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, …): 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, …): Returns the first non-null value.
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#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, …): 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, …): Returns the first non-null value.
Like for more free Cheatsheets ❤️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalytics
❤3
Top 5 Important Languages for Data Science 🧑💻📊
1. Python - 50% 🐍
2. R - 20% 📉
3. SQL - 15% 🗄️
4. Java - 7% ☕
5. Julia - 5% 🚀
6. Matlab - 3% 🧮
1. Python - 50% 🐍
2. R - 20% 📉
3. SQL - 15% 🗄️
4. Java - 7% ☕
5. Julia - 5% 🚀
6. Matlab - 3% 🧮
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🔰 How to become a data scientist in 2025?
👨🏻💻 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.
🔢 Step 1: Strengthen your math and statistics!
✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
✅ Linear algebra: matrices, vectors, eigenvalues.
🔗 Course: MIT 18.06 Linear Algebra
✅ Calculus: derivative, integral, optimization.
🔗 Course: MIT Single Variable Calculus
✅ Statistics and probability: Bayes' theorem, hypothesis testing.
🔗 Course: Statistics 110
➖➖➖➖➖
🔢 Step 2: Learn to code.
✏️ Learn Python and become proficient in coding. The most important topics you need to master are:
✅ Python: Pandas, NumPy, Matplotlib libraries
🔗 Course: FreeCodeCamp Python Course
✅ SQL language: Join commands, Window functions, query optimization.
🔗 Course: Stanford SQL Course
✅ Data structures and algorithms: arrays, linked lists, trees.
🔗 Course: MIT Introduction to Algorithms
➖➖➖➖➖
🔢 Step 3: Clean and visualize data
✏️ Learn how to process and clean data and then create an engaging story from it!
✅ Data cleaning: Working with missing values and detecting outliers.
🔗 Course: Data Cleaning
✅ Data visualization: Matplotlib, Seaborn, Tableau
🔗 Course: Data Visualization Tutorial
➖➖➖➖➖
🔢 Step 4: Learn Machine Learning
✏️ It's time to enter the exciting world of machine learning! You should know these topics:
✅ Supervised learning: regression, classification.
✅ Unsupervised learning: clustering, PCA, anomaly detection.
✅ Deep learning: neural networks, CNN, RNN
🔗 Course: CS229: Machine Learning
➖➖➖➖➖
🔢 Step 5: Working with Big Data and Cloud Technologies
✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
✅ Big Data Tools: Hadoop, Spark, Dask
✅ Cloud platforms: AWS, GCP, Azure
🔗 Course: Data Engineering
➖➖➖➖➖
🔢 Step 6: Do real projects!
✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
✅ Kaggle competitions: solving real-world challenges.
✅ End-to-End projects: data collection, modeling, implementation.
✅ GitHub: Publish your projects on GitHub.
🔗 Platform: Kaggle🔗 Platform: ods.ai
➖➖➖➖➖
🔢 Step 7: Learn MLOps and deploy models
✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
✅ MLOps training: model versioning, monitoring, model retraining.
✅ Deployment models: Flask, FastAPI, Docker
🔗 Course: Stanford MLOps Course
➖➖➖➖➖
🔢 Step 8: Stay up to date and network
✏️ 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.
✅ Read scientific articles: arXiv, Google Scholar
✅ Connect with the data community:
🔗 Site: Papers with code
🔗 Site: AI Research at Google
👨🏻💻 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.
🔢 Step 1: Strengthen your math and statistics!
✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
✅ Linear algebra: matrices, vectors, eigenvalues.
🔗 Course: MIT 18.06 Linear Algebra
✅ Calculus: derivative, integral, optimization.
🔗 Course: MIT Single Variable Calculus
✅ Statistics and probability: Bayes' theorem, hypothesis testing.
🔗 Course: Statistics 110
➖➖➖➖➖
🔢 Step 2: Learn to code.
✏️ Learn Python and become proficient in coding. The most important topics you need to master are:
✅ Python: Pandas, NumPy, Matplotlib libraries
🔗 Course: FreeCodeCamp Python Course
✅ SQL language: Join commands, Window functions, query optimization.
🔗 Course: Stanford SQL Course
✅ Data structures and algorithms: arrays, linked lists, trees.
🔗 Course: MIT Introduction to Algorithms
➖➖➖➖➖
🔢 Step 3: Clean and visualize data
✏️ Learn how to process and clean data and then create an engaging story from it!
✅ Data cleaning: Working with missing values and detecting outliers.
🔗 Course: Data Cleaning
✅ Data visualization: Matplotlib, Seaborn, Tableau
🔗 Course: Data Visualization Tutorial
➖➖➖➖➖
🔢 Step 4: Learn Machine Learning
✏️ It's time to enter the exciting world of machine learning! You should know these topics:
✅ Supervised learning: regression, classification.
✅ Unsupervised learning: clustering, PCA, anomaly detection.
✅ Deep learning: neural networks, CNN, RNN
🔗 Course: CS229: Machine Learning
➖➖➖➖➖
🔢 Step 5: Working with Big Data and Cloud Technologies
✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
✅ Big Data Tools: Hadoop, Spark, Dask
✅ Cloud platforms: AWS, GCP, Azure
🔗 Course: Data Engineering
➖➖➖➖➖
🔢 Step 6: Do real projects!
✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
✅ Kaggle competitions: solving real-world challenges.
✅ End-to-End projects: data collection, modeling, implementation.
✅ GitHub: Publish your projects on GitHub.
🔗 Platform: Kaggle🔗 Platform: ods.ai
➖➖➖➖➖
🔢 Step 7: Learn MLOps and deploy models
✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
✅ MLOps training: model versioning, monitoring, model retraining.
✅ Deployment models: Flask, FastAPI, Docker
🔗 Course: Stanford MLOps Course
➖➖➖➖➖
🔢 Step 8: Stay up to date and network
✏️ 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.
✅ Read scientific articles: arXiv, Google Scholar
✅ Connect with the data community:
🔗 Site: Papers with code
🔗 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
❤7
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:
➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.
2. Unsupervised Learning
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.
Some popular unsupervised learning algorithms include:
➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – 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:
➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – 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:
➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – 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:
➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.
2. Unsupervised Learning
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.
Some popular unsupervised learning algorithms include:
➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – 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:
➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – 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:
➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.
❤3
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 👇👇
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Hope this helps you 😊
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 👇👇
https://t.iss.one/DataSimplifier
Hope this helps you 😊
❤7
🟢 7 valuable resources that you can use to prepare for data science interviews!
🟢 One of the most important factors to get data science jobs in the best companies is success in job interviews.
🗂 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
1️⃣ machine learning
📕 Link: Machine Learning
2️⃣ Python programming language
📕 Link: Python Programming Language
3️⃣ SQL programming language
📕 Link: SQL Programming Language
4️⃣ R programming language
📕 Link: R Programming Language
5️⃣ Pandas library
📕 Link: Pandas Python Library
6️⃣ NumPy library
📕 Link: NumPy Python Library
7️⃣ Matplotlib library
📕 Link: Matplotlib Python Library
Enjoy 👍
🟢 One of the most important factors to get data science jobs in the best companies is success in job interviews.
🗂 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
1️⃣ machine learning
📕 Link: Machine Learning
2️⃣ Python programming language
📕 Link: Python Programming Language
3️⃣ SQL programming language
📕 Link: SQL Programming Language
4️⃣ R programming language
📕 Link: R Programming Language
5️⃣ Pandas library
📕 Link: Pandas Python Library
6️⃣ NumPy library
📕 Link: NumPy Python Library
7️⃣ Matplotlib library
📕 Link: Matplotlib Python Library
Enjoy 👍
❤6
🚀🔥 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺
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Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.
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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-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.
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Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence pinned «🚀🔥 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…»
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Top AI Channels on WhatsApp!
1. ChatGPT – Your go-to AI for anything and everything. https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
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5. Generative AI – Your creative partner for text, images, code, and more. https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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🚀🔥 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-552-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-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.
❤6
Hi guys,
Now you can directly find job opportunities on WhatsApp. Here is the list of top job related channels on WhatsApp 👇
Latest Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Python & AI Jobs: https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R
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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 :)
Now you can directly find job opportunities on WhatsApp. Here is the list of top job related channels on WhatsApp 👇
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 :)
❤4
Complete SQL road map
👇👇
1.Intro to SQL
• Definition
• Purpose
• Relational DBs
• DBMS
2.Basic SQL Syntax
• SELECT
• FROM
• WHERE
• ORDER BY
• GROUP BY
3. Data Types
• Integer
• Floating-Point
• Character
• Date
• VARCHAR
• TEXT
• BLOB
• BOOLEAN
4.Sub languages
• DML
• DDL
• DQL
• DCL
• TCL
5. Data Manipulation
• INSERT
• UPDATE
• DELETE
6. Data Definition
• CREATE
• ALTER
• DROP
• Indexes
7.Query Filtering and Sorting
• WHERE
• AND
• OR Conditions
• Ascending
• Descending
8. Data Aggregation
• SUM
• AVG
• COUNT
• MIN
• MAX
9.Joins and Relationships
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• Self-Joins
• Cross Joins
• FULL OUTER JOIN
10.Subqueries
• Subqueries used in
• Filtering data
• Aggregating data
• Joining tables
• Correlated Subqueries
11.Views
• Creating
• Modifying
• Dropping Views
12.Transactions
• ACID Properties
• COMMIT
• ROLLBACK
• SAVEPOINT
• ROLLBACK TO SAVEPOINT
13.Stored Procedures
• CREATE PROCEDURE
• ALTER PROCEDURE
• DROP PROCEDURE
• EXECUTE PROCEDURE
• User-Defined Functions (UDFs)
14.Triggers
• Trigger Events
• Trigger Execution and Syntax
15. Security and Permissions
• CREATE USER
• GRANT
• REVOKE
• ALTER USER
• DROP USER
16.Optimizations
• Indexing Strategies
• Query Optimization
17.Normalization
• 1NF(Normal Form)
• 2NF
• 3NF
• BCNF
18.Backup and Recovery
• Database Backups
• Point-in-Time Recovery
19.NoSQL Databases
• MongoDB
• Cassandra etc...
• Key differences
20. Data Integrity
• Primary Key
• Foreign Key
21.Advanced SQL Queries
• Window Functions
• Common Table Expressions (CTEs)
22.Full-Text Search
• Full-Text Indexes
• Search Optimization
23. Data Import and Export
• Importing Data
• Exporting Data (CSV, JSON)
• Using SQL Dump Files
24.Database Design
• Entity-Relationship Diagrams
• Normalization Techniques
25.Advanced Indexing
• Composite Indexes
• Covering Indexes
26.Database Transactions
• Savepoints
• Nested Transactions
• Two-Phase Commit Protocol
27.Performance Tuning
• Query Profiling and Analysis
• Query Cache Optimization
------------------ END -------------------
👇👇
1.Intro to SQL
• Definition
• Purpose
• Relational DBs
• DBMS
2.Basic SQL Syntax
• SELECT
• FROM
• WHERE
• ORDER BY
• GROUP BY
3. Data Types
• Integer
• Floating-Point
• Character
• Date
• VARCHAR
• TEXT
• BLOB
• BOOLEAN
4.Sub languages
• DML
• DDL
• DQL
• DCL
• TCL
5. Data Manipulation
• INSERT
• UPDATE
• DELETE
6. Data Definition
• CREATE
• ALTER
• DROP
• Indexes
7.Query Filtering and Sorting
• WHERE
• AND
• OR Conditions
• Ascending
• Descending
8. Data Aggregation
• SUM
• AVG
• COUNT
• MIN
• MAX
9.Joins and Relationships
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• Self-Joins
• Cross Joins
• FULL OUTER JOIN
10.Subqueries
• Subqueries used in
• Filtering data
• Aggregating data
• Joining tables
• Correlated Subqueries
11.Views
• Creating
• Modifying
• Dropping Views
12.Transactions
• ACID Properties
• COMMIT
• ROLLBACK
• SAVEPOINT
• ROLLBACK TO SAVEPOINT
13.Stored Procedures
• CREATE PROCEDURE
• ALTER PROCEDURE
• DROP PROCEDURE
• EXECUTE PROCEDURE
• User-Defined Functions (UDFs)
14.Triggers
• Trigger Events
• Trigger Execution and Syntax
15. Security and Permissions
• CREATE USER
• GRANT
• REVOKE
• ALTER USER
• DROP USER
16.Optimizations
• Indexing Strategies
• Query Optimization
17.Normalization
• 1NF(Normal Form)
• 2NF
• 3NF
• BCNF
18.Backup and Recovery
• Database Backups
• Point-in-Time Recovery
19.NoSQL Databases
• MongoDB
• Cassandra etc...
• Key differences
20. Data Integrity
• Primary Key
• Foreign Key
21.Advanced SQL Queries
• Window Functions
• Common Table Expressions (CTEs)
22.Full-Text Search
• Full-Text Indexes
• Search Optimization
23. Data Import and Export
• Importing Data
• Exporting Data (CSV, JSON)
• Using SQL Dump Files
24.Database Design
• Entity-Relationship Diagrams
• Normalization Techniques
25.Advanced Indexing
• Composite Indexes
• Covering Indexes
26.Database Transactions
• Savepoints
• Nested Transactions
• Two-Phase Commit Protocol
27.Performance Tuning
• Query Profiling and Analysis
• Query Cache Optimization
------------------ END -------------------
❤8
Essential Topics to Master Data Science Interviews: 🚀
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 ❤️ if you're ready to elevate your data science game! 📊
ENJOY LEARNING 👍👍
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 ❤️ if you're ready to elevate your data science game! 📊
ENJOY LEARNING 👍👍
❤8🔥2
Essential Skills to Master for a Data Analytics Career
1️⃣ SQL 🗂️ Learn how to query databases, use joins, aggregate data, and write optimized SQL queries.
2️⃣ Data Visualization 📊 Communicate insights effectively using tools like Power BI, Tableau, and Excel charts.
3️⃣ Python for Data Analysis 🐍 Use libraries like Pandas, NumPy, and Matplotlib to manipulate and analyze data efficiently.
4️⃣ Statistical Thinking 📈 Understand key concepts like probability, hypothesis testing, and regression analysis for data-driven decisions.
5️⃣ Business Acumen 💼 Know how to translate raw data into actionable insights that drive business growth.
6️⃣ Data Cleaning & Wrangling 🧹 Real-world data is messy—learn techniques to handle missing values, duplicates, and outliers.
7️⃣ Excel Proficiency 📑 Master formulas, PivotTables, and Power Query for quick and effective data analysis.
8️⃣ Communication & Storytelling 🎤 Turn complex data findings into compelling narratives that stakeholders can understand.
9️⃣ Critical Thinking & Problem-Solving 🔍 Go beyond numbers—ask the right questions and identify meaningful patterns in data.
🔟 Continuous Learning & AI Integration 🤖 Stay updated with new analytics trends and leverage AI for automation and insights.
Master these skills, and you’ll be well on your way to becoming a top-tier data analyst! 🚀
Like for detailed explanation ❤️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1️⃣ SQL 🗂️ Learn how to query databases, use joins, aggregate data, and write optimized SQL queries.
2️⃣ Data Visualization 📊 Communicate insights effectively using tools like Power BI, Tableau, and Excel charts.
3️⃣ Python for Data Analysis 🐍 Use libraries like Pandas, NumPy, and Matplotlib to manipulate and analyze data efficiently.
4️⃣ Statistical Thinking 📈 Understand key concepts like probability, hypothesis testing, and regression analysis for data-driven decisions.
5️⃣ Business Acumen 💼 Know how to translate raw data into actionable insights that drive business growth.
6️⃣ Data Cleaning & Wrangling 🧹 Real-world data is messy—learn techniques to handle missing values, duplicates, and outliers.
7️⃣ Excel Proficiency 📑 Master formulas, PivotTables, and Power Query for quick and effective data analysis.
8️⃣ Communication & Storytelling 🎤 Turn complex data findings into compelling narratives that stakeholders can understand.
9️⃣ Critical Thinking & Problem-Solving 🔍 Go beyond numbers—ask the right questions and identify meaningful patterns in data.
🔟 Continuous Learning & AI Integration 🤖 Stay updated with new analytics trends and leverage AI for automation and insights.
Master these skills, and you’ll be well on your way to becoming a top-tier data analyst! 🚀
Like for detailed explanation ❤️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤5🔥1
🔅SQL Revision Notes for Interview💡
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