Data Science & Machine Learning
72.9K subscribers
776 photos
2 videos
68 files
683 links
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free

For collaborations: @love_data
Download Telegram
SQL Basics for Beginners: Must-Know Concepts

1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.

2. SQL Syntax
SQL is written using statements, which consist of keywords like SELECT, FROM, WHERE, etc., to perform operations on the data.
- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g., SELECT, FROM).

3. SQL Data Types
Databases store data in different formats. The most common data types are:
- INT (Integer): For whole numbers.
- VARCHAR(n) or TEXT: For storing text data.
- DATE: For dates.
- DECIMAL: For precise decimal values, often used in financial calculations.

4. Basic SQL Queries
Here are some fundamental SQL operations:

- SELECT Statement: Used to retrieve data from a database.

     SELECT column1, column2 FROM table_name;

- WHERE Clause: Filters data based on conditions.

     SELECT * FROM table_name WHERE condition;

- ORDER BY: Sorts data in ascending (ASC) or descending (DESC) order.

     SELECT column1, column2 FROM table_name ORDER BY column1 ASC;

- LIMIT: Limits the number of rows returned.

     SELECT * FROM table_name LIMIT 5;

5. Filtering Data with WHERE Clause
The WHERE clause helps you filter data based on a condition:

   SELECT * FROM employees WHERE salary > 50000;

You can use comparison operators like:
- =: Equal to
- >: Greater than
- <: Less than
- LIKE: For pattern matching

6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.

     SELECT COUNT(*) FROM table_name;

- SUM(): Adds up values in a column.

     SELECT SUM(salary) FROM employees;

- AVG(): Calculates the average value.

     SELECT AVG(salary) FROM employees;

- GROUP BY: Groups rows that have the same values into summary rows.

     SELECT department, AVG(salary) FROM employees GROUP BY department;

7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.

     SELECT employees.name, departments.department
FROM employees
INNER JOIN departments
ON employees.department_id = departments.id;

- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.

     SELECT employees.name, departments.department
FROM employees
LEFT JOIN departments
ON employees.department_id = departments.id;

8. Inserting Data
To add new data to a table, you use the INSERT INTO statement:

   INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);

9. Updating Data
You can update existing data in a table using the UPDATE statement:

   UPDATE employees SET salary = 65000 WHERE name = 'John Doe';

10. Deleting Data
To remove data from a table, use the DELETE statement:

    DELETE FROM employees WHERE name = 'John Doe';


Here you can find essential SQL Interview Resources๐Ÿ‘‡
https://t.iss.one/DataSimplifier

Like this post if you need more ๐Ÿ‘โค๏ธ

Hope it helps :)
โค5
SQL Checklist for Data Analysts ๐Ÿš€

๐ŸŒฑ Getting Started with SQL

๐Ÿ‘‰ Install SQL database software (MySQL, PostgreSQL, or SQL Server)
๐Ÿ‘‰ Set up your database environment and connect to your data

๐Ÿ” Load & Explore Data

๐Ÿ‘‰ Understand tables, rows, and columns
๐Ÿ‘‰ Use SELECT to retrieve data and LIMIT to get a sample view
๐Ÿ‘‰ Explore schema and table structure with DESCRIBE or SHOW COLUMNS

๐Ÿงน Data Filtering Essentials

๐Ÿ‘‰ Filter data using WHERE clauses
๐Ÿ‘‰ Use comparison operators (=, >, <) and logical operators (AND, OR)
๐Ÿ‘‰ Handle NULL values with IS NULL and IS NOT NULL

๐Ÿ”„ Transforming Data

๐Ÿ‘‰ Sort data with ORDER BY
๐Ÿ‘‰ Create calculated columns with AS and use arithmetic operators (+, -, *, /)
๐Ÿ‘‰ Use CASE WHEN for conditional expressions

๐Ÿ“Š Aggregation & Grouping

๐Ÿ‘‰ Summarize data with aggregation functions: SUM, COUNT, AVG, MIN, MAX
๐Ÿ‘‰ Group data with GROUP BY and filter groups with HAVING

๐Ÿ”— Mastering Joins

๐Ÿ‘‰ Combine tables with JOIN (INNER, LEFT, RIGHT, FULL OUTER)
๐Ÿ‘‰ Understand primary and foreign keys to create meaningful joins
๐Ÿ‘‰ Use SELF JOIN for analyzing data within the same table

๐Ÿ“… Date & Time Data

๐Ÿ‘‰ Convert dates and extract parts (year, month, day) with EXTRACT
๐Ÿ‘‰ Perform time-based analysis using DATEDIFF and date functions

๐Ÿ“ˆ Quick Exploratory Analysis

๐Ÿ‘‰ Calculate statistics to understand data distributions
๐Ÿ‘‰ Use GROUP BY with aggregation for category-based analysis

๐Ÿ“‰ Basic Data Visualizations (Optional)

๐Ÿ‘‰ Integrate SQL with visualization tools (Power BI, Tableau)
๐Ÿ‘‰ Create charts directly in SQL with certain extensions (like MySQL's built-in charts)

๐Ÿ’ช Advanced Query Handling

๐Ÿ‘‰ Master subqueries and nested queries
๐Ÿ‘‰ Use WITH (Common Table Expressions) for complex queries
๐Ÿ‘‰ Window functions for running totals, moving averages, and rankings (ROW_NUMBER, RANK, LAG, LEAD)

๐Ÿš€ Optimize for Performance

๐Ÿ‘‰ Index critical columns for faster querying
๐Ÿ‘‰ Analyze query plans and use optimizations
๐Ÿ‘‰ Limit result sets and avoid excessive joins for efficiency

๐Ÿ“‚ Practice Projects

๐Ÿ‘‰ Use real datasets to perform SQL analysis
๐Ÿ‘‰ Create a portfolio with case studies and projects

Here you can find SQL Interview Resources๐Ÿ‘‡
https://t.iss.one/DataSimplifier

Like this post if you need more ๐Ÿ‘โค๏ธ

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)
โค5๐Ÿ‘1
๐—›๐—ผ๐˜„ ๐˜๐—ผ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐—ฆ๐—ค๐—Ÿ ๐—ณ๐—ผ๐—ฟ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ (๐—ช๐—ถ๐˜๐—ต๐—ผ๐˜‚๐˜ ๐—š๐—ฒ๐˜๐˜๐—ถ๐—ป๐—ด ๐—ข๐˜ƒ๐—ฒ๐—ฟ๐˜„๐—ต๐—ฒ๐—น๐—บ๐—ฒ๐—ฑ!)๐Ÿง 

Letโ€™s be honest:
SQL seems simpleโ€ฆ until JOINs, Subqueries, and Window Functions come crashing in.

But mastering SQL doesnโ€™t have to be hard.

You just need the right roadmapโ€”and thatโ€™s exactly what this is.

Hereโ€™s a 5-step SQL journey to go from beginner to job-ready analyst๐Ÿ‘‡

๐Ÿ”น ๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿญ: Nail the Basics (Learn to Think in SQL)

Start with the foundations:

โœ… SELECT, WHERE, ORDER BY
โœ… DISTINCT, LIMIT, BETWEEN, LIKE
โœ… COUNT, SUM, AVG, MIN, MAX

Practice with small tables to build confidence.

Use platforms like:
โžก๏ธ W3Schools
โžก๏ธ Modesql
โžก๏ธ LeetCode (easy problems)

๐Ÿ”น ๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฎ: Understand GROUP BY and Aggregations (The Analystโ€™s Superpower)

This is where real-world queries begin. Learn:

โœ… GROUP BY + HAVING
โœ… Combining GROUP BY with COUNT/AVG
โœ… Filtering aggregated data

Example:
"Find top 5 cities with the highest total sales in 2023"
Thatโ€™s GROUP BY magic.

๐Ÿ”น ๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฏ: MASTER JOINS (Stop Getting Confused)
JOINS scare a lot of people. But theyโ€™re just pattern-matching across tables.

Learn one by one:
โœ… INNER JOIN
โœ… LEFT JOIN
โœ… RIGHT JOIN
โœ… FULL OUTER JOIN
โœ… SELF JOIN
โœ… CROSS JOIN (rare, but good to know)
Visualize them using Venn diagrams or draw sample tablesโ€”it helps!

๐Ÿ”น ๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฐ: Learn Subqueries and CTEs (Write Cleaner, Powerful SQL)

โœ… Subqueries: Query inside another query
โœ… CTEs (WITH clause): Cleaner and reusable queries
โœ… Use them to break down complex problems

CTEs = the secret sauce to writing queries recruiters love.

๐Ÿ”น ๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฑ: Level Up with Window Functions (Your Entry into Advanced SQL)

If you want to stand out, this is it:

โœ… ROW_NUMBER(), RANK(), DENSE_RANK()
โœ… LAG(), LEAD(), NTILE()
โœ… PARTITION BY and ORDER BY combo

Use these to:
โžก๏ธ Find top N per group
โžก๏ธ Track user behavior over time
โžก๏ธ Do cohort analysis

You donโ€™t need 100 LeetCode problems.

You need 10 real-world queries done deeply
.

Keep it simple. Keep it useful.
โค10
Roadmap to become Data Scientist
๐Ÿ‘12๐Ÿ˜5
Top 20 #SQL INTERVIEW QUESTIONS

1๏ธโƒฃ Explain Order of Execution of SQL query
2๏ธโƒฃ Provide a use case for each of the functions Rank, Dense_Rank & Row_Number ( ๐Ÿ’ก majority struggle )
3๏ธโƒฃ Write a query to find the cumulative sum/Running Total
4๏ธโƒฃ Find the Most selling product by sales/ highest Salary of employees
5๏ธโƒฃ Write a query to find the 2nd/nth highest Salary of employees
6๏ธโƒฃ Difference between union vs union all
7๏ธโƒฃ Identify if there any duplicates in a table
8๏ธโƒฃ Scenario based Joins question, understanding of Inner, Left and Outer Joins via simple yet tricky question
9๏ธโƒฃ LAG, write a query to find all those records where the transaction value is greater then previous transaction value
1๏ธโƒฃ 0๏ธโƒฃ Rank vs Dense Rank, query to find the 2nd highest Salary of employee
( Ideal soln should handle ties)
1๏ธโƒฃ 1๏ธโƒฃ Write a query to find the Running Difference (Ideal sol'n using windows function)
1๏ธโƒฃ 2๏ธโƒฃ Write a query to display year on year/month on month growth
1๏ธโƒฃ 3๏ธโƒฃ Write a query to find rolling average of daily sign-ups
1๏ธโƒฃ 4๏ธโƒฃ Write a query to find the running difference using self join (helps in understanding the logical approach, ideally this question is solved via windows function)
1๏ธโƒฃ 5๏ธโƒฃ Write a query to find the cumulative sum using self join
(you can use windows function to solve this question)
1๏ธโƒฃ6๏ธโƒฃ Differentiate between a clustered index and a non-clustered index?
1๏ธโƒฃ7๏ธโƒฃ What is a Candidate key?
1๏ธโƒฃ8๏ธโƒฃWhat is difference between Primary key and Unique key?
1๏ธโƒฃ9๏ธโƒฃWhat's the difference between RANK & DENSE_RANK in SQL?
2๏ธโƒฃ0๏ธโƒฃ Whats the difference between LAG & LEAD in SQL?

Access SQL Learning Series for Free: https://t.iss.one/sqlspecialist/523

Hope it helps :)
โค3๐Ÿ‘2
5 Key SQL Aggregate Functions for data analyst

๐ŸžSUM(): Adds up all the values in a numeric column.

๐ŸžAVG(): Calculates the average of a numeric column.

๐ŸžCOUNT(): Counts the total number of rows or non-NULL values in a column.

๐ŸžMAX(): Returns the highest value in a column.

๐ŸžMIN(): Returns the lowest value in a column.
โค2
SQL vs Mysql
โค4๐Ÿ‘3
Want to become a Data Scientist?

Hereโ€™s a quick roadmap with essential concepts:

1. Mathematics & Statistics

Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning.

Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance.

Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization.


2. Programming

Python or R: Choose a primary programming language for data science.

Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning.

R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization.


SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets.


3. Data Wrangling & Preprocessing

Data Cleaning: Handle missing values, outliers, duplicates, and data formatting.
Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.).
Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights.


4. Data Visualization

Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data.
Tableau or Power BI: Learn interactive visualization tools for building dashboards.
Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders.


5. Machine Learning

Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).
Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression.


6. Advanced Machine Learning & Deep Learning

Neural Networks: Understand the basics of neural networks and backpropagation.
Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
Transfer Learning: Apply pre-trained models for specific use cases.
Frameworks: Use TensorFlow Keras for building deep learning models.


7. Natural Language Processing (NLP)

Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal.
NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe).
NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation.


8. Big Data Tools (Optional)

Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing.


9. Data Science Workflows & Pipelines (Optional)

ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring.
Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform).


10. Model Validation & Tuning

Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting.
Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance.
Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization.


11. Time Series Analysis

Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting.
Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting.


12. Experimentation & A/B Testing

Experiment Design: Learn how to set up and analyze controlled experiments.
A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes.

ENJOY LEARNING ๐Ÿ‘๐Ÿ‘
โค6
โœ… Essential NLP Techniques Every Data Scientist Should Know ๐Ÿš€ ๐Ÿ“

These NLP techniques are crucial for extracting insights from text and building intelligent applications.

1๏ธโƒฃ Tokenization: Breaking Down Text ๐Ÿงฉ
- Split text into individual units (words, phrases, symbols).
- Essential for preparing text for analysis.

2๏ธโƒฃ Stop Word Removal: Clearing the Clutter ๐Ÿšซ
- Remove common words (e.g., "the," "a," "is") that don't carry much meaning.
- Helps focus on important content words.

3๏ธโƒฃ Stemming & Lemmatization: Reducing to the Root ๐ŸŒณ
- Reduce words to their base form (stem or lemma).
- Improves analysis by grouping related words together.
โ€“ Stemming (fast but may create non-words): running -> run
โ€“ Lemmatization (accurate but slower): better -> good

4๏ธโƒฃ Named Entity Recognition (NER): Spotting the Key Players ๐Ÿ‘ค
- Identify and classify named entities (people, organizations, locations, dates).
- Useful for extracting structured information.

5๏ธโƒฃ TF-IDF: Identifying Important Words โš–๏ธ
- Measures word importance in a document relative to the entire corpus.
- Helps identify keywords and significant terms.
- TF (Term Frequency): How often a word appears in a document.
- IDF (Inverse Document Frequency): How rare the word is across all documents.

6๏ธโƒฃ Bag of Words: Representing Text Numerically ๐Ÿ”ข
- Create a vector representation of text based on word counts.
- Useful for machine learning algorithms that require numerical input.

๐Ÿ’ก Master these techniques to analyze text, classify documents, and build NLP models.

React โค๏ธ for more
โค4
Planning for Data Science or Data Engineering Interview.

Focus on SQL & Python first. Here are some important questions which you should know.

๐ˆ๐ฆ๐ฉ๐จ๐ซ๐ญ๐š๐ง๐ญ ๐’๐๐‹ ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ

1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.

๐ˆ๐ฆ๐ฉ๐จ๐ซ๐ญ๐š๐ง๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ

1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

Join for more: https://t.iss.one/datasciencefun

ENJOY LEARNING ๐Ÿ‘๐Ÿ‘
โค4
Data Science Essential Libraries โœ…
โค7
Here you can find free SQL Resources
๐Ÿ‘‡๐Ÿ‘‡
https://t.iss.one/sqlspecialist
โค3
Step-by-Step Roadmap to Learn Data Science in 2025:

Step 1: Understand the Role
A data scientist in 2025 is expected to:

Analyze data to extract insights

Build predictive models using ML

Communicate findings to stakeholders

Work with large datasets in cloud environments


Step 2: Master the Prerequisite Skills

A. Programming

Learn Python (must-have): Focus on pandas, numpy, matplotlib, seaborn, scikit-learn

R (optional but helpful for statistical analysis)

SQL: Strong command over data extraction and transformation


B. Math & Stats

Probability, Descriptive & Inferential Statistics

Linear Algebra & Calculus (only what's necessary for ML)

Hypothesis testing


Step 3: Learn Data Handling

Data Cleaning, Preprocessing

Exploratory Data Analysis (EDA)

Feature Engineering

Tools: Python (pandas), Excel, SQL


Step 4: Master Machine Learning

Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forests, XGBoost

Unsupervised Learning: K-Means, Hierarchical Clustering, PCA

Deep Learning (optional): Use TensorFlow or PyTorch

Evaluation Metrics: Accuracy, AUC, Confusion Matrix, RMSE


Step 5: Learn Data Visualization & Storytelling

Python (matplotlib, seaborn, plotly)

Power BI / Tableau

Communicating insights clearly is as important as modeling


Step 6: Use Real Datasets & Projects

Work on projects using Kaggle, UCI, or public APIs

Examples:

Customer churn prediction

Sales forecasting

Sentiment analysis

Fraud detection



Step 7: Understand Cloud & MLOps (2025+ Skills)

Cloud: AWS (S3, EC2, SageMaker), GCP, or Azure

MLOps: Model deployment (Flask, FastAPI), CI/CD for ML, Docker basics


Step 8: Build Portfolio & Resume

Create GitHub repos with well-documented code

Post projects and blogs on Medium or LinkedIn

Prepare a data science-specific resume


Step 9: Apply Smartly

Focus on job roles like: Data Scientist, ML Engineer, Data Analyst โ†’ DS

Use platforms like LinkedIn, Glassdoor, Hirect, AngelList, etc.

Practice data science interviews: case studies, ML concepts, SQL + Python coding


Step 10: Keep Learning & Updating

Follow top newsletters: Data Elixir, Towards Data Science

Read papers (arXiv, Google Scholar) on trending topics: LLMs, AutoML, Explainable AI

Upskill with certifications (Google Data Cert, Coursera, DataCamp, Udemy)

Free Resources to learn Data Science

Kaggle Courses: https://www.kaggle.com/learn

CS50 AI by Harvard: https://cs50.harvard.edu/ai/

Fast.ai: https://course.fast.ai/

Google ML Crash Course: https://developers.google.com/machine-learning/crash-course

Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998

Data Science Books: https://t.iss.one/datalemur

React โค๏ธ for more
โค6๐Ÿ‘1
Best Code Editors For Python ๐Ÿ‘จโ€๐Ÿ’ป
โค9๐Ÿ‘5
๐Ÿ”— Roadmap to master Machine Learning
โค4๐Ÿ‘1