Essential Programming Languages to Learn Data Science 👇👇
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts 👇👇
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
Join @free4unow_backup for more free resources.
ENJOY LEARNING👍👍
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts 👇👇
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
Join @free4unow_backup for more free resources.
ENJOY LEARNING👍👍
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Call for papers on AI to AI Journey* conference journal has started!
Prize for the best scientific paper - 1 million roubles!
Selected papers will be published in the scientific journal Doklady Mathematics.
📖 The journal:
• Indexed in the largest bibliographic databases of scientific citations
• Accessible to an international audience and published in the world’s digital libraries
Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference.
Prize for the best article - 1 million roubles!
More detailed information can be found in the Selection Rules -> AI Journey
*AI Journey - a major online conference in the field of AI technologies
Prize for the best scientific paper - 1 million roubles!
Selected papers will be published in the scientific journal Doklady Mathematics.
📖 The journal:
• Indexed in the largest bibliographic databases of scientific citations
• Accessible to an international audience and published in the world’s digital libraries
Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference.
Prize for the best article - 1 million roubles!
More detailed information can be found in the Selection Rules -> AI Journey
*AI Journey - a major online conference in the field of AI technologies
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10 Public APIs you can use for your next project
🌍 http://restcountries.com - Country data API
🌱 http://trefle.io - Plants data API
🚀http://api.nasa.gov - Space-related API
🎵 http://developer.spotify.com - Music data API
📰 http://newsapi.org - Access news articles
🌅 http://sunrise-sunset.org/api - Sunrise and sunset times API
🐲 http://pokeapi.co - Pokémon data API
🎥 http://omdbapi.com - Movie database API
🐈 http://catfact.ninja - Cat facts API
🐶 http://thedogapi.com - Dog picture API
🌍 http://restcountries.com - Country data API
🌱 http://trefle.io - Plants data API
🚀http://api.nasa.gov - Space-related API
🎵 http://developer.spotify.com - Music data API
📰 http://newsapi.org - Access news articles
🌅 http://sunrise-sunset.org/api - Sunrise and sunset times API
🐲 http://pokeapi.co - Pokémon data API
🎥 http://omdbapi.com - Movie database API
🐈 http://catfact.ninja - Cat facts API
🐶 http://thedogapi.com - Dog picture API
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🔴 How to MASTER a programming language using ChatGPT: 📌
1. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
2. What are some commonly asked interview questions about [lang]?
3. What are the advanced topics to learn in [lang]? Explain them to me with code examples.
4. Give me some practice questions along with solutions for [concept] in [lang].
5. What are some common mistakes that people make in [lang]?
6. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
7. How can I optimize the performance of my code in [lang]?
8. What are some coding exercises or mini-projects I can do regularly to reinforce my understanding and application of [lang] concepts?
9. Are there any specific tools or frameworks that are commonly used in [lang]? How can I learn and utilize them effectively?
10. What are the debugging techniques and tools available in [lang] to help troubleshoot and fix code issues?
11. Are there any coding conventions or style guidelines that I should follow when writing code in [lang]?
12. How can I effectively collaborate with other developers in [lang] on a project?
13. What are some common data structures and algorithms that I should be familiar with in [lang]?
How to Create Resume using ChatGPT 👇👇
https://t.me/free4unow_backup/687
Master DSA 👇👇
https://t.me/dsabooks/156
1. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
2. What are some commonly asked interview questions about [lang]?
3. What are the advanced topics to learn in [lang]? Explain them to me with code examples.
4. Give me some practice questions along with solutions for [concept] in [lang].
5. What are some common mistakes that people make in [lang]?
6. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
7. How can I optimize the performance of my code in [lang]?
8. What are some coding exercises or mini-projects I can do regularly to reinforce my understanding and application of [lang] concepts?
9. Are there any specific tools or frameworks that are commonly used in [lang]? How can I learn and utilize them effectively?
10. What are the debugging techniques and tools available in [lang] to help troubleshoot and fix code issues?
11. Are there any coding conventions or style guidelines that I should follow when writing code in [lang]?
12. How can I effectively collaborate with other developers in [lang] on a project?
13. What are some common data structures and algorithms that I should be familiar with in [lang]?
How to Create Resume using ChatGPT 👇👇
https://t.me/free4unow_backup/687
Master DSA 👇👇
https://t.me/dsabooks/156
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