Essential Python Libraries for Data Analytics 😄👇

Python Free Resources: https://t.iss.one/pythondevelopersindia

1. NumPy:
- Efficient numerical operations and array manipulation.

2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).

3. Matplotlib:
- 2D plotting library for creating visualizations.

4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.

5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.

6. PyTorch:
- Deep learning library, particularly popular for neural network research.

7. Django:
- High-level web framework for building robust, scalable web applications.

8. Flask:
- Lightweight web framework for building smaller web applications and APIs.

9. Requests:
- HTTP library for making HTTP requests.

10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.

As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.

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Complete SQL Topics for Data Analysts 😄👇

1. Introduction to SQL:
- Basic syntax and structure
- Understanding databases and tables

2. Querying Data:
- SELECT statement
- Filtering data using WHERE clause
- Sorting data with ORDER BY

3. Joins:
- INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
- Combining data from multiple tables

4. Aggregation Functions:
- GROUP BY
- Aggregate functions like COUNT, SUM, AVG, MAX, MIN

5. Subqueries:
- Using subqueries in SELECT, WHERE, and HAVING clauses

6. Data Modification:
- INSERT, UPDATE, DELETE statements
- Transactions and Rollback

7. Data Types and Constraints:
- Understanding various data types (e.g., INT, VARCHAR)
- Using constraints (e.g., PRIMARY KEY, FOREIGN KEY)

8. Indexes:
- Creating and managing indexes for performance optimization

9. Views:
- Creating and using views for simplified querying

10. Stored Procedures and Functions:
- Writing and executing stored procedures
- Creating and using functions

11. Normalization:
- Understanding database normalization concepts

12. Data Import and Export:
- Importing and exporting data using SQL

13. Window Functions:
- ROW_NUMBER(), RANK(), DENSE_RANK(), and others

14. Advanced Filtering:
- Using CASE statements for conditional logic

15. Advanced Join Techniques:
- Self-joins and other advanced join scenarios

16. Analytical Functions:
- LAG(), LEAD(), OVER() for advanced analytics

17. Working with Dates and Times:
- Date and time functions and formatting

18. Performance Tuning:
- Query optimization strategies

19. Security:
- Understanding SQL injection and best practices for security

20. Handling NULL Values:
- Dealing with NULL values in queries

Ensure hands-on practice on these topics to strengthen your SQL skills.

Since SQL is one of the most essential skill for data analysts, I have decided to teach each topic daily in this channel for free. Like this post if you want me to continue this SQL series 👍♥️

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Thanks for the amazing response guys 😄

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Let's start with the first topic:

### 1. Introduction to SQL:
SQL (Structured Query Language) is a programming language designed for managing and querying relational databases. It provides a standardized way to interact with databases. The basic structure of an SQL query involves:

SELECT column1, column2 FROM table_name WHERE condition;

- `SELECT: Specifies the columns to retrieve.
- FROM: Specifies the table from which to retrieve the data.
- WHERE: Filters the rows based on a condition.

Example:
``sql
SELECT first_name, last_name FROM employees WHERE department = 'IT';
`

This query retrieves the first name and last name of employees working in the IT department.

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SQL Learning Series Part-2

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Querying Data

Now that we understand the basic structure, let's delve into querying data with more detail.

#### SELECT Statement:
The SELECT statement retrieves data from one or more tables. You can select specific columns or use * to select all columns.

-- Selecting specific columns
SELECT column1, column2 FROM table_name;

-- Selecting all columns
SELECT * FROM table_name;

#### Filtering Data with WHERE:
The WHERE clause filters rows based on a specified condition.

SELECT column1, column2 FROM table_name WHERE condition;

Example:

SELECT product_name, price FROM products WHERE category = 'Electronics';

This query retrieves the product names and prices for items in the 'Electronics' category.

#### Sorting Data with ORDER BY:
The ORDER BY clause sorts the result set based on one or more columns.

SELECT column1, column2 FROM table_name ORDER BY column1 [ASC|DESC];

Example:

SELECT product_name, price FROM products ORDER BY price DESC;

This query sorts products by price in descending order.

Understanding these fundamentals is crucial for effective data retrieval.

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SQL Learning Series Part-3

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Today, we will learn about Joins in more detail.

Joins allow you to combine rows from two or more tables based on related columns. There are several types of joins:

#### INNER JOIN:
Returns rows when there is a match in both tables.

SELECT column1, column2 FROM table1 INNER JOIN table2 ON table1.column = table2.column;

#### LEFT JOIN (or LEFT OUTER JOIN):
Returns all rows from the left table and matching rows from the right table.

SELECT column1, column2 FROM table1 LEFT JOIN table2 ON table1.column = table2.column;

#### RIGHT JOIN (or RIGHT OUTER JOIN):
Returns all rows from the right table and matching rows from the left table.

SELECT column1, column2 FROM table1 RIGHT JOIN table2 ON table1.column = table2.column;

#### FULL JOIN (or FULL OUTER JOIN):
Returns all rows when there is a match in either table.

SELECT column1, column2 FROM table1 FULL JOIN table2 ON table1.column = table2.column;

Joins are powerful for combining data from different sources.

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SQL LEARNING SERIES PART-4

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Today, we will learn about Aggregate Functions

Aggregation functions perform calculations on sets of values and return a single result. Common aggregation functions include:

#### COUNT():
Counts the number of rows in a result set.

SELECT COUNT(column) FROM table;

#### SUM():
Calculates the sum of values in a column.

SELECT SUM(column) FROM table;

#### AVG():
Calculates the average value of a numeric column.

SELECT AVG(column) FROM table;

#### MAX():
Returns the maximum value in a column.

SELECT MAX(column) FROM table;

#### MIN():
Returns the minimum value in a column.

SELECT MIN(column) FROM table;

Example:

SELECT COUNT(order_id), AVG(total_amount) FROM orders WHERE customer_id = 123;

This query counts the number of orders and calculates the average total amount for a specific customer.

Understanding aggregation is crucial for summarizing and analyzing data.

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SQL LEARNING SERIES PART-5

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Today, we will learn about Subqueries

Subqueries, also known as nested queries, allow you to use the result of one query within another query. There are different types of subqueries:

#### Subquery in SELECT:
Using a subquery to retrieve a single value.

SELECT column1, (SELECT column2 FROM table2 WHERE condition) AS subquery_result FROM table1;

#### Subquery in WHERE:
Filtering based on the result of a subquery.

SELECT column1 FROM table1 WHERE column2 = (SELECT column3 FROM table2 WHERE condition);

#### Subquery in HAVING:
Filtering aggregated results with a subquery.

SELECT column1, COUNT(column2) FROM table1 GROUP BY column1 HAVING COUNT(column2) > (SELECT threshold FROM settings);

Subqueries are useful for complex queries and can be employed in various parts of a statement.

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Thanks for the amazing response guys. I will continue posting SQL learning series as SQL is one of the Essential topic for data analysts. Meanwhile I will parallely start learning series for python, excel, tableau & power bi as well in coming days :)

Technical Skills Required to become a data analyst 😄👇

Tool 1: MS-Excel (Google sheets knowledge is a plus)

👉 Lookups (vlookup, xlookup, hlookup and its use cases)
👉 Pivot tables, Pivot charts
👉 Power Query, Power Pivot
👉 Conditional formatting
👉 Various charts and its formatting
👉 Basic VBA/Macro
👉 Major Excel functions/formulas (text, numeric, logical functions)

Tool 2: SQL (with any one RDBMS tool)

👉 Database fundamentals (primary key, foreign key, relationships, cardinality, etc.)
👉 DDL, DML statements (commonly used ones)
👉 Basic Select queries (single table queries)
👉 Joins and Unions (multiple table queries)
👉 Subqueries and CTEs
👉 Window functions (Rank, DenseRank, RowNumber, Lead, Lag)
👉 Views and Stored Procedures
👉 SQL Server/MySQL/PostGreSQL (any one RDBMS)
👉 Complete Roadmap for SQL

Tool 3: Power BI (equivalent topics in Tableau)

👉 Power Query, Power Pivot (data cleaning and modelling)
👉 Basic M-language and Intermediate DAX functions
👉 Filter and row context
👉 Measures and calculated columns
👉 Data modelling basics (with best practices)
👉 Types of charts/visuals (and its use cases)
👉 Bookmarks, Filters/Slicers (for creating buttons/page navigation)
👉 Advanced Tooltips, Drill through feature
👉 Power BI service basics (schedule refresh, license types, workspace roles, etc.)
👉 Power BI Interview Questions

Tool 4: Python (equivalent topics in R)

👉 Python basic syntax
👉 Python libraries/IDEs (Jupyter notebook)
👉 Pandas
👉 Numpy
👉 Matplotlib
👉 Scikitlearn

You may learn a combination of any 3 of these tools to secure an entry-level role and then upskill on the 4th one after getting a job.

➡ Excel + SQL + Power BI/ Tableau + Python/ R

So, in my learning series, I will focus on these tools mostly.

If we get time, I'll also try to cover other essential Topics like Statistics, Data Portfolio, etc.

Obviously everything will be free of cost.

Stay tuned for free learning

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1100+ wanted to continue learning SQL, so here you go 😄

SQL LEARNING SERIES PART-6

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Today we will learn about Data Modification:

Let's explore how to modify data within a database using SQL. There are three main operations: INSERT, UPDATE, and DELETE.

#### INSERT Statement:
Adds new rows of data into a table.

INSERT INTO table_name (column1, column2) VALUES (value1, value2);

Example:

INSERT INTO employees (first_name, last_name, department) VALUES ('John', 'Doe', 'HR');

#### UPDATE Statement:
Modifies existing data in a table.

UPDATE table_name SET column1 = value1, column2 = value2 WHERE condition;

Example:

UPDATE employees SET department = 'Finance' WHERE last_name = 'Doe';

#### DELETE Statement:
Removes rows from a table based on a condition.

DELETE FROM table_name WHERE condition;

Example:

DELETE FROM employees WHERE last_name = 'Doe';

Ensure caution when performing UPDATE and DELETE operations to avoid unintended consequences.

This is a bit tricky but important concept.

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SQL LEARNING SERIES PART-7

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Today we will learn about Data Types and Constraints:

Understanding data types and constraints is crucial for designing a well-structured database.

#### Data Types:
SQL supports various data types, such as INT, VARCHAR, DATE, and more. Each column in a table must be assigned a specific data type.

CREATE TABLE table_name (
    column1 INT,
    column2 VARCHAR(50),
    column3 DATE
);

#### Constraints:
Constraints enforce rules on the data in a table. Common constraints include:

- PRIMARY KEY: Uniquely identifies each record in a table.
- FOREIGN KEY: Establishes a link between two tables.
- NOT NULL: Ensures a column cannot have NULL values.
- UNIQUE: Ensures all values in a column are different.

Example:

CREATE TABLE employees (
    employee_id INT PRIMARY KEY,
    first_name VARCHAR(50) NOT NULL,
    last_name VARCHAR(50) NOT NULL,
    department_id INT,
    FOREIGN KEY (department_id) REFERENCES departments(department_id)
);

This creates a table of employees with a primary key, non-null first and last names, and a foreign key linking to the departments table.

Understanding and implementing data types and constraints contribute to a well-designed and efficient database.

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SQL LEARNING SERIES PART-8

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Today we will learn about Indexes:

Indexes are crucial for optimizing the performance of database queries by allowing faster retrieval of data. They work similarly to the index of a book, making it quicker to find specific information.

#### Creating Indexes:

CREATE INDEX index_name ON table_name (column1, column2, ...);

Indexes can be created on one or multiple columns.

#### Removing Indexes:

DROP INDEX index_name ON table_name;

Indexes should be used judiciously, as they consume additional storage space and can impact the performance of write operations.

Optimizing queries often involves balancing the use of indexes to speed up read operations without significantly affecting write performance.

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SQL LEARNING SERIES PART-9

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Today we will learn about Views:

Views in SQL are virtual tables based on the result of a SELECT query. They provide a way to simplify complex queries and encapsulate logic.

#### Creating a View:

CREATE VIEW view_name AS
SELECT column1, column2 FROM table1 WHERE condition;

#### Querying a View:
Once created, you can treat a view like a regular table in your queries.

SELECT * FROM view_name;

#### Updating a View:
Views can be updated if they are based on simple SELECT statements.

CREATE OR REPLACE VIEW view_name AS
SELECT new_column1, new_column2 FROM new_table WHERE new_condition;

Views are useful for abstracting complex queries and enhancing the security of sensitive data.

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SQL LEARNING SERIES PART-10

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Today we will learn about Stored Procedures and Functions:

Stored procedures and functions are precompiled and stored in the database, providing a way to encapsulate and reuse logic on the server side.

#### Stored Procedures:
A stored procedure is a set of SQL statements that can be executed as a single unit.

CREATE PROCEDURE procedure_name
AS
BEGIN
    -- SQL statements
END;

#### Executing a Stored Procedure:

EXEC procedure_name;

#### Functions:
A function returns a value based on input parameters. There are two types: scalar functions and table-valued functions.

CREATE FUNCTION function_name (@param1 INT, @param2 VARCHAR(50))
RETURNS INT
AS
BEGIN
    -- SQL statements
    RETURN some_value;
END;

#### Calling a Function:

SELECT dbo.function_name(param1, param2);

Stored procedures and functions enhance code modularity and maintainability. They are valuable for implementing business logic on the database side.

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SQL LEARNING SERIES PART-11

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Let's also learn about Normalization today:

Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. There are different normal forms, each with specific rules:

#### First Normal Form (1NF):
- Each column contains atomic (indivisible) values.
- There are no repeating groups or arrays.

#### Second Normal Form (2NF):
- Meets the requirements of 1NF.
- All non-key columns are fully functionally dependent on the primary key.

#### Third Normal Form (3NF):
- Meets the requirements of 2NF.
- Eliminates transitive dependencies, where non-key columns depend on other non-key columns.

#### Example:
Consider a denormalized table:

CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    customer_name VARCHAR(50),
    product_name VARCHAR(50),
    price DECIMAL(10, 2)
);

Normalized to 3NF:

CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    customer_id INT,
    product_id INT,
    order_date DATE,
    quantity INT,
    total_price DECIMAL(10, 2)
);

CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    customer_name VARCHAR(50)
);

CREATE TABLE products (
    product_id INT PRIMARY KEY,
    product_name VARCHAR(50),
    price DECIMAL(10, 2)
);

Normalization helps avoid data anomalies and ensures efficient database design.

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SQL LEARNING SERIES PART-12

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Today, we will learn about Data Import and Export:

SQL provides mechanisms to import data into a database or export it to external files.

#### Importing Data:

-- Using INSERT INTO SELECT to import data from one table to another
INSERT INTO destination_table (column1, column2)
SELECT column3, column4 FROM source_table;

#### Exporting Data:

-- Using SELECT INTO OUTFILE to export data to a file
SELECT column1, column2 INTO OUTFILE 'file_path.csv'
FIELDS TERMINATED BY ',' FROM table_name;

These operations are useful for transferring data between databases, archiving, or exchanging information with other systems.

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SQL LEARNING SERIES PART-13

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Let's also learn about Window Functions today:

Window functions perform calculations across a set of table rows related to the current row. They are particularly useful for analytics and reporting.

#### ROW_NUMBER():
Assigns a unique number to each row within a partition of a result set.

SELECT column1, column2, ROW_NUMBER() OVER (PARTITION BY column3 ORDER BY column4) AS row_num
FROM table_name;

#### RANK(), DENSE_RANK():
Assign ranks to rows based on a specified column, with optional handling of ties.

SELECT column1, column2, RANK() OVER (ORDER BY column3) AS rank_num
FROM table_name;

#### LEAD(), LAG():
Access data from subsequent or previous rows within the result set.

SELECT column1, column2, LEAD(column2) OVER (ORDER BY column1) AS next_value
FROM table_name;

Window functions provide powerful capabilities for comparative and sequential analysis in a dataset.

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SQL LEARNING SERIES PART-14

Complete SQL Topics for Data Analysis
-> https://t.iss.one/sqlspecialist/523

Today, we will learn about Advanced Filtering:

Advanced filtering in SQL involves using CASE statements for conditional logic within queries.

#### CASE Statement:
Allows conditional logic in a query, similar to a switch statement in other programming languages.

SELECT column1, column2,
    CASE
        WHEN condition1 THEN 'Result1'
        WHEN condition2 THEN 'Result2'
        ELSE 'DefaultResult'
    END AS custom_column
FROM table_name;

#### Example:

SELECT product_name, price,
    CASE
        WHEN price > 1000 THEN 'Expensive'
        WHEN price BETWEEN 500 AND 1000 THEN 'Moderate'
        ELSE 'Affordable'
    END AS price_category
FROM products;

This query categorizes products based on their price into 'Expensive', 'Moderate', or 'Affordable'.

Advanced filtering is useful for creating custom columns based on specific conditions in your data.

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