.
Like if you need similar content 😄👍
1. Understand the problem thoroughly before jumping into the model.
➝ Taking time to understand the problem helps build a solution aligned with business needs and goals.
2. Focus on feature engineering to improve accuracy.
➝ Well-engineered features make a big difference in model performance. Collaborating with data engineers on clean and well-structured data can simplify feature engineering.
3. Start simple, test assumptions, and iterate.
➝ Begin with straightforward models to test ideas quickly. Iteration and experimentation will lead to stronger results.
4. Keep track of versions for reproducibility.
➝ Documenting versions of data and code helps maintain consistency, making it easier to reproduce results.
5. Regularly validate your model with new data.
➝ Models should be updated and validated as new data becomes available to avoid performance degradation.
6. Always prioritize interpretability alongside accuracy.
➝ Building interpretable models helps stakeholders understand and trust your results, making insights more actionable.
Like if you need similar content 😄👍
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Forwarded from Python Projects & Resources
𝗚𝗼𝗼𝗴𝗹𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
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Whether you’re a beginner or looking to sharpen your skills, these resources will help you stay ahead in the tech game.
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Top 5 data science projects for freshers
1. Predictive Analytics on a Dataset:
- Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.
2. Customer Segmentation:
- Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.
3. Sentiment Analysis on Social Media Data:
- Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.
4. Recommendation System:
- Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.
5. Fraud Detection:
- Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.
Free Datsets -> https://news.1rj.ru/str/DataPortfolio/2
These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.
Join @pythonspecialist for more data science projects
1. Predictive Analytics on a Dataset:
- Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.
2. Customer Segmentation:
- Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.
3. Sentiment Analysis on Social Media Data:
- Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.
4. Recommendation System:
- Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.
5. Fraud Detection:
- Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.
Free Datsets -> https://news.1rj.ru/str/DataPortfolio/2
These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.
Join @pythonspecialist for more data science projects
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Forwarded from Artificial Intelligence
𝟰 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
These free, Microsoft-backed courses are a game-changer!
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Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started:
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!
Please react 👍❤️ if you guys want me to share more of this content...
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!
Please react 👍❤️ if you guys want me to share more of this content...
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I just signed up for Hack the Future: A Gen AI Sprint Powered by Data—a nationwide hackathon where you'll tackle real-world challenges using Data and AI. It’s a golden opportunity to work with industry experts, participate in hands-on workshops, and win exciting prizes.
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𝗟𝗲𝗮𝗿𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗹𝗲𝘃𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗚𝗮𝗺𝗲!😍
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Infosys Springboard is offering a wide range of 100% free courses with certificates to help you upskill and boost your resume—at no cost.
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Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science
Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.
1. Basic python and statistics
Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset
2. Advanced Statistics
Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
3. Supervised Learning
a) Regression Problems
How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview
b) Classification problems
Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking
4. Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
5. Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Share with credits: https://news.1rj.ru/str/sqlproject
ENJOY LEARNING 👍👍
Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.
1. Basic python and statistics
Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset
2. Advanced Statistics
Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
3. Supervised Learning
a) Regression Problems
How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview
b) Classification problems
Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking
4. Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
5. Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Share with credits: https://news.1rj.ru/str/sqlproject
ENJOY LEARNING 👍👍
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𝟱 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗔𝗪𝗦, 𝗜𝗕𝗠, 𝗖𝗶𝘀𝗰𝗼, 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱. 😍
- Python
- Artificial Intelligence,
- Cybersecurity
- Cloud Computing, and
- Machine Learning
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/3E2wYNr
Enroll For FREE & Get Certified 🎓
- Python
- Artificial Intelligence,
- Cybersecurity
- Cloud Computing, and
- Machine Learning
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/3E2wYNr
Enroll For FREE & Get Certified 🎓
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Free Courses with Certificate
Web Development
Data Science & Machine Learning
Programming books
Python Free Courses
Data Analytics
Ethical Hacking & Cyber Security
English Speaking & Communication
Stock Marketing & Investment Banking
Excel
ChatGPT Hacks
SQL
Tableau & Power BI
Coding Projects
Data Science Projects
Jobs & Internship Opportunities
Coding Interviews
Udemy Free Courses with Certificate
Cryptocurrency & Bitcoin
Python Projects
Data Analyst Interview
Data Analyst Jobs
Python Interview
ChatGPT Hacks
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