🚀🔥 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.
In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.
👉 Apply now: https://go.readytensor.ai/cert-514-agentic-ai-certification
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.
In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.
👉 Apply now: https://go.readytensor.ai/cert-514-agentic-ai-certification
❤3
⌨️ An Amazing Cheatsheet for Tailwind CSS to master Tailwind in Minutes
❤4
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.
❤6
Future-Proof Skills for Data Analysts in 2025 & Beyond
1️⃣ AI-Powered Analytics 🤖 Leverage AI and AutoML tools like ChatGPT, DataRobot, and H2O.ai to automate insights and decision-making.
2️⃣ Generative AI for Data Analysis 🧠 Use AI for generating SQL queries, writing Python noscripts, and automating data storytelling.
3️⃣ Real-Time Data Processing ⚡ Learn streaming technologies like Apache Kafka and Apache Flink for real-time analytics.
4️⃣ DataOps & MLOps 🔄 Understand how to deploy and maintain machine learning models and analytical workflows in production environments.
5️⃣ Knowledge of Graph Databases 📊 Work with Neo4j and Amazon Neptune to analyze relationships in complex datasets.
6️⃣ Advanced Data Privacy & Ethics 🔐 Stay updated on GDPR, CCPA, and AI ethics to ensure responsible data handling.
7️⃣ No-Code & Low-Code Analytics 🛠️ Use platforms like Alteryx, Knime, and Google AutoML for rapid prototyping and automation.
8️⃣ API & Web Scraping Skills 🌍 Extract real-time data using APIs and web scraping tools like BeautifulSoup and Selenium.
9️⃣ Cross-Disciplinary Collaboration 🤝 Work with product managers, engineers, and business leaders to drive data-driven strategies.
🔟 Continuous Learning & Adaptability 🚀 Stay ahead by learning new technologies, attending conferences, and networking with industry experts.
Like for detailed explanation ❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
1️⃣ AI-Powered Analytics 🤖 Leverage AI and AutoML tools like ChatGPT, DataRobot, and H2O.ai to automate insights and decision-making.
2️⃣ Generative AI for Data Analysis 🧠 Use AI for generating SQL queries, writing Python noscripts, and automating data storytelling.
3️⃣ Real-Time Data Processing ⚡ Learn streaming technologies like Apache Kafka and Apache Flink for real-time analytics.
4️⃣ DataOps & MLOps 🔄 Understand how to deploy and maintain machine learning models and analytical workflows in production environments.
5️⃣ Knowledge of Graph Databases 📊 Work with Neo4j and Amazon Neptune to analyze relationships in complex datasets.
6️⃣ Advanced Data Privacy & Ethics 🔐 Stay updated on GDPR, CCPA, and AI ethics to ensure responsible data handling.
7️⃣ No-Code & Low-Code Analytics 🛠️ Use platforms like Alteryx, Knime, and Google AutoML for rapid prototyping and automation.
8️⃣ API & Web Scraping Skills 🌍 Extract real-time data using APIs and web scraping tools like BeautifulSoup and Selenium.
9️⃣ Cross-Disciplinary Collaboration 🤝 Work with product managers, engineers, and business leaders to drive data-driven strategies.
🔟 Continuous Learning & Adaptability 🚀 Stay ahead by learning new technologies, attending conferences, and networking with industry experts.
Like for detailed explanation ❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤2
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)
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Today let's understand the fascinating world of Data Science from start.
## What is Data Science?
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. In simpler terms, data science involves obtaining, processing, and analyzing data to gain insights for various purposes¹².
### The Data Science Lifecycle
The data science lifecycle refers to the various stages a data science project typically undergoes. While each project is unique, most follow a similar structure:
1. Data Collection and Storage:
- In this initial phase, data is collected from various sources such as databases, Excel files, text files, APIs, web scraping, or real-time data streams.
- The type and volume of data collected depend on the specific problem being addressed.
- Once collected, the data is stored in an appropriate format for further processing.
2. Data Preparation:
- Often considered the most time-consuming phase, data preparation involves cleaning and transforming raw data into a suitable format for analysis.
- Tasks include handling missing or inconsistent data, removing duplicates, normalization, and data type conversions.
- The goal is to create a clean, high-quality dataset that can yield accurate and reliable analytical results.
3. Exploration and Visualization:
- During this phase, data scientists explore the prepared data to understand its patterns, characteristics, and potential anomalies.
- Techniques like statistical analysis and data visualization are used to summarize the data's main features.
- Visualization methods help convey insights effectively.
4. Model Building and Machine Learning:
- This phase involves selecting appropriate algorithms and building predictive models.
- Machine learning techniques are applied to train models on historical data and make predictions.
- Common tasks include regression, classification, clustering, and recommendation systems.
5. Model Evaluation and Deployment:
- After building models, they are evaluated using metrics such as accuracy, precision, recall, and F1-score.
- Once satisfied with the model's performance, it can be deployed for real-world use.
- Deployment may involve integrating the model into an application or system.
### Why Data Science Matters
- Business Insights: Organizations use data science to gain insights into customer behavior, market trends, and operational efficiency. This informs strategic decisions and drives business growth.
- Healthcare and Medicine: Data science helps analyze patient data, predict disease outbreaks, and optimize treatment plans. It contributes to personalized medicine and drug discovery.
- Finance and Risk Management: Financial institutions use data science for fraud detection, credit scoring, and risk assessment. It enhances decision-making and minimizes financial risks.
- Social Sciences and Public Policy: Data science aids in understanding social phenomena, predicting election outcomes, and optimizing public services.
- Technology and Innovation: Data science fuels innovations in artificial intelligence, natural language processing, and recommendation systems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
## What is Data Science?
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. In simpler terms, data science involves obtaining, processing, and analyzing data to gain insights for various purposes¹².
### The Data Science Lifecycle
The data science lifecycle refers to the various stages a data science project typically undergoes. While each project is unique, most follow a similar structure:
1. Data Collection and Storage:
- In this initial phase, data is collected from various sources such as databases, Excel files, text files, APIs, web scraping, or real-time data streams.
- The type and volume of data collected depend on the specific problem being addressed.
- Once collected, the data is stored in an appropriate format for further processing.
2. Data Preparation:
- Often considered the most time-consuming phase, data preparation involves cleaning and transforming raw data into a suitable format for analysis.
- Tasks include handling missing or inconsistent data, removing duplicates, normalization, and data type conversions.
- The goal is to create a clean, high-quality dataset that can yield accurate and reliable analytical results.
3. Exploration and Visualization:
- During this phase, data scientists explore the prepared data to understand its patterns, characteristics, and potential anomalies.
- Techniques like statistical analysis and data visualization are used to summarize the data's main features.
- Visualization methods help convey insights effectively.
4. Model Building and Machine Learning:
- This phase involves selecting appropriate algorithms and building predictive models.
- Machine learning techniques are applied to train models on historical data and make predictions.
- Common tasks include regression, classification, clustering, and recommendation systems.
5. Model Evaluation and Deployment:
- After building models, they are evaluated using metrics such as accuracy, precision, recall, and F1-score.
- Once satisfied with the model's performance, it can be deployed for real-world use.
- Deployment may involve integrating the model into an application or system.
### Why Data Science Matters
- Business Insights: Organizations use data science to gain insights into customer behavior, market trends, and operational efficiency. This informs strategic decisions and drives business growth.
- Healthcare and Medicine: Data science helps analyze patient data, predict disease outbreaks, and optimize treatment plans. It contributes to personalized medicine and drug discovery.
- Finance and Risk Management: Financial institutions use data science for fraud detection, credit scoring, and risk assessment. It enhances decision-making and minimizes financial risks.
- Social Sciences and Public Policy: Data science aids in understanding social phenomena, predicting election outcomes, and optimizing public services.
- Technology and Innovation: Data science fuels innovations in artificial intelligence, natural language processing, and recommendation systems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
❤3👍1
140+ basic to advanced Python programs.pdf
4.4 MB
140+ Basic to Advance Python Programs 🧠
React ❤️ For More
React ❤️ For More
❤11
WhatsApp is no longer a platform just for chat.
It's an educational goldmine.
If you do, you’re sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.
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It's an educational goldmine.
If you do, you’re sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.
I have curated the list of best WhatsApp channels to learn coding & data science for FREE
Free Courses with Certificate
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❤5👏1
Forwarded from Data Analytics
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❤2