๐ฉโ๐ซ๐งโ๐ซ PROGRAMMING LANGUAGES YOU SHOULD LEARN TO BECOME.
โ๏ธ[ Web Developer]
โ๏ธ[ Game Developer]
โ๏ธ[ Data Analysis]
โ๏ธ[ Desktop Developer]
โ๏ธ[ Embedded System Program]
โ๏ธ[Mobile Apps Development]
โ๏ธ[ Web Developer]
PHP, C#, JS, JAVA, Python, Rubyโ๏ธ[ Game Developer]
Java, C++, Python, JS, Ruby, C, C#โ๏ธ[ Data Analysis]
R, Matlab, Java, Pythonโ๏ธ[ Desktop Developer]
Java, C#, C++, Pythonโ๏ธ[ Embedded System Program]
C, Python, C++ โ๏ธ[Mobile Apps Development]
Kotlin, Dart, Objective-C, Java, Python, JS, Swift, C#โค1
๐๐ฅ ๐๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ๐ป ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ ๐๐๐ถ๐น๐ฑ๐ฒ๐ฟ โ ๐๐ฟ๐ฒ๐ฒ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ
Master the most in-demand AI skill in todayโs job market: building autonomous AI systems.
In Ready Tensorโs free, project-first program, youโll create three portfolio-ready projects using ๐๐ฎ๐ป๐ด๐๐ต๐ฎ๐ถ๐ป, ๐๐ฎ๐ป๐ด๐๐ฟ๐ฎ๐ฝ๐ต, and vector databases โ and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
๐๐ฟ๐ฒ๐ฒ. ๐ฆ๐ฒ๐น๐ณ-๐ฝ๐ฎ๐ฐ๐ฒ๐ฑ. ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ-๐ฐ๐ต๐ฎ๐ป๐ด๐ถ๐ป๐ด.
๐ Apply now: https://go.readytensor.ai/cert-514-agentic-ai-certification
Master the most in-demand AI skill in todayโs job market: building autonomous AI systems.
In Ready Tensorโs free, project-first program, youโll create three portfolio-ready projects using ๐๐ฎ๐ป๐ด๐๐ต๐ฎ๐ถ๐ป, ๐๐ฎ๐ป๐ด๐๐ฟ๐ฎ๐ฝ๐ต, and vector databases โ and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
๐๐ฟ๐ฒ๐ฒ. ๐ฆ๐ฒ๐น๐ณ-๐ฝ๐ฎ๐ฐ๐ฒ๐ฑ. ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ-๐ฐ๐ต๐ฎ๐ป๐ด๐ถ๐ป๐ด.
๐ Apply now: https://go.readytensor.ai/cert-514-agentic-ai-certification
www.readytensor.ai
Agentic AI Developer Certification Program by Ready Tensor
Learn to build chatbots, AI assistants, and multi-agent systems with Ready Tensor's free, self-paced, and beginner-friendly Agentic AI Developer Certification. View the full program guide and how to get certified.
โค2
Data science is a multidisciplinary field that combines techniques from statistics, computer science, and domain-specific knowledge to extract insights and knowledge from data. Here are some essential concepts in data science:
1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.
2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.
3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.
4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.
5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.
7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.
8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.
9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.
10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.
These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.
Join for more: https://t.me/datasciencefun
ENJOY LEARNING ๐๐
1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.
2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.
3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.
4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.
5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.
7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.
8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.
9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.
10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.
These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.
Join for more: https://t.me/datasciencefun
ENJOY LEARNING ๐๐
โค3
๐๐ฅ ๐๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ๐ป ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ ๐๐๐ถ๐น๐ฑ๐ฒ๐ฟ โ ๐๐ฟ๐ฒ๐ฒ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ
Master the most in-demand AI skill in todayโs job market: building autonomous AI systems.
In Ready Tensorโs free, project-first program, youโll create three portfolio-ready projects using ๐๐ฎ๐ป๐ด๐๐ต๐ฎ๐ถ๐ป, ๐๐ฎ๐ป๐ด๐๐ฟ๐ฎ๐ฝ๐ต, and vector databases โ and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
๐๐ฟ๐ฒ๐ฒ. ๐ฆ๐ฒ๐น๐ณ-๐ฝ๐ฎ๐ฐ๐ฒ๐ฑ. ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ-๐ฐ๐ต๐ฎ๐ป๐ด๐ถ๐ป๐ด.
๐ Apply now: https://go.readytensor.ai/cert-514-agentic-ai-certification
Master the most in-demand AI skill in todayโs job market: building autonomous AI systems.
In Ready Tensorโs free, project-first program, youโll create three portfolio-ready projects using ๐๐ฎ๐ป๐ด๐๐ต๐ฎ๐ถ๐ป, ๐๐ฎ๐ป๐ด๐๐ฟ๐ฎ๐ฝ๐ต, and vector databases โ and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
๐๐ฟ๐ฒ๐ฒ. ๐ฆ๐ฒ๐น๐ณ-๐ฝ๐ฎ๐ฐ๐ฒ๐ฑ. ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ-๐ฐ๐ต๐ฎ๐ป๐ด๐ถ๐ป๐ด.
๐ Apply now: https://go.readytensor.ai/cert-514-agentic-ai-certification
โค3
โจ๏ธ An Amazing Cheatsheet for Tailwind CSS to master Tailwind in Minutes
โค3
If I were to start my Machine Learning career from scratch (as an engineer), I'd focus here (no specific order):
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
โค5
Future-Proof Skills for Data Analysts in 2025 & Beyond
1๏ธโฃ AI-Powered Analytics ๐ค Leverage AI and AutoML tools like ChatGPT, DataRobot, and H2O.ai to automate insights and decision-making.
2๏ธโฃ Generative AI for Data Analysis ๐ง Use AI for generating SQL queries, writing Python scripts, and automating data storytelling.
3๏ธโฃ Real-Time Data Processing โก Learn streaming technologies like Apache Kafka and Apache Flink for real-time analytics.
4๏ธโฃ DataOps & MLOps ๐ Understand how to deploy and maintain machine learning models and analytical workflows in production environments.
5๏ธโฃ Knowledge of Graph Databases ๐ Work with Neo4j and Amazon Neptune to analyze relationships in complex datasets.
6๏ธโฃ Advanced Data Privacy & Ethics ๐ Stay updated on GDPR, CCPA, and AI ethics to ensure responsible data handling.
7๏ธโฃ No-Code & Low-Code Analytics ๐ ๏ธ Use platforms like Alteryx, Knime, and Google AutoML for rapid prototyping and automation.
8๏ธโฃ API & Web Scraping Skills ๐ Extract real-time data using APIs and web scraping tools like BeautifulSoup and Selenium.
9๏ธโฃ Cross-Disciplinary Collaboration ๐ค Work with product managers, engineers, and business leaders to drive data-driven strategies.
๐ Continuous Learning & Adaptability ๐ Stay ahead by learning new technologies, attending conferences, and networking with industry experts.
Like for detailed explanation โค๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
1๏ธโฃ AI-Powered Analytics ๐ค Leverage AI and AutoML tools like ChatGPT, DataRobot, and H2O.ai to automate insights and decision-making.
2๏ธโฃ Generative AI for Data Analysis ๐ง Use AI for generating SQL queries, writing Python scripts, and automating data storytelling.
3๏ธโฃ Real-Time Data Processing โก Learn streaming technologies like Apache Kafka and Apache Flink for real-time analytics.
4๏ธโฃ DataOps & MLOps ๐ Understand how to deploy and maintain machine learning models and analytical workflows in production environments.
5๏ธโฃ Knowledge of Graph Databases ๐ Work with Neo4j and Amazon Neptune to analyze relationships in complex datasets.
6๏ธโฃ Advanced Data Privacy & Ethics ๐ Stay updated on GDPR, CCPA, and AI ethics to ensure responsible data handling.
7๏ธโฃ No-Code & Low-Code Analytics ๐ ๏ธ Use platforms like Alteryx, Knime, and Google AutoML for rapid prototyping and automation.
8๏ธโฃ API & Web Scraping Skills ๐ Extract real-time data using APIs and web scraping tools like BeautifulSoup and Selenium.
9๏ธโฃ Cross-Disciplinary Collaboration ๐ค Work with product managers, engineers, and business leaders to drive data-driven strategies.
๐ Continuous Learning & Adaptability ๐ Stay ahead by learning new technologies, attending conferences, and networking with industry experts.
Like for detailed explanation โค๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
โค1
Top 10 Websites for Data Science
1. Flowing Data (http://flowingdata.com)
2. Analytics Vidhya (http://www.analyticsvidhya.com)
3. R-Bloggers (http://www.r-bloggers.com)
4. Edwin Chen (http://blog.echen.me)
5. Hunch (http://hunch.net)
6. KDNuggets (http://www.kdnuggets.com)
7. Data Science Central (http://www.datasciencecentral.com)
8. Kaggle Competitions (https://www.kaggle.com/competitions)
9. Simply Statistics (http://simplystatistics.org)
10. FastML (http://fastml.com)
1. Flowing Data (http://flowingdata.com)
2. Analytics Vidhya (http://www.analyticsvidhya.com)
3. R-Bloggers (http://www.r-bloggers.com)
4. Edwin Chen (http://blog.echen.me)
5. Hunch (http://hunch.net)
6. KDNuggets (http://www.kdnuggets.com)
7. Data Science Central (http://www.datasciencecentral.com)
8. Kaggle Competitions (https://www.kaggle.com/competitions)
9. Simply Statistics (http://simplystatistics.org)
10. FastML (http://fastml.com)
โค4