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
- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g.,
3. SQL Data Types
Databases store data in different formats. The most common data types are:
-
-
-
-
4. Basic SQL Queries
Here are some fundamental SQL operations:
- SELECT Statement: Used to retrieve data from a database.
- WHERE Clause: Filters data based on conditions.
- ORDER BY: Sorts data in ascending (
- LIMIT: Limits the number of rows returned.
5. Filtering Data with WHERE Clause
The
You can use comparison operators like:
-
-
-
-
6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.
- SUM(): Adds up values in a column.
- AVG(): Calculates the average value.
- GROUP BY: Groups rows that have the same values into summary rows.
7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.
- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
8. Inserting Data
To add new data to a table, you use the
9. Updating Data
You can update existing data in a table using the
10. Deleting Data
To remove data from a table, use the
Here you can find essential SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Like this post if you need more ๐โค๏ธ
Hope it helps :)
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 matching6. 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 Resources๐
https://t.iss.one/DataSimplifier
Like this post if you need more ๐โค๏ธ
Hope it helps :)
โค5
SQL Checklist for Data Analysts ๐
๐ฑ Getting Started with SQL
๐ Install SQL database software (MySQL, PostgreSQL, or SQL Server)
๐ Set up your database environment and connect to your data
๐ Load & Explore Data
๐ Understand tables, rows, and columns
๐ Use SELECT to retrieve data and LIMIT to get a sample view
๐ Explore schema and table structure with DESCRIBE or SHOW COLUMNS
๐งน Data Filtering Essentials
๐ Filter data using WHERE clauses
๐ Use comparison operators (=, >, <) and logical operators (AND, OR)
๐ Handle NULL values with IS NULL and IS NOT NULL
๐ Transforming Data
๐ Sort data with ORDER BY
๐ Create calculated columns with AS and use arithmetic operators (+, -, *, /)
๐ Use CASE WHEN for conditional expressions
๐ Aggregation & Grouping
๐ Summarize data with aggregation functions: SUM, COUNT, AVG, MIN, MAX
๐ Group data with GROUP BY and filter groups with HAVING
๐ Mastering Joins
๐ Combine tables with JOIN (INNER, LEFT, RIGHT, FULL OUTER)
๐ Understand primary and foreign keys to create meaningful joins
๐ Use SELF JOIN for analyzing data within the same table
๐ Date & Time Data
๐ Convert dates and extract parts (year, month, day) with EXTRACT
๐ Perform time-based analysis using DATEDIFF and date functions
๐ Quick Exploratory Analysis
๐ Calculate statistics to understand data distributions
๐ Use GROUP BY with aggregation for category-based analysis
๐ Basic Data Visualizations (Optional)
๐ Integrate SQL with visualization tools (Power BI, Tableau)
๐ Create charts directly in SQL with certain extensions (like MySQL's built-in charts)
๐ช Advanced Query Handling
๐ Master subqueries and nested queries
๐ Use WITH (Common Table Expressions) for complex queries
๐ Window functions for running totals, moving averages, and rankings (ROW_NUMBER, RANK, LAG, LEAD)
๐ Optimize for Performance
๐ Index critical columns for faster querying
๐ Analyze query plans and use optimizations
๐ Limit result sets and avoid excessive joins for efficiency
๐ Practice Projects
๐ Use real datasets to perform SQL analysis
๐ Create a portfolio with case studies and projects
Here you can find SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Like this post if you need more ๐โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐ฑ Getting Started with SQL
๐ Install SQL database software (MySQL, PostgreSQL, or SQL Server)
๐ Set up your database environment and connect to your data
๐ Load & Explore Data
๐ Understand tables, rows, and columns
๐ Use SELECT to retrieve data and LIMIT to get a sample view
๐ Explore schema and table structure with DESCRIBE or SHOW COLUMNS
๐งน Data Filtering Essentials
๐ Filter data using WHERE clauses
๐ Use comparison operators (=, >, <) and logical operators (AND, OR)
๐ Handle NULL values with IS NULL and IS NOT NULL
๐ Transforming Data
๐ Sort data with ORDER BY
๐ Create calculated columns with AS and use arithmetic operators (+, -, *, /)
๐ Use CASE WHEN for conditional expressions
๐ Aggregation & Grouping
๐ Summarize data with aggregation functions: SUM, COUNT, AVG, MIN, MAX
๐ Group data with GROUP BY and filter groups with HAVING
๐ Mastering Joins
๐ Combine tables with JOIN (INNER, LEFT, RIGHT, FULL OUTER)
๐ Understand primary and foreign keys to create meaningful joins
๐ Use SELF JOIN for analyzing data within the same table
๐ Date & Time Data
๐ Convert dates and extract parts (year, month, day) with EXTRACT
๐ Perform time-based analysis using DATEDIFF and date functions
๐ Quick Exploratory Analysis
๐ Calculate statistics to understand data distributions
๐ Use GROUP BY with aggregation for category-based analysis
๐ Basic Data Visualizations (Optional)
๐ Integrate SQL with visualization tools (Power BI, Tableau)
๐ Create charts directly in SQL with certain extensions (like MySQL's built-in charts)
๐ช Advanced Query Handling
๐ Master subqueries and nested queries
๐ Use WITH (Common Table Expressions) for complex queries
๐ Window functions for running totals, moving averages, and rankings (ROW_NUMBER, RANK, LAG, LEAD)
๐ Optimize for Performance
๐ Index critical columns for faster querying
๐ Analyze query plans and use optimizations
๐ Limit result sets and avoid excessive joins for efficiency
๐ Practice Projects
๐ Use real datasets to perform SQL analysis
๐ Create a portfolio with case studies and projects
Here you can find SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Like this post if you need more ๐โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค5๐1
๐๐ผ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ฆ๐ค๐ ๐ณ๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ (๐ช๐ถ๐๐ต๐ผ๐๐ ๐๐ฒ๐๐๐ถ๐ป๐ด ๐ข๐๐ฒ๐ฟ๐๐ต๐ฒ๐น๐บ๐ฒ๐ฑ!)๐ง
Letโs be honest:
SQL seems simpleโฆ until JOINs, Subqueries, and Window Functions come crashing in.
But mastering SQL doesnโt have to be hard.
You just need the right roadmapโand thatโs exactly what this is.
Hereโs a 5-step SQL journey to go from beginner to job-ready analyst๐
๐น ๐ฆ๐๐ฒ๐ฝ ๐ญ: Nail the Basics (Learn to Think in SQL)
Start with the foundations:
โ SELECT, WHERE, ORDER BY
โ DISTINCT, LIMIT, BETWEEN, LIKE
โ COUNT, SUM, AVG, MIN, MAX
Practice with small tables to build confidence.
Use platforms like:
โก๏ธ W3Schools
โก๏ธ Modesql
โก๏ธ LeetCode (easy problems)
๐น ๐ฆ๐๐ฒ๐ฝ ๐ฎ: Understand GROUP BY and Aggregations (The Analystโs Superpower)
This is where real-world queries begin. Learn:
โ GROUP BY + HAVING
โ Combining GROUP BY with COUNT/AVG
โ Filtering aggregated data
Example:
"Find top 5 cities with the highest total sales in 2023"
Thatโs GROUP BY magic.
๐น ๐ฆ๐๐ฒ๐ฝ ๐ฏ: MASTER JOINS (Stop Getting Confused)
JOINS scare a lot of people. But theyโre just pattern-matching across tables.
Learn one by one:
โ INNER JOIN
โ LEFT JOIN
โ RIGHT JOIN
โ FULL OUTER JOIN
โ SELF JOIN
โ CROSS JOIN (rare, but good to know)
Visualize them using Venn diagrams or draw sample tablesโit helps!
๐น ๐ฆ๐๐ฒ๐ฝ ๐ฐ: Learn Subqueries and CTEs (Write Cleaner, Powerful SQL)
โ Subqueries: Query inside another query
โ CTEs (WITH clause): Cleaner and reusable queries
โ Use them to break down complex problems
CTEs = the secret sauce to writing queries recruiters love.
๐น ๐ฆ๐๐ฒ๐ฝ ๐ฑ: Level Up with Window Functions (Your Entry into Advanced SQL)
If you want to stand out, this is it:
โ ROW_NUMBER(), RANK(), DENSE_RANK()
โ LAG(), LEAD(), NTILE()
โ PARTITION BY and ORDER BY combo
Use these to:
โก๏ธ Find top N per group
โก๏ธ Track user behavior over time
โก๏ธ Do cohort analysis
You donโt need 100 LeetCode problems.
You need 10 real-world queries done deeply.
Keep it simple. Keep it useful.
Letโs be honest:
SQL seems simpleโฆ until JOINs, Subqueries, and Window Functions come crashing in.
But mastering SQL doesnโt have to be hard.
You just need the right roadmapโand thatโs exactly what this is.
Hereโs a 5-step SQL journey to go from beginner to job-ready analyst๐
๐น ๐ฆ๐๐ฒ๐ฝ ๐ญ: Nail the Basics (Learn to Think in SQL)
Start with the foundations:
โ SELECT, WHERE, ORDER BY
โ DISTINCT, LIMIT, BETWEEN, LIKE
โ COUNT, SUM, AVG, MIN, MAX
Practice with small tables to build confidence.
Use platforms like:
โก๏ธ W3Schools
โก๏ธ Modesql
โก๏ธ LeetCode (easy problems)
๐น ๐ฆ๐๐ฒ๐ฝ ๐ฎ: Understand GROUP BY and Aggregations (The Analystโs Superpower)
This is where real-world queries begin. Learn:
โ GROUP BY + HAVING
โ Combining GROUP BY with COUNT/AVG
โ Filtering aggregated data
Example:
"Find top 5 cities with the highest total sales in 2023"
Thatโs GROUP BY magic.
๐น ๐ฆ๐๐ฒ๐ฝ ๐ฏ: MASTER JOINS (Stop Getting Confused)
JOINS scare a lot of people. But theyโre just pattern-matching across tables.
Learn one by one:
โ INNER JOIN
โ LEFT JOIN
โ RIGHT JOIN
โ FULL OUTER JOIN
โ SELF JOIN
โ CROSS JOIN (rare, but good to know)
Visualize them using Venn diagrams or draw sample tablesโit helps!
๐น ๐ฆ๐๐ฒ๐ฝ ๐ฐ: Learn Subqueries and CTEs (Write Cleaner, Powerful SQL)
โ Subqueries: Query inside another query
โ CTEs (WITH clause): Cleaner and reusable queries
โ Use them to break down complex problems
CTEs = the secret sauce to writing queries recruiters love.
๐น ๐ฆ๐๐ฒ๐ฝ ๐ฑ: Level Up with Window Functions (Your Entry into Advanced SQL)
If you want to stand out, this is it:
โ ROW_NUMBER(), RANK(), DENSE_RANK()
โ LAG(), LEAD(), NTILE()
โ PARTITION BY and ORDER BY combo
Use these to:
โก๏ธ Find top N per group
โก๏ธ Track user behavior over time
โก๏ธ Do cohort analysis
You donโt need 100 LeetCode problems.
You need 10 real-world queries done deeply.
Keep it simple. Keep it useful.
โค10
Top 20 #SQL INTERVIEW QUESTIONS
1๏ธโฃ Explain Order of Execution of SQL query
2๏ธโฃ Provide a use case for each of the functions Rank, Dense_Rank & Row_Number ( ๐ก majority struggle )
3๏ธโฃ Write a query to find the cumulative sum/Running Total
4๏ธโฃ Find the Most selling product by sales/ highest Salary of employees
5๏ธโฃ Write a query to find the 2nd/nth highest Salary of employees
6๏ธโฃ Difference between union vs union all
7๏ธโฃ Identify if there any duplicates in a table
8๏ธโฃ Scenario based Joins question, understanding of Inner, Left and Outer Joins via simple yet tricky question
9๏ธโฃ LAG, write a query to find all those records where the transaction value is greater then previous transaction value
1๏ธโฃ 0๏ธโฃ Rank vs Dense Rank, query to find the 2nd highest Salary of employee
( Ideal soln should handle ties)
1๏ธโฃ 1๏ธโฃ Write a query to find the Running Difference (Ideal sol'n using windows function)
1๏ธโฃ 2๏ธโฃ Write a query to display year on year/month on month growth
1๏ธโฃ 3๏ธโฃ Write a query to find rolling average of daily sign-ups
1๏ธโฃ 4๏ธโฃ 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๏ธโฃ 5๏ธโฃ Write a query to find the cumulative sum using self join
(you can use windows function to solve this question)
1๏ธโฃ6๏ธโฃ Differentiate between a clustered index and a non-clustered index?
1๏ธโฃ7๏ธโฃ What is a Candidate key?
1๏ธโฃ8๏ธโฃWhat is difference between Primary key and Unique key?
1๏ธโฃ9๏ธโฃWhat's the difference between RANK & DENSE_RANK in SQL?
2๏ธโฃ0๏ธโฃ Whats the difference between LAG & LEAD in SQL?
Access SQL Learning Series for Free: https://t.iss.one/sqlspecialist/523
Hope it helps :)
1๏ธโฃ Explain Order of Execution of SQL query
2๏ธโฃ Provide a use case for each of the functions Rank, Dense_Rank & Row_Number ( ๐ก majority struggle )
3๏ธโฃ Write a query to find the cumulative sum/Running Total
4๏ธโฃ Find the Most selling product by sales/ highest Salary of employees
5๏ธโฃ Write a query to find the 2nd/nth highest Salary of employees
6๏ธโฃ Difference between union vs union all
7๏ธโฃ Identify if there any duplicates in a table
8๏ธโฃ Scenario based Joins question, understanding of Inner, Left and Outer Joins via simple yet tricky question
9๏ธโฃ LAG, write a query to find all those records where the transaction value is greater then previous transaction value
1๏ธโฃ 0๏ธโฃ Rank vs Dense Rank, query to find the 2nd highest Salary of employee
( Ideal soln should handle ties)
1๏ธโฃ 1๏ธโฃ Write a query to find the Running Difference (Ideal sol'n using windows function)
1๏ธโฃ 2๏ธโฃ Write a query to display year on year/month on month growth
1๏ธโฃ 3๏ธโฃ Write a query to find rolling average of daily sign-ups
1๏ธโฃ 4๏ธโฃ 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๏ธโฃ 5๏ธโฃ Write a query to find the cumulative sum using self join
(you can use windows function to solve this question)
1๏ธโฃ6๏ธโฃ Differentiate between a clustered index and a non-clustered index?
1๏ธโฃ7๏ธโฃ What is a Candidate key?
1๏ธโฃ8๏ธโฃWhat is difference between Primary key and Unique key?
1๏ธโฃ9๏ธโฃWhat's the difference between RANK & DENSE_RANK in SQL?
2๏ธโฃ0๏ธโฃ Whats the difference between LAG & LEAD in SQL?
Access SQL Learning Series for Free: https://t.iss.one/sqlspecialist/523
Hope it helps :)
โค3๐2
5 Key SQL Aggregate Functions for data analyst
๐SUM(): Adds up all the values in a numeric column.
๐AVG(): Calculates the average of a numeric column.
๐COUNT(): Counts the total number of rows or non-NULL values in a column.
๐MAX(): Returns the highest value in a column.
๐MIN(): Returns the lowest value in a column.
๐SUM(): Adds up all the values in a numeric column.
๐AVG(): Calculates the average of a numeric column.
๐COUNT(): Counts the total number of rows or non-NULL values in a column.
๐MAX(): Returns the highest value in a column.
๐MIN(): Returns the lowest value in a column.
โค2
Want to become a Data Scientist?
Hereโs 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 ๐๐
Hereโs 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 ๐๐
โค6
โ
Essential NLP Techniques Every Data Scientist Should Know ๐ ๐
These NLP techniques are crucial for extracting insights from text and building intelligent applications.
1๏ธโฃ Tokenization: Breaking Down Text ๐งฉ
- Split text into individual units (words, phrases, symbols).
- Essential for preparing text for analysis.
2๏ธโฃ Stop Word Removal: Clearing the Clutter ๐ซ
- Remove common words (e.g., "the," "a," "is") that don't carry much meaning.
- Helps focus on important content words.
3๏ธโฃ Stemming & Lemmatization: Reducing to the Root ๐ณ
- 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๏ธโฃ Named Entity Recognition (NER): Spotting the Key Players ๐ค
- Identify and classify named entities (people, organizations, locations, dates).
- Useful for extracting structured information.
5๏ธโฃ TF-IDF: Identifying Important Words โ๏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๏ธโฃ Bag of Words: Representing Text Numerically ๐ข
- Create a vector representation of text based on word counts.
- Useful for machine learning algorithms that require numerical input.
๐ก Master these techniques to analyze text, classify documents, and build NLP models.
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These NLP techniques are crucial for extracting insights from text and building intelligent applications.
1๏ธโฃ Tokenization: Breaking Down Text ๐งฉ
- Split text into individual units (words, phrases, symbols).
- Essential for preparing text for analysis.
2๏ธโฃ Stop Word Removal: Clearing the Clutter ๐ซ
- Remove common words (e.g., "the," "a," "is") that don't carry much meaning.
- Helps focus on important content words.
3๏ธโฃ Stemming & Lemmatization: Reducing to the Root ๐ณ
- 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๏ธโฃ Named Entity Recognition (NER): Spotting the Key Players ๐ค
- Identify and classify named entities (people, organizations, locations, dates).
- Useful for extracting structured information.
5๏ธโฃ TF-IDF: Identifying Important Words โ๏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๏ธโฃ Bag of Words: Representing Text Numerically ๐ข
- Create a vector representation of text based on word counts.
- Useful for machine learning algorithms that require numerical input.
๐ก Master these techniques to analyze text, classify documents, and build NLP models.
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โค4
Planning for Data Science or Data Engineering Interview.
Focus on SQL & Python first. Here are some important questions which you should know.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
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.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
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 ๐๐
Focus on SQL & Python first. Here are some important questions which you should know.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
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.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
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 ๐๐
โค4
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 โค๏ธ for more
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 โค๏ธ for more
โค6๐1