Data Analyst Resume Template-
https://www.dayjob.com/downloads/CV_examples/data_analyst_CV_template.pdf
Kaggle exploratory data analysis
* Notebooks:
https://www.kaggle.com/notebooks
* Datasets:
https://www.kaggle.com/datasets
Project ideas:
Alex the Analyst Portfolio Project Series:
https://www.youtube.com/watch?v=qfyynHBFOsM&list=PLUaB-1hjhk8H48Pj32z4GZgGWyylqv85f&t=0s
https://www.dayjob.com/downloads/CV_examples/data_analyst_CV_template.pdf
Kaggle exploratory data analysis
* Notebooks:
https://www.kaggle.com/notebooks
* Datasets:
https://www.kaggle.com/datasets
Project ideas:
Alex the Analyst Portfolio Project Series:
https://www.youtube.com/watch?v=qfyynHBFOsM&list=PLUaB-1hjhk8H48Pj32z4GZgGWyylqv85f&t=0s
❤2
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 Resources👇
https://t.iss.one/DataSimplifier
Like this post if you need more 👍❤️
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 Resources👇
https://t.iss.one/DataSimplifier
Like this post if you need more 👍❤️
Hope it helps :)
❤5
Top 10 important data science concepts
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
❤3🥰1
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 Resources👇
https://t.iss.one/DataSimplifier
Like this post if you need more 👍❤️
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 Resources👇
https://t.iss.one/DataSimplifier
Like this post if you need more 👍❤️
Hope it helps :)
❤1
How do you start AI and ML ?
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.iss.one/machinelearning_deeplearning
Like for more ❤️
All the best 👍👍
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.iss.one/machinelearning_deeplearning
Like for more ❤️
All the best 👍👍
❤1
Top 10 machine Learning algorithms
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
❤2
Creative ways to craft your data analytics portfolio
Free Data sets for Data Analytics Projects: https://t.iss.one/DataPortfolio
1. Storytelling with Data Projects: Craft narratives around real-world scenarios, demonstrating your ability to extract insights from data. Use visuals, such as charts and graphs, to make your analysis more engaging.
2. Interactive Dashboards: Build interactive dashboards using tools like Tableau or Power BI. Showcase your skills in creating user-friendly interfaces that allow for dynamic exploration of data.
3. Predictive Modeling Showcase: Develop projects that involve predictive modeling, such as machine learning algorithms. Highlight your ability to make data-driven predictions and explain the implications of your findings.
4. Data Visualization Blog: Start a blog to share your insights and showcase your projects. Explain your analysis process, display visualizations, and discuss the impact of your findings. This demonstrates your ability to communicate complex ideas.
5. Open Source Contributions: Contribute to data-related open-source projects on platforms like GitHub. This not only adds to your portfolio but also demonstrates collaboration skills and engagement with the broader data science community.
6. Kaggle Competitions: Participate in Kaggle competitions and document your approach and results. Employ a variety of algorithms and techniques to solve different types of problems, showcasing your versatility.
7. Industry-specific Analyses: Tailor projects to specific industries of interest. For example, analyze trends in healthcare, finance, or marketing. This demonstrates your understanding of domain-specific challenges and your ability to provide actionable insights.
8. Portfolio Website: Create a professional portfolio website to showcase your projects. Include project descriptions, methodologies, visualizations, and the impact of your analyses. Make it easy for potential employers to navigate and understand your work.
9. Skill Diversification: Showcase a range of skills by incorporating data cleaning, feature engineering, and other pre-processing steps into your projects. Highlighting a holistic approach to data analysis enhances your portfolio.
10. Continuous Learning Projects: Demonstrate your commitment to ongoing learning by including projects that showcase new tools, techniques, or methodologies you've recently acquired. This shows adaptability and a proactive attitude toward staying current in the field.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Free Data sets for Data Analytics Projects: https://t.iss.one/DataPortfolio
1. Storytelling with Data Projects: Craft narratives around real-world scenarios, demonstrating your ability to extract insights from data. Use visuals, such as charts and graphs, to make your analysis more engaging.
2. Interactive Dashboards: Build interactive dashboards using tools like Tableau or Power BI. Showcase your skills in creating user-friendly interfaces that allow for dynamic exploration of data.
3. Predictive Modeling Showcase: Develop projects that involve predictive modeling, such as machine learning algorithms. Highlight your ability to make data-driven predictions and explain the implications of your findings.
4. Data Visualization Blog: Start a blog to share your insights and showcase your projects. Explain your analysis process, display visualizations, and discuss the impact of your findings. This demonstrates your ability to communicate complex ideas.
5. Open Source Contributions: Contribute to data-related open-source projects on platforms like GitHub. This not only adds to your portfolio but also demonstrates collaboration skills and engagement with the broader data science community.
6. Kaggle Competitions: Participate in Kaggle competitions and document your approach and results. Employ a variety of algorithms and techniques to solve different types of problems, showcasing your versatility.
7. Industry-specific Analyses: Tailor projects to specific industries of interest. For example, analyze trends in healthcare, finance, or marketing. This demonstrates your understanding of domain-specific challenges and your ability to provide actionable insights.
8. Portfolio Website: Create a professional portfolio website to showcase your projects. Include project descriptions, methodologies, visualizations, and the impact of your analyses. Make it easy for potential employers to navigate and understand your work.
9. Skill Diversification: Showcase a range of skills by incorporating data cleaning, feature engineering, and other pre-processing steps into your projects. Highlighting a holistic approach to data analysis enhances your portfolio.
10. Continuous Learning Projects: Demonstrate your commitment to ongoing learning by including projects that showcase new tools, techniques, or methodologies you've recently acquired. This shows adaptability and a proactive attitude toward staying current in the field.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤2⚡1
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 -------------------
Some good resources to learn SQL
1.Tutorial & Courses
• Learn SQL: https://bit.ly/3FxxKPz
• Udacity: imp.i115008.net/AoAg7K
2. YouTube Channel's
• FreeCodeCamp:rb.gy/pprz73
• Programming with Mosh: rb.gy/g62hpe
3. Books
• SQL in a Nutshell: https://t.iss.one/DataAnalystInterview/158
4. SQL Interview Questions
https://t.iss.one/sqlanalyst/72?single
Join @free4unow_backup for more free resourses
ENJOY LEARNING 👍👍
👇👇
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 -------------------
Some good resources to learn SQL
1.Tutorial & Courses
• Learn SQL: https://bit.ly/3FxxKPz
• Udacity: imp.i115008.net/AoAg7K
2. YouTube Channel's
• FreeCodeCamp:rb.gy/pprz73
• Programming with Mosh: rb.gy/g62hpe
3. Books
• SQL in a Nutshell: https://t.iss.one/DataAnalystInterview/158
4. SQL Interview Questions
https://t.iss.one/sqlanalyst/72?single
Join @free4unow_backup for more free resourses
ENJOY LEARNING 👍👍
❤4
Machine Learning Algorithms every data scientist should know:
📌 Supervised Learning:
🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression
🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)
📌 Unsupervised Learning:
🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN
🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)
📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)
📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking
Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
📌 Supervised Learning:
🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression
🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)
📌 Unsupervised Learning:
🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN
🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)
📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)
📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking
Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
❤3