✅ Data Science Project Ideas
1️⃣ Beginner Friendly Projects
• Exploratory Data Analysis (EDA) on CSV datasets
• Student Marks Analysis
• COVID / Weather Data Analysis
• Simple Data Visualization Dashboard
• Basic Recommendation System (rule-based)
2️⃣ Python for Data Science
• Sales Data Analysis using Pandas
• Web Scraping + Analysis (BeautifulSoup)
• Data Cleaning Preprocessing Project
• Movie Rating Analysis
• Stock Price Analysis (historical data)
3️⃣ Machine Learning Projects
• House Price Prediction
• Spam Email Classifier
• Loan Approval Prediction
• Customer Churn Prediction
• Iris / Titanic Dataset Classification
4️⃣ Data Visualization Projects
• Interactive Dashboard using Matplotlib/Seaborn
• Sales Performance Dashboard
• Social Media Analytics Dashboard
• COVID Trends Visualization
• Country-wise GDP Analysis
5️⃣ NLP (Text Language) Projects
• Sentiment Analysis on Reviews
• Resume Screening System
• Fake News Detection
• Chatbot (Rule-based → ML-based)
• Topic Modeling on Articles
6️⃣ Advanced ML / AI Projects
• Recommendation System (Collaborative Filtering)
• Credit Card Fraud Detection
• Image Classification (CNN basics)
• Face Mask Detection
• Speech-to-Text Analysis
7️⃣ Data Engineering / Big Data
• ETL Pipeline using Python
• Data Warehouse Design (Star Schema)
• Log File Analysis
• API Data Ingestion Project
• Batch Processing with Large Datasets
8️⃣ Real-World / Portfolio Projects
• End-to-End Data Science Project
• Business Problem → Data → Model → Insights
• Kaggle Competition Project
• Open Dataset Case Study
• Automated Data Reporting Tool
1️⃣ Beginner Friendly Projects
• Exploratory Data Analysis (EDA) on CSV datasets
• Student Marks Analysis
• COVID / Weather Data Analysis
• Simple Data Visualization Dashboard
• Basic Recommendation System (rule-based)
2️⃣ Python for Data Science
• Sales Data Analysis using Pandas
• Web Scraping + Analysis (BeautifulSoup)
• Data Cleaning Preprocessing Project
• Movie Rating Analysis
• Stock Price Analysis (historical data)
3️⃣ Machine Learning Projects
• House Price Prediction
• Spam Email Classifier
• Loan Approval Prediction
• Customer Churn Prediction
• Iris / Titanic Dataset Classification
4️⃣ Data Visualization Projects
• Interactive Dashboard using Matplotlib/Seaborn
• Sales Performance Dashboard
• Social Media Analytics Dashboard
• COVID Trends Visualization
• Country-wise GDP Analysis
5️⃣ NLP (Text Language) Projects
• Sentiment Analysis on Reviews
• Resume Screening System
• Fake News Detection
• Chatbot (Rule-based → ML-based)
• Topic Modeling on Articles
6️⃣ Advanced ML / AI Projects
• Recommendation System (Collaborative Filtering)
• Credit Card Fraud Detection
• Image Classification (CNN basics)
• Face Mask Detection
• Speech-to-Text Analysis
7️⃣ Data Engineering / Big Data
• ETL Pipeline using Python
• Data Warehouse Design (Star Schema)
• Log File Analysis
• API Data Ingestion Project
• Batch Processing with Large Datasets
8️⃣ Real-World / Portfolio Projects
• End-to-End Data Science Project
• Business Problem → Data → Model → Insights
• Kaggle Competition Project
• Open Dataset Case Study
• Automated Data Reporting Tool
❤2🔥1
🔥 Trending Repository: cognee
📝 Description: Memory for AI Agents in 6 lines of code
🔗 Repository URL: https://github.com/topoteretes/cognee
🌐 Website: https://www.cognee.ai
📖 Readme: https://github.com/topoteretes/cognee#readme
📊 Statistics:
🌟 Stars: 11.7K stars
👀 Watchers: 59
🍴 Forks: 1.2K forks
💻 Programming Languages: Python - TypeScript - Shell - Dockerfile - CSS - Mako
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Memory for AI Agents in 6 lines of code
🔗 Repository URL: https://github.com/topoteretes/cognee
🌐 Website: https://www.cognee.ai
📖 Readme: https://github.com/topoteretes/cognee#readme
📊 Statistics:
🌟 Stars: 11.7K stars
👀 Watchers: 59
🍴 Forks: 1.2K forks
💻 Programming Languages: Python - TypeScript - Shell - Dockerfile - CSS - Mako
🏷️ Related Topics:
#open_source #ai #knowledge #neo4j #knowledge_graph #openai #help_wanted #graph_database #ai_agents #contributions_welcome #cognitive_architecture #good_first_issue #rag #good_first_pr #vector_database #graph_rag #ai_memory #cognitive_memory #graphrag #context_engineering
==================================
🧠 By: https://t.iss.one/DataScienceM
❤1
🔥 Trending Repository: fish-shell
📝 Description: The user-friendly command line shell.
🔗 Repository URL: https://github.com/fish-shell/fish-shell
🌐 Website: https://fishshell.com
📖 Readme: https://github.com/fish-shell/fish-shell#readme
📊 Statistics:
🌟 Stars: 32.3K stars
👀 Watchers: 279
🍴 Forks: 2.2K forks
💻 Programming Languages: Rust - Shell - Python - HTML - JavaScript - CMake
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: The user-friendly command line shell.
🔗 Repository URL: https://github.com/fish-shell/fish-shell
🌐 Website: https://fishshell.com
📖 Readme: https://github.com/fish-shell/fish-shell#readme
📊 Statistics:
🌟 Stars: 32.3K stars
👀 Watchers: 279
🍴 Forks: 2.2K forks
💻 Programming Languages: Rust - Shell - Python - HTML - JavaScript - CMake
🏷️ Related Topics:
#shell #rust #fish #terminal
==================================
🧠 By: https://t.iss.one/DataScienceM
❤1
🔥 Trending Repository: prompt-optimizer
📝 Description: 一款提示词优化器，助力于编写高质量的提示词
🔗 Repository URL: https://github.com/linshenkx/prompt-optimizer
🌐 Website: https://prompt.always200.com
📖 Readme: https://github.com/linshenkx/prompt-optimizer#readme
📊 Statistics:
🌟 Stars: 19.2K stars
👀 Watchers: 77
🍴 Forks: 2.4K forks
💻 Programming Languages: TypeScript - Vue - JavaScript - Shell - CSS - Dockerfile
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: 一款提示词优化器，助力于编写高质量的提示词
🔗 Repository URL: https://github.com/linshenkx/prompt-optimizer
🌐 Website: https://prompt.always200.com
📖 Readme: https://github.com/linshenkx/prompt-optimizer#readme
📊 Statistics:
🌟 Stars: 19.2K stars
👀 Watchers: 77
🍴 Forks: 2.4K forks
💻 Programming Languages: TypeScript - Vue - JavaScript - Shell - CSS - Dockerfile
🏷️ Related Topics:
#prompt #prompt_toolkit #prompt_tuning #llm #prompt_engineering #prompt_optimization
==================================
🧠 By: https://t.iss.one/DataScienceM
❤2
🔥 Trending Repository: anet
📝 Description: Simple Rust VPN Client / Server
🔗 Repository URL: https://github.com/ZeroTworu/anet
📖 Readme: https://github.com/ZeroTworu/anet#readme
📊 Statistics:
🌟 Stars: 268 stars
👀 Watchers: 15
🍴 Forks: 20 forks
💻 Programming Languages: Rust - Inno Setup - Shell - Makefile
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Simple Rust VPN Client / Server
🔗 Repository URL: https://github.com/ZeroTworu/anet
📖 Readme: https://github.com/ZeroTworu/anet#readme
📊 Statistics:
🌟 Stars: 268 stars
👀 Watchers: 15
🍴 Forks: 20 forks
💻 Programming Languages: Rust - Inno Setup - Shell - Makefile
🏷️ Related Topics:
#rust #vpn
==================================
🧠 By: https://t.iss.one/DataScienceM
❤2
🔥 Trending Repository: data-engineer-handbook
📝 Description: This is a repo with links to everything you'd ever want to learn about data engineering
🔗 Repository URL: https://github.com/DataExpert-io/data-engineer-handbook
📖 Readme: https://github.com/DataExpert-io/data-engineer-handbook#readme
📊 Statistics:
🌟 Stars: 39.7K stars
👀 Watchers: 466
🍴 Forks: 7.6K forks
💻 Programming Languages: Jupyter Notebook - Python - Makefile - Dockerfile - Shell
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: This is a repo with links to everything you'd ever want to learn about data engineering
🔗 Repository URL: https://github.com/DataExpert-io/data-engineer-handbook
📖 Readme: https://github.com/DataExpert-io/data-engineer-handbook#readme
📊 Statistics:
🌟 Stars: 39.7K stars
👀 Watchers: 466
🍴 Forks: 7.6K forks
💻 Programming Languages: Jupyter Notebook - Python - Makefile - Dockerfile - Shell
🏷️ Related Topics:
#data #awesome #sql #bigdata #dataengineering #apachespark
==================================
🧠 By: https://t.iss.one/DataScienceM
❤1
Data Science Interview Prep Guide
1️⃣ Core Data Science Concepts
• What is Data Science vs Data Analytics vs ML
• Descriptive, diagnostic, predictive, prescriptive analytics
• Structured vs unstructured data
• Data-driven decision making
• Business problem framing
2️⃣ Statistics Probability (Non-Negotiable)
• Mean, median, variance, standard deviation
• Probability distributions (normal, binomial, Poisson)
• Hypothesis testing p-values
• Confidence intervals
• Correlation vs causation
• Sampling bias
3️⃣ Data Cleaning EDA
• Handling missing values outliers
• Data normalization scaling
• Feature engineering
• Exploratory data analysis (EDA)
• Data leakage detection
• Data quality validation
4️⃣ Python SQL for Data Science
• Python (NumPy, Pandas)
• Data manipulation transformations
• Vectorization performance optimization
• SQL joins, CTEs, window functions
• Writing business-ready queries
5️⃣ Machine Learning Essentials
• Supervised vs unsupervised learning
• Regression vs classification
• Model selection baseline models
• Overfitting, underfitting
• Bias–variance tradeoff
• Hyperparameter tuning
6️⃣ Model Evaluation Metrics
• Accuracy, precision, recall, F1
• ROC AUC
• Confusion matrix
• RMSE, MAE, log loss
• Metrics for imbalanced data
• Linking ML metrics to business KPIs
7️⃣ Real-World Deployment Knowledge
• Feature stores
• Model deployment (batch vs real-time)
• Model monitoring drift
• Experiment tracking
• Data model versioning
• Model explainability (business-friendly)
8️⃣ Must-Have Projects
• Customer churn prediction
• Fraud detection
• Sales or demand forecasting
• Recommendation system
• End-to-end ML pipeline
• Business-focused case study
9️⃣ Common Interview Questions
• Walk me through an end-to-end DS project
• How do you choose evaluation metrics?
• How do you handle imbalanced data?
• How do you explain a model to leadership?
• How do you improve a failing model?
🔟 Pro Tips
✔️ Always connect answers to business impact
✔️ Explain why, not just how
✔️ Be clear about trade-offs
✔️ Discuss failures learnings
✔️ Show structured thinking
https://t.iss.one/DataScienceN
1️⃣ Core Data Science Concepts
• What is Data Science vs Data Analytics vs ML
• Descriptive, diagnostic, predictive, prescriptive analytics
• Structured vs unstructured data
• Data-driven decision making
• Business problem framing
2️⃣ Statistics Probability (Non-Negotiable)
• Mean, median, variance, standard deviation
• Probability distributions (normal, binomial, Poisson)
• Hypothesis testing p-values
• Confidence intervals
• Correlation vs causation
• Sampling bias
3️⃣ Data Cleaning EDA
• Handling missing values outliers
• Data normalization scaling
• Feature engineering
• Exploratory data analysis (EDA)
• Data leakage detection
• Data quality validation
4️⃣ Python SQL for Data Science
• Python (NumPy, Pandas)
• Data manipulation transformations
• Vectorization performance optimization
• SQL joins, CTEs, window functions
• Writing business-ready queries
5️⃣ Machine Learning Essentials
• Supervised vs unsupervised learning
• Regression vs classification
• Model selection baseline models
• Overfitting, underfitting
• Bias–variance tradeoff
• Hyperparameter tuning
6️⃣ Model Evaluation Metrics
• Accuracy, precision, recall, F1
• ROC AUC
• Confusion matrix
• RMSE, MAE, log loss
• Metrics for imbalanced data
• Linking ML metrics to business KPIs
7️⃣ Real-World Deployment Knowledge
• Feature stores
• Model deployment (batch vs real-time)
• Model monitoring drift
• Experiment tracking
• Data model versioning
• Model explainability (business-friendly)
8️⃣ Must-Have Projects
• Customer churn prediction
• Fraud detection
• Sales or demand forecasting
• Recommendation system
• End-to-end ML pipeline
• Business-focused case study
9️⃣ Common Interview Questions
• Walk me through an end-to-end DS project
• How do you choose evaluation metrics?
• How do you handle imbalanced data?
• How do you explain a model to leadership?
• How do you improve a failing model?
🔟 Pro Tips
✔️ Always connect answers to business impact
✔️ Explain why, not just how
✔️ Be clear about trade-offs
✔️ Discuss failures learnings
✔️ Show structured thinking
https://t.iss.one/DataScienceN
❤5
Data Science Jupyter Notebooks
Data Science Interview Prep Guide 1️⃣ Core Data Science Concepts • What is Data Science vs Data Analytics vs ML • Descriptive, diagnostic, predictive, prescriptive analytics • Structured vs unstructured data • Data-driven decision making • Business problem…
If you’re prepping for a data science role, this guide has EVERYTHING you need. Check it out! 💡
❤3
🔥 Trending Repository: shannon
📝 Description: Fully autonomous AI hacker to find actual exploits in your web apps. Shannon has achieved a 96.15% success rate on the hint-free, source-aware XBOW Benchmark.
🔗 Repository URL: https://github.com/KeygraphHQ/shannon
🌐 Website: https://keygraph.io/
📖 Readme: https://github.com/KeygraphHQ/shannon#readme
📊 Statistics:
🌟 Stars: 7.9K stars
👀 Watchers: 63
🍴 Forks: 1.1K forks
💻 Programming Languages: TypeScript - JavaScript - Shell - Dockerfile
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Fully autonomous AI hacker to find actual exploits in your web apps. Shannon has achieved a 96.15% success rate on the hint-free, source-aware XBOW Benchmark.
🔗 Repository URL: https://github.com/KeygraphHQ/shannon
🌐 Website: https://keygraph.io/
📖 Readme: https://github.com/KeygraphHQ/shannon#readme
📊 Statistics:
🌟 Stars: 7.9K stars
👀 Watchers: 63
🍴 Forks: 1.1K forks
💻 Programming Languages: TypeScript - JavaScript - Shell - Dockerfile
🏷️ Related Topics:
#security_audit #penetration_testing #pentesting #security_automation #security_tools
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: litebox
📝 Description: A security-focused library OS supporting kernel- and user-mode execution
🔗 Repository URL: https://github.com/microsoft/litebox
📖 Readme: https://github.com/microsoft/litebox#readme
📊 Statistics:
🌟 Stars: 914 stars
👀 Watchers: 11
🍴 Forks: 40 forks
💻 Programming Languages: Rust - C - JavaScript - CSS - Assembly - Python
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: A security-focused library OS supporting kernel- and user-mode execution
🔗 Repository URL: https://github.com/microsoft/litebox
📖 Readme: https://github.com/microsoft/litebox#readme
📊 Statistics:
🌟 Stars: 914 stars
👀 Watchers: 11
🍴 Forks: 40 forks
💻 Programming Languages: Rust - C - JavaScript - CSS - Assembly - Python
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: heretic
📝 Description: Fully automatic censorship removal for language models
🔗 Repository URL: https://github.com/p-e-w/heretic
📖 Readme: https://github.com/p-e-w/heretic#readme
📊 Statistics:
🌟 Stars: 4.5K stars
👀 Watchers: 27
🍴 Forks: 441 forks
💻 Programming Languages: Python
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Fully automatic censorship removal for language models
🔗 Repository URL: https://github.com/p-e-w/heretic
📖 Readme: https://github.com/p-e-w/heretic#readme
📊 Statistics:
🌟 Stars: 4.5K stars
👀 Watchers: 27
🍴 Forks: 441 forks
💻 Programming Languages: Python
🏷️ Related Topics:
#transformer #llm #abliteration
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: MiniCPM-o
📝 Description: A Gemini 2.5 Flash Level MLLM for Vision, Speech, and Full-Duplex Multimodal Live Streaming on Your Phone
🔗 Repository URL: https://github.com/OpenBMB/MiniCPM-o
📖 Readme: https://github.com/OpenBMB/MiniCPM-o#readme
📊 Statistics:
🌟 Stars: 23.1K stars
👀 Watchers: 156
🍴 Forks: 1.8K forks
💻 Programming Languages: Python - Vue - JavaScript - Shell - Less - CSS
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: A Gemini 2.5 Flash Level MLLM for Vision, Speech, and Full-Duplex Multimodal Live Streaming on Your Phone
🔗 Repository URL: https://github.com/OpenBMB/MiniCPM-o
📖 Readme: https://github.com/OpenBMB/MiniCPM-o#readme
📊 Statistics:
🌟 Stars: 23.1K stars
👀 Watchers: 156
🍴 Forks: 1.8K forks
💻 Programming Languages: Python - Vue - JavaScript - Shell - Less - CSS
🏷️ Related Topics:
#multi_modal #minicpm #minicpm_v
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: escrcpy
📝 Description: 📱 Display and control your Android device graphically with scrcpy.
🔗 Repository URL: https://github.com/viarotel-org/escrcpy
🌐 Website: https://viarotel.eu.org/
📖 Readme: https://github.com/viarotel-org/escrcpy#readme
📊 Statistics:
🌟 Stars: 7.7K stars
👀 Watchers: 48
🍴 Forks: 563 forks
💻 Programming Languages: JavaScript - Vue - TypeScript - Roff - CSS - VBScript
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: 📱 Display and control your Android device graphically with scrcpy.
🔗 Repository URL: https://github.com/viarotel-org/escrcpy
🌐 Website: https://viarotel.eu.org/
📖 Readme: https://github.com/viarotel-org/escrcpy#readme
📊 Statistics:
🌟 Stars: 7.7K stars
👀 Watchers: 48
🍴 Forks: 563 forks
💻 Programming Languages: JavaScript - Vue - TypeScript - Roff - CSS - VBScript
🏷️ Related Topics:
#android #windows #macos #linux #screenshots #gui #recording #screensharing #mirroring #hacktoberfest #scrcpy #scrcpy_engine #gnirehtet #genymobile #scrcpy_gui #hacktoberfest2025 #hacktoberfest2026
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: awesome-claude-skills
📝 Description: A curated list of awesome Claude Skills, resources, and tools for customizing Claude AI workflows
🔗 Repository URL: https://github.com/ComposioHQ/awesome-claude-skills
📖 Readme: https://github.com/ComposioHQ/awesome-claude-skills#readme
📊 Statistics:
🌟 Stars: 31.5K stars
👀 Watchers: 244
🍴 Forks: 3K forks
💻 Programming Languages: Python - JavaScript - Shell
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: A curated list of awesome Claude Skills, resources, and tools for customizing Claude AI workflows
🔗 Repository URL: https://github.com/ComposioHQ/awesome-claude-skills
📖 Readme: https://github.com/ComposioHQ/awesome-claude-skills#readme
📊 Statistics:
🌟 Stars: 31.5K stars
👀 Watchers: 244
🍴 Forks: 3K forks
💻 Programming Languages: Python - JavaScript - Shell
🏷️ Related Topics:
#automation #skill #mcp #saas #cursor #codex #workflow_automation #ai_agents #claude #rube #gemini_cli #composio #antigravity #agent_skills #claude_code
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: gitbutler
📝 Description: The GitButler version control client, backed by Git, powered by Tauri/Rust/Svelte
🔗 Repository URL: https://github.com/gitbutlerapp/gitbutler
🌐 Website: https://gitbutler.com
📖 Readme: https://github.com/gitbutlerapp/gitbutler#readme
📊 Statistics:
🌟 Stars: 17.7K stars
👀 Watchers: 47
🍴 Forks: 768 forks
💻 Programming Languages: Rust - Svelte - TypeScript - Shell - CSS - JavaScript
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: The GitButler version control client, backed by Git, powered by Tauri/Rust/Svelte
🔗 Repository URL: https://github.com/gitbutlerapp/gitbutler
🌐 Website: https://gitbutler.com
📖 Readme: https://github.com/gitbutlerapp/gitbutler#readme
📊 Statistics:
🌟 Stars: 17.7K stars
👀 Watchers: 47
🍴 Forks: 768 forks
💻 Programming Languages: Rust - Svelte - TypeScript - Shell - CSS - JavaScript
🏷️ Related Topics:
#github #git #tauri
==================================
🧠 By: https://t.iss.one/DataScienceM
One day or Day one. You decide.
Data Science edition.
𝗢𝗻𝗲 𝗗𝗮𝘆 : I will learn SQL.
𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my projects for my portfolio.
𝗗𝗮𝘆 𝗢𝗻𝗲: Look on Kaggle for a dataset to work on.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will master statistics.
𝗗𝗮𝘆 𝗢𝗻𝗲: Start the free Khan Academy Statistics and Probability course.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn to tell stories with data.
𝗗𝗮𝘆 𝗢𝗻𝗲: Install Tableau Public and create my first chart.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will become a Data Scientist.
𝗗𝗮𝘆 𝗢𝗻𝗲: Update my resume and apply to some Data Science job postings.
https://t.iss.one/DataScienceN
Data Science edition.
𝗢𝗻𝗲 𝗗𝗮𝘆 : I will learn SQL.
𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my projects for my portfolio.
𝗗𝗮𝘆 𝗢𝗻𝗲: Look on Kaggle for a dataset to work on.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will master statistics.
𝗗𝗮𝘆 𝗢𝗻𝗲: Start the free Khan Academy Statistics and Probability course.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn to tell stories with data.
𝗗𝗮𝘆 𝗢𝗻𝗲: Install Tableau Public and create my first chart.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will become a Data Scientist.
𝗗𝗮𝘆 𝗢𝗻𝗲: Update my resume and apply to some Data Science job postings.
https://t.iss.one/DataScienceN
❤6
Data Science Jupyter Notebooks
One day or Day one. You decide. Data Science edition. 𝗢𝗻𝗲 𝗗𝗮𝘆 : I will learn SQL. 𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench. 𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my projects for my portfolio. 𝗗𝗮𝘆 𝗢𝗻𝗲: Look on Kaggle for a dataset to work on. 𝗢𝗻𝗲 𝗗𝗮𝘆: I will master statistics.…
Don’t wait for the perfect moment. Start today
Install that tool, pick that dataset, take that course. Every big goal begins with Day One 💪
Install that tool, pick that dataset, take that course. Every big goal begins with Day One 💪
❤4
Forwarded from Learn Python Hub
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0
✅ https://t.iss.one/Codeprogrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
Here is a powerful 𝗜𝗡𝗧𝗘𝗥𝗩𝗜𝗘𝗪 𝗧𝗜𝗣 to help you land a job!
Most people who are skilled enough would be able to clear technical rounds with ease.
But when it comes to 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹/𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗳𝗶𝘁 rounds, some folks may falter and lose the potential offer.
Many companies schedule a behavioral round with a top-level manager in the organization to understand the culture fit (except for freshers).
One needs to clear this round to reach the salary negotiation round.
Here are some tips to clear such rounds:
1️⃣ Once the HR schedules the interview, try to find the LinkedIn profile of the interviewer using the name in their email ID.
2️⃣ Learn more about his/her past experiences and try to strike up a conversation on that during the interview.
3️⃣ This shows that you have done good research and also helps strike a personal connection.
4️⃣ Also, this is the round not just to evaluate if you're a fit for the company, but also to assess if the company is a right fit for you.
5️⃣ Hence, feel free to ask many questions about your role and company to get a clear understanding before taking the offer. This shows that you really care about the role you're getting into.
💡 𝗕𝗼𝗻𝘂𝘀 𝘁𝗶𝗽 - Be polite yet assertive in such interviews. It impresses a lot of senior folks.
https://t.iss.one/DataScienceN
Most people who are skilled enough would be able to clear technical rounds with ease.
But when it comes to 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹/𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗳𝗶𝘁 rounds, some folks may falter and lose the potential offer.
Many companies schedule a behavioral round with a top-level manager in the organization to understand the culture fit (except for freshers).
One needs to clear this round to reach the salary negotiation round.
Here are some tips to clear such rounds:
1️⃣ Once the HR schedules the interview, try to find the LinkedIn profile of the interviewer using the name in their email ID.
2️⃣ Learn more about his/her past experiences and try to strike up a conversation on that during the interview.
3️⃣ This shows that you have done good research and also helps strike a personal connection.
4️⃣ Also, this is the round not just to evaluate if you're a fit for the company, but also to assess if the company is a right fit for you.
5️⃣ Hence, feel free to ask many questions about your role and company to get a clear understanding before taking the offer. This shows that you really care about the role you're getting into.
💡 𝗕𝗼𝗻𝘂𝘀 𝘁𝗶𝗽 - Be polite yet assertive in such interviews. It impresses a lot of senior folks.
https://t.iss.one/DataScienceN
❤5
🔥 Trending Repository: monty
📝 Description: A minimal, secure Python interpreter written in Rust for use by AI
🔗 Repository URL: https://github.com/pydantic/monty
📖 Readme: https://github.com/pydantic/monty#readme
📊 Statistics:
🌟 Stars: 2.2K stars
👀 Watchers: 17
🍴 Forks: 55 forks
💻 Programming Languages: Rust - Python - TypeScript
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: A minimal, secure Python interpreter written in Rust for use by AI
🔗 Repository URL: https://github.com/pydantic/monty
📖 Readme: https://github.com/pydantic/monty#readme
📊 Statistics:
🌟 Stars: 2.2K stars
👀 Watchers: 17
🍴 Forks: 55 forks
💻 Programming Languages: Rust - Python - TypeScript
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
❤1
🔥 Trending Repository: addons
📝 Description: ➕ Docker add-ons for Home Assistant
🔗 Repository URL: https://github.com/home-assistant/addons
🌐 Website: https://home-assistant.io/hassio/
📖 Readme: https://github.com/home-assistant/addons#readme
📊 Statistics:
🌟 Stars: 1.9K stars
👀 Watchers: 73
🍴 Forks: 1.8K forks
💻 Programming Languages: Shell - Dockerfile - Groovy - HTML - Python - C - CMake
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: ➕ Docker add-ons for Home Assistant
🔗 Repository URL: https://github.com/home-assistant/addons
🌐 Website: https://home-assistant.io/hassio/
📖 Readme: https://github.com/home-assistant/addons#readme
📊 Statistics:
🌟 Stars: 1.9K stars
👀 Watchers: 73
🍴 Forks: 1.8K forks
💻 Programming Languages: Shell - Dockerfile - Groovy - HTML - Python - C - CMake
🏷️ Related Topics:
#docker #iot #automation #home #hacktoberfest
==================================
🧠 By: https://t.iss.one/DataScienceM
❤1