Coding Project Ideas with AI ๐๐
1. Sentiment Analysis Tool: Develop a tool that uses AI to analyze the sentiment of text data, such as social media posts, customer reviews, or news articles. The tool could classify the sentiment as positive, negative, or neutral.
2. Image Recognition App: Create an app that uses AI image recognition algorithms to identify objects, scenes, or people in images. This could be useful for applications like automatic photo tagging or security surveillance.
3. Chatbot Development: Build a chatbot using AI natural language processing techniques to interact with users and provide information or assistance on a specific topic. You could integrate the chatbot into a website or messaging platform.
4. Recommendation System: Develop a recommendation system that uses AI algorithms to suggest products, movies, music, or other items based on user preferences and behavior. This could enhance the user experience on e-commerce platforms or streaming services.
5. Fraud Detection System: Create a fraud detection system that uses AI to analyze patterns and anomalies in financial transactions data. The system could help identify potentially fraudulent activities and prevent financial losses.
6. Health Monitoring App: Build an app that uses AI to monitor health data, such as heart rate, sleep patterns, or activity levels, and provide personalized recommendations for improving health and wellness.
7. Language Translation Tool: Develop a language translation tool that uses AI machine translation algorithms to translate text between different languages accurately and efficiently.
8. Autonomous Driving System: Work on a project to develop an autonomous driving system that uses AI computer vision and sensor data processing to navigate vehicles safely and efficiently on roads.
9. Personalized Content Generator: Create a tool that uses AI natural language generation techniques to generate personalized content, such as articles, emails, or marketing messages tailored to individual preferences.
10. Music Recommendation Engine: Build a music recommendation engine that uses AI algorithms to analyze music preferences and suggest playlists or songs based on user tastes and listening habits.
Join for more: https://t.iss.one/Programming_experts
ENJOY LEARNING ๐๐
1. Sentiment Analysis Tool: Develop a tool that uses AI to analyze the sentiment of text data, such as social media posts, customer reviews, or news articles. The tool could classify the sentiment as positive, negative, or neutral.
2. Image Recognition App: Create an app that uses AI image recognition algorithms to identify objects, scenes, or people in images. This could be useful for applications like automatic photo tagging or security surveillance.
3. Chatbot Development: Build a chatbot using AI natural language processing techniques to interact with users and provide information or assistance on a specific topic. You could integrate the chatbot into a website or messaging platform.
4. Recommendation System: Develop a recommendation system that uses AI algorithms to suggest products, movies, music, or other items based on user preferences and behavior. This could enhance the user experience on e-commerce platforms or streaming services.
5. Fraud Detection System: Create a fraud detection system that uses AI to analyze patterns and anomalies in financial transactions data. The system could help identify potentially fraudulent activities and prevent financial losses.
6. Health Monitoring App: Build an app that uses AI to monitor health data, such as heart rate, sleep patterns, or activity levels, and provide personalized recommendations for improving health and wellness.
7. Language Translation Tool: Develop a language translation tool that uses AI machine translation algorithms to translate text between different languages accurately and efficiently.
8. Autonomous Driving System: Work on a project to develop an autonomous driving system that uses AI computer vision and sensor data processing to navigate vehicles safely and efficiently on roads.
9. Personalized Content Generator: Create a tool that uses AI natural language generation techniques to generate personalized content, such as articles, emails, or marketing messages tailored to individual preferences.
10. Music Recommendation Engine: Build a music recommendation engine that uses AI algorithms to analyze music preferences and suggest playlists or songs based on user tastes and listening habits.
Join for more: https://t.iss.one/Programming_experts
ENJOY LEARNING ๐๐
โค1
Quick SQL functions cheat sheet for beginners
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, โฆ): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, โฆ): Returns the first non-null value.
Like for more free Cheatsheets โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalytics
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, โฆ): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, โฆ): Returns the first non-null value.
Like for more free Cheatsheets โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalytics
โค3
Top 5 Important Languages for Data Science ๐งโ๐ป๐
1. Python - 50% ๐
2. R - 20% ๐
3. SQL - 15% ๐๏ธ
4. Java - 7% โ
5. Julia - 5% ๐
6. Matlab - 3% ๐งฎ
1. Python - 50% ๐
2. R - 20% ๐
3. SQL - 15% ๐๏ธ
4. Java - 7% โ
5. Julia - 5% ๐
6. Matlab - 3% ๐งฎ
โค2๐1
๐ฐ How to become a data scientist in 2025?
๐จ๐ปโ๐ป If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
๐ข Step 1: Strengthen your math and statistics!
โ๏ธ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
โ Linear algebra: matrices, vectors, eigenvalues.
๐ Course: MIT 18.06 Linear Algebra
โ Calculus: derivative, integral, optimization.
๐ Course: MIT Single Variable Calculus
โ Statistics and probability: Bayes' theorem, hypothesis testing.
๐ Course: Statistics 110
โโโโโ
๐ข Step 2: Learn to code.
โ๏ธ Learn Python and become proficient in coding. The most important topics you need to master are:
โ Python: Pandas, NumPy, Matplotlib libraries
๐ Course: FreeCodeCamp Python Course
โ SQL language: Join commands, Window functions, query optimization.
๐ Course: Stanford SQL Course
โ Data structures and algorithms: arrays, linked lists, trees.
๐ Course: MIT Introduction to Algorithms
โโโโโ
๐ข Step 3: Clean and visualize data
โ๏ธ Learn how to process and clean data and then create an engaging story from it!
โ Data cleaning: Working with missing values โโand detecting outliers.
๐ Course: Data Cleaning
โ Data visualization: Matplotlib, Seaborn, Tableau
๐ Course: Data Visualization Tutorial
โโโโโ
๐ข Step 4: Learn Machine Learning
โ๏ธ It's time to enter the exciting world of machine learning! You should know these topics:
โ Supervised learning: regression, classification.
โ Unsupervised learning: clustering, PCA, anomaly detection.
โ Deep learning: neural networks, CNN, RNN
๐ Course: CS229: Machine Learning
โโโโโ
๐ข Step 5: Working with Big Data and Cloud Technologies
โ๏ธ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
โ Big Data Tools: Hadoop, Spark, Dask
โ Cloud platforms: AWS, GCP, Azure
๐ Course: Data Engineering
โโโโโ
๐ข Step 6: Do real projects!
โ๏ธ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
โ Kaggle competitions: solving real-world challenges.
โ End-to-End projects: data collection, modeling, implementation.
โ GitHub: Publish your projects on GitHub.
๐ Platform: Kaggle๐ Platform: ods.ai
โโโโโ
๐ข Step 7: Learn MLOps and deploy models
โ๏ธ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
โ MLOps training: model versioning, monitoring, model retraining.
โ Deployment models: Flask, FastAPI, Docker
๐ Course: Stanford MLOps Course
โโโโโ
๐ข Step 8: Stay up to date and network
โ๏ธ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
โ Read scientific articles: arXiv, Google Scholar
โ Connect with the data community:
๐ Site: Papers with code
๐ Site: AI Research at Google
๐จ๐ปโ๐ป If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
๐ข Step 1: Strengthen your math and statistics!
โ๏ธ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
โ Linear algebra: matrices, vectors, eigenvalues.
๐ Course: MIT 18.06 Linear Algebra
โ Calculus: derivative, integral, optimization.
๐ Course: MIT Single Variable Calculus
โ Statistics and probability: Bayes' theorem, hypothesis testing.
๐ Course: Statistics 110
โโโโโ
๐ข Step 2: Learn to code.
โ๏ธ Learn Python and become proficient in coding. The most important topics you need to master are:
โ Python: Pandas, NumPy, Matplotlib libraries
๐ Course: FreeCodeCamp Python Course
โ SQL language: Join commands, Window functions, query optimization.
๐ Course: Stanford SQL Course
โ Data structures and algorithms: arrays, linked lists, trees.
๐ Course: MIT Introduction to Algorithms
โโโโโ
๐ข Step 3: Clean and visualize data
โ๏ธ Learn how to process and clean data and then create an engaging story from it!
โ Data cleaning: Working with missing values โโand detecting outliers.
๐ Course: Data Cleaning
โ Data visualization: Matplotlib, Seaborn, Tableau
๐ Course: Data Visualization Tutorial
โโโโโ
๐ข Step 4: Learn Machine Learning
โ๏ธ It's time to enter the exciting world of machine learning! You should know these topics:
โ Supervised learning: regression, classification.
โ Unsupervised learning: clustering, PCA, anomaly detection.
โ Deep learning: neural networks, CNN, RNN
๐ Course: CS229: Machine Learning
โโโโโ
๐ข Step 5: Working with Big Data and Cloud Technologies
โ๏ธ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
โ Big Data Tools: Hadoop, Spark, Dask
โ Cloud platforms: AWS, GCP, Azure
๐ Course: Data Engineering
โโโโโ
๐ข Step 6: Do real projects!
โ๏ธ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
โ Kaggle competitions: solving real-world challenges.
โ End-to-End projects: data collection, modeling, implementation.
โ GitHub: Publish your projects on GitHub.
๐ Platform: Kaggle๐ Platform: ods.ai
โโโโโ
๐ข Step 7: Learn MLOps and deploy models
โ๏ธ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
โ MLOps training: model versioning, monitoring, model retraining.
โ Deployment models: Flask, FastAPI, Docker
๐ Course: Stanford MLOps Course
โโโโโ
๐ข Step 8: Stay up to date and network
โ๏ธ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
โ Read scientific articles: arXiv, Google Scholar
โ Connect with the data community:
๐ Site: Papers with code
๐ Site: AI Research at Google
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data
โค7
This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.
1. Supervised Learning
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.
Some common supervised learning algorithms include:
โก๏ธ Linear Regression โ For predicting continuous values, like house prices.
โก๏ธ Logistic Regression โ For predicting categories, like spam or not spam.
โก๏ธ Decision Trees โ For making decisions in a step-by-step way.
โก๏ธ K-Nearest Neighbors (KNN) โ For finding similar data points.
โก๏ธ Random Forests โ A collection of decision trees for better accuracy.
โก๏ธ Neural Networks โ The foundation of deep learning, mimicking the human brain.
2. Unsupervised Learning
With unsupervised learning, the model explores patterns in data that doesnโt have any labels. It finds hidden structures or groupings.
Some popular unsupervised learning algorithms include:
โก๏ธ K-Means Clustering โ For grouping data into clusters.
โก๏ธ Hierarchical Clustering โ For building a tree of clusters.
โก๏ธ Principal Component Analysis (PCA) โ For reducing data to its most important parts.
โก๏ธ Autoencoders โ For finding simpler representations of data.
3. Semi-Supervised Learning
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.
Common semi-supervised learning algorithms include:
โก๏ธ Label Propagation โ For spreading labels through connected data points.
โก๏ธ Semi-Supervised SVM โ For combining labeled and unlabeled data.
โก๏ธ Graph-Based Methods โ For using graph structures to improve learning.
4. Reinforcement Learning
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.
Popular reinforcement learning algorithms include:
โก๏ธ Q-Learning โ For learning the best actions over time.
โก๏ธ Deep Q-Networks (DQN) โ Combining Q-learning with deep learning.
โก๏ธ Policy Gradient Methods โ For learning policies directly.
โก๏ธ Proximal Policy Optimization (PPO) โ For stable and effective learning.
1. Supervised Learning
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.
Some common supervised learning algorithms include:
โก๏ธ Linear Regression โ For predicting continuous values, like house prices.
โก๏ธ Logistic Regression โ For predicting categories, like spam or not spam.
โก๏ธ Decision Trees โ For making decisions in a step-by-step way.
โก๏ธ K-Nearest Neighbors (KNN) โ For finding similar data points.
โก๏ธ Random Forests โ A collection of decision trees for better accuracy.
โก๏ธ Neural Networks โ The foundation of deep learning, mimicking the human brain.
2. Unsupervised Learning
With unsupervised learning, the model explores patterns in data that doesnโt have any labels. It finds hidden structures or groupings.
Some popular unsupervised learning algorithms include:
โก๏ธ K-Means Clustering โ For grouping data into clusters.
โก๏ธ Hierarchical Clustering โ For building a tree of clusters.
โก๏ธ Principal Component Analysis (PCA) โ For reducing data to its most important parts.
โก๏ธ Autoencoders โ For finding simpler representations of data.
3. Semi-Supervised Learning
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.
Common semi-supervised learning algorithms include:
โก๏ธ Label Propagation โ For spreading labels through connected data points.
โก๏ธ Semi-Supervised SVM โ For combining labeled and unlabeled data.
โก๏ธ Graph-Based Methods โ For using graph structures to improve learning.
4. Reinforcement Learning
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.
Popular reinforcement learning algorithms include:
โก๏ธ Q-Learning โ For learning the best actions over time.
โก๏ธ Deep Q-Networks (DQN) โ Combining Q-learning with deep learning.
โก๏ธ Policy Gradient Methods โ For learning policies directly.
โก๏ธ Proximal Policy Optimization (PPO) โ For stable and effective learning.
โค3
Goldman Sachs senior data analyst interview asked questions
SQL
1 find avg of salaries department wise from table
2 Write a SQL query to see employee name and manager name using a self-join on 'employees' table with columns 'emp_id', 'name', and 'manager_id'.
3 newest joinee for every department (solved using lead lag)
POWER BI
1. What does Filter context in DAX mean?
2. Explain how to implement Row-Level Security (RLS) in Power BI.
3. Describe different types of filters in Power BI.
4. Explain the difference between 'ALL' and 'ALLSELECTED' in DAX.
5. How do you calculate the total sales for a specific product using DAX?
PYTHON
1. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
2. Find unique values in a list of assorted numbers and print the count of how many times each value is repeated.
3. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Hope this helps you ๐
SQL
1 find avg of salaries department wise from table
2 Write a SQL query to see employee name and manager name using a self-join on 'employees' table with columns 'emp_id', 'name', and 'manager_id'.
3 newest joinee for every department (solved using lead lag)
POWER BI
1. What does Filter context in DAX mean?
2. Explain how to implement Row-Level Security (RLS) in Power BI.
3. Describe different types of filters in Power BI.
4. Explain the difference between 'ALL' and 'ALLSELECTED' in DAX.
5. How do you calculate the total sales for a specific product using DAX?
PYTHON
1. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
2. Find unique values in a list of assorted numbers and print the count of how many times each value is repeated.
3. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Hope this helps you ๐
โค7
๐ข 7 valuable resources that you can use to prepare for data science interviews!
๐ข One of the most important factors to get data science jobs in the best companies is success in job interviews.
๐ I have put here 7 valuable resources that helped me a lot while preparing for data science interviews. I hope these resources can help you succeed in data science interviews
1๏ธโฃ machine learning
๐ Link: Machine Learning
2๏ธโฃ Python programming language
๐ Link: Python Programming Language
3๏ธโฃ SQL programming language
๐ Link: SQL Programming Language
4๏ธโฃ R programming language
๐ Link: R Programming Language
5๏ธโฃ Pandas library
๐ Link: Pandas Python Library
6๏ธโฃ NumPy library
๐ Link: NumPy Python Library
7๏ธโฃ Matplotlib library
๐ Link: Matplotlib Python Library
Enjoy ๐
๐ข One of the most important factors to get data science jobs in the best companies is success in job interviews.
๐ I have put here 7 valuable resources that helped me a lot while preparing for data science interviews. I hope these resources can help you succeed in data science interviews
1๏ธโฃ machine learning
๐ Link: Machine Learning
2๏ธโฃ Python programming language
๐ Link: Python Programming Language
3๏ธโฃ SQL programming language
๐ Link: SQL Programming Language
4๏ธโฃ R programming language
๐ Link: R Programming Language
5๏ธโฃ Pandas library
๐ Link: Pandas Python Library
6๏ธโฃ NumPy library
๐ Link: NumPy Python Library
7๏ธโฃ Matplotlib library
๐ Link: Matplotlib Python Library
Enjoy ๐
โค6
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-552-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-552-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.
โค1
Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence pinned ยซ๐๐ฅ 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 ๐๐ฎ๐ป๐ด๐๐ต๐ฎ๐ถ๐ปโฆยป
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6. Prompt Engineering โ Your secret weapon to get the best out of AI. https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
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8. AI Studio โ Everything about AI & Tech https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
9. Google Gemini โ Generate images & videos with AI. https://whatsapp.com/channel/0029Vb5Q4ly3mFY3Jz7qIu3i/103
10. Data Science & Machine Learning โ Your fuel for insights, predictions, and smarter decisions. https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. Data Science Projects โ Your engine for building smarter, self-learning systems. https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z/208
React โค๏ธ for more
โค12
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-552-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-552-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.
โค6
Hi guys,
Now you can directly find job opportunities on WhatsApp. Here is the list of top job related channels on WhatsApp ๐
Latest Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Python & AI Jobs: https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R
Software Engineer Jobs: https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L
Data Science Jobs: https://whatsapp.com/channel/0029VaxTMmQADTOA746w7U2P
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Web Developer Jobs: https://whatsapp.com/channel/0029Vb1raTiDjiOias5ARu2p
Remote Jobs: https://whatsapp.com/channel/0029Vb1RrFuC1Fu3E0aiac2E
Google Jobs: https://whatsapp.com/channel/0029VaxngnVInlqV6xJhDs3m
Hope it helps :)
Now you can directly find job opportunities on WhatsApp. Here is the list of top job related channels on WhatsApp ๐
Latest Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Python & AI Jobs: https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R
Software Engineer Jobs: https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L
Data Science Jobs: https://whatsapp.com/channel/0029VaxTMmQADTOA746w7U2P
Data Analyst Jobs: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
Web Developer Jobs: https://whatsapp.com/channel/0029Vb1raTiDjiOias5ARu2p
Remote Jobs: https://whatsapp.com/channel/0029Vb1RrFuC1Fu3E0aiac2E
Google Jobs: https://whatsapp.com/channel/0029VaxngnVInlqV6xJhDs3m
Hope it helps :)
โค4
Complete SQL road map
๐๐
1.Intro to SQL
โข Definition
โข Purpose
โข Relational DBs
โข DBMS
2.Basic SQL Syntax
โข SELECT
โข FROM
โข WHERE
โข ORDER BY
โข GROUP BY
3. Data Types
โข Integer
โข Floating-Point
โข Character
โข Date
โข VARCHAR
โข TEXT
โข BLOB
โข BOOLEAN
4.Sub languages
โข DML
โข DDL
โข DQL
โข DCL
โข TCL
5. Data Manipulation
โข INSERT
โข UPDATE
โข DELETE
6. Data Definition
โข CREATE
โข ALTER
โข DROP
โข Indexes
7.Query Filtering and Sorting
โข WHERE
โข AND
โข OR Conditions
โข Ascending
โข Descending
8. Data Aggregation
โข SUM
โข AVG
โข COUNT
โข MIN
โข MAX
9.Joins and Relationships
โข INNER JOIN
โข LEFT JOIN
โข RIGHT JOIN
โข Self-Joins
โข Cross Joins
โข FULL OUTER JOIN
10.Subqueries
โข Subqueries used in
โข Filtering data
โข Aggregating data
โข Joining tables
โข Correlated Subqueries
11.Views
โข Creating
โข Modifying
โข Dropping Views
12.Transactions
โข ACID Properties
โข COMMIT
โข ROLLBACK
โข SAVEPOINT
โข ROLLBACK TO SAVEPOINT
13.Stored Procedures
โข CREATE PROCEDURE
โข ALTER PROCEDURE
โข DROP PROCEDURE
โข EXECUTE PROCEDURE
โข User-Defined Functions (UDFs)
14.Triggers
โข Trigger Events
โข Trigger Execution and Syntax
15. Security and Permissions
โข CREATE USER
โข GRANT
โข REVOKE
โข ALTER USER
โข DROP USER
16.Optimizations
โข Indexing Strategies
โข Query Optimization
17.Normalization
โข 1NF(Normal Form)
โข 2NF
โข 3NF
โข BCNF
18.Backup and Recovery
โข Database Backups
โข Point-in-Time Recovery
19.NoSQL Databases
โข MongoDB
โข Cassandra etc...
โข Key differences
20. Data Integrity
โข Primary Key
โข Foreign Key
21.Advanced SQL Queries
โข Window Functions
โข Common Table Expressions (CTEs)
22.Full-Text Search
โข Full-Text Indexes
โข Search Optimization
23. Data Import and Export
โข Importing Data
โข Exporting Data (CSV, JSON)
โข Using SQL Dump Files
24.Database Design
โข Entity-Relationship Diagrams
โข Normalization Techniques
25.Advanced Indexing
โข Composite Indexes
โข Covering Indexes
26.Database Transactions
โข Savepoints
โข Nested Transactions
โข Two-Phase Commit Protocol
27.Performance Tuning
โข Query Profiling and Analysis
โข Query Cache Optimization
------------------ END -------------------
๐๐
1.Intro to SQL
โข Definition
โข Purpose
โข Relational DBs
โข DBMS
2.Basic SQL Syntax
โข SELECT
โข FROM
โข WHERE
โข ORDER BY
โข GROUP BY
3. Data Types
โข Integer
โข Floating-Point
โข Character
โข Date
โข VARCHAR
โข TEXT
โข BLOB
โข BOOLEAN
4.Sub languages
โข DML
โข DDL
โข DQL
โข DCL
โข TCL
5. Data Manipulation
โข INSERT
โข UPDATE
โข DELETE
6. Data Definition
โข CREATE
โข ALTER
โข DROP
โข Indexes
7.Query Filtering and Sorting
โข WHERE
โข AND
โข OR Conditions
โข Ascending
โข Descending
8. Data Aggregation
โข SUM
โข AVG
โข COUNT
โข MIN
โข MAX
9.Joins and Relationships
โข INNER JOIN
โข LEFT JOIN
โข RIGHT JOIN
โข Self-Joins
โข Cross Joins
โข FULL OUTER JOIN
10.Subqueries
โข Subqueries used in
โข Filtering data
โข Aggregating data
โข Joining tables
โข Correlated Subqueries
11.Views
โข Creating
โข Modifying
โข Dropping Views
12.Transactions
โข ACID Properties
โข COMMIT
โข ROLLBACK
โข SAVEPOINT
โข ROLLBACK TO SAVEPOINT
13.Stored Procedures
โข CREATE PROCEDURE
โข ALTER PROCEDURE
โข DROP PROCEDURE
โข EXECUTE PROCEDURE
โข User-Defined Functions (UDFs)
14.Triggers
โข Trigger Events
โข Trigger Execution and Syntax
15. Security and Permissions
โข CREATE USER
โข GRANT
โข REVOKE
โข ALTER USER
โข DROP USER
16.Optimizations
โข Indexing Strategies
โข Query Optimization
17.Normalization
โข 1NF(Normal Form)
โข 2NF
โข 3NF
โข BCNF
18.Backup and Recovery
โข Database Backups
โข Point-in-Time Recovery
19.NoSQL Databases
โข MongoDB
โข Cassandra etc...
โข Key differences
20. Data Integrity
โข Primary Key
โข Foreign Key
21.Advanced SQL Queries
โข Window Functions
โข Common Table Expressions (CTEs)
22.Full-Text Search
โข Full-Text Indexes
โข Search Optimization
23. Data Import and Export
โข Importing Data
โข Exporting Data (CSV, JSON)
โข Using SQL Dump Files
24.Database Design
โข Entity-Relationship Diagrams
โข Normalization Techniques
25.Advanced Indexing
โข Composite Indexes
โข Covering Indexes
26.Database Transactions
โข Savepoints
โข Nested Transactions
โข Two-Phase Commit Protocol
27.Performance Tuning
โข Query Profiling and Analysis
โข Query Cache Optimization
------------------ END -------------------
โค9
Essential Topics to Master Data Science Interviews: ๐
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes
Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.
Show some โค๏ธ if you're ready to elevate your data science game! ๐
ENJOY LEARNING ๐๐
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes
Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.
Show some โค๏ธ if you're ready to elevate your data science game! ๐
ENJOY LEARNING ๐๐
โค8๐ฅ2
Essential Skills to Master for a Data Analytics Career
1๏ธโฃ SQL ๐๏ธ Learn how to query databases, use joins, aggregate data, and write optimized SQL queries.
2๏ธโฃ Data Visualization ๐ Communicate insights effectively using tools like Power BI, Tableau, and Excel charts.
3๏ธโฃ Python for Data Analysis ๐ Use libraries like Pandas, NumPy, and Matplotlib to manipulate and analyze data efficiently.
4๏ธโฃ Statistical Thinking ๐ Understand key concepts like probability, hypothesis testing, and regression analysis for data-driven decisions.
5๏ธโฃ Business Acumen ๐ผ Know how to translate raw data into actionable insights that drive business growth.
6๏ธโฃ Data Cleaning & Wrangling ๐งน Real-world data is messyโlearn techniques to handle missing values, duplicates, and outliers.
7๏ธโฃ Excel Proficiency ๐ Master formulas, PivotTables, and Power Query for quick and effective data analysis.
8๏ธโฃ Communication & Storytelling ๐ค Turn complex data findings into compelling narratives that stakeholders can understand.
9๏ธโฃ Critical Thinking & Problem-Solving ๐ Go beyond numbersโask the right questions and identify meaningful patterns in data.
๐ Continuous Learning & AI Integration ๐ค Stay updated with new analytics trends and leverage AI for automation and insights.
Master these skills, and youโll be well on your way to becoming a top-tier data analyst! ๐
Like for detailed explanation โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1๏ธโฃ SQL ๐๏ธ Learn how to query databases, use joins, aggregate data, and write optimized SQL queries.
2๏ธโฃ Data Visualization ๐ Communicate insights effectively using tools like Power BI, Tableau, and Excel charts.
3๏ธโฃ Python for Data Analysis ๐ Use libraries like Pandas, NumPy, and Matplotlib to manipulate and analyze data efficiently.
4๏ธโฃ Statistical Thinking ๐ Understand key concepts like probability, hypothesis testing, and regression analysis for data-driven decisions.
5๏ธโฃ Business Acumen ๐ผ Know how to translate raw data into actionable insights that drive business growth.
6๏ธโฃ Data Cleaning & Wrangling ๐งน Real-world data is messyโlearn techniques to handle missing values, duplicates, and outliers.
7๏ธโฃ Excel Proficiency ๐ Master formulas, PivotTables, and Power Query for quick and effective data analysis.
8๏ธโฃ Communication & Storytelling ๐ค Turn complex data findings into compelling narratives that stakeholders can understand.
9๏ธโฃ Critical Thinking & Problem-Solving ๐ Go beyond numbersโask the right questions and identify meaningful patterns in data.
๐ Continuous Learning & AI Integration ๐ค Stay updated with new analytics trends and leverage AI for automation and insights.
Master these skills, and youโll be well on your way to becoming a top-tier data analyst! ๐
Like for detailed explanation โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค5๐ฅ1
๐
SQL Revision Notes for Interview๐ก
โค5๐ฅ2