WhatsApp is no longer a platform just for chat.

It's an educational goldmine.

If you do, you’re sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.

I have curated the list of best WhatsApp channels to learn coding & data science for FREE

Free Courses with Certificate
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ENJOY LEARNING 👍👍

Business Analyst vs Data Analyst

SQL From Basic to Advanced level

Basic SQL is ONLY 7 commands:
- SELECT
- FROM
- WHERE (also use SQL comparison operators such as =, <=, >=, <> etc.)
- ORDER BY
- Aggregate functions such as SUM, AVERAGE, COUNT etc.
- GROUP BY
- CREATE, INSERT, DELETE, etc.
You can do all this in just one morning.

Once you know these, take the next step and learn commands like:
- LEFT JOIN
- INNER JOIN
- LIKE
- IN
- CASE WHEN
- HAVING (undertstand how it's different from GROUP BY)
- UNION ALL
This should take another day.

Once both basic and intermediate are done, start learning more advanced SQL concepts such as:
- Subqueries (when to use subqueries vs CTE?)
- CTEs (WITH AS)
- Stored Procedures
- Triggers
- Window functions (LEAD, LAG, PARTITION BY, RANK, DENSE RANK)
These can be done in a couple of days.
Learning these concepts is NOT hard at all

- what takes time is practice and knowing what command to use when. How do you master that?
- First, create a basic SQL project
- Then, work on an intermediate SQL project (search online) -

Lastly, create something advanced on SQL with many CTEs, subqueries, stored procedures and triggers etc.

This is ALL you need to become a badass in SQL, and trust me when I say this, it is not rocket science. It's just logic.

Remember that practice is the key here. It will be more clear and perfect with the continous practice

Best telegram channel to learn SQL: https://t.iss.one/sqlanalyst

Data Analyst Jobs👇
https://t.iss.one/jobs_SQL

Join @free4unow_backup for more free resources.

Like this post if it helps 😄❤️

ENJOY LEARNING 👍👍

If you're a data science beginner, Python is the best programming language to get started.

Here are 7 Python libraries for data science you need to know if you want to learn:

- Data analysis
- Data visualization
- Machine learning
- Deep learning

NumPy

NumPy is a library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

Pandas

Widely used library for data manipulation and analysis, offering data structures like DataFrame and Series that simplify handling of structured data and performing tasks such as filtering, grouping, and merging.

Matplotlib

Powerful plotting library for creating static, interactive, and animated visualizations in Python, enabling data scientists to generate a wide variety of plots, charts, and graphs to explore and communicate data effectively.

Scikit-learn

Comprehensive machine learning library that includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection, as well as utilities for data preprocessing and evaluation.

Seaborn

Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics, making it easier to generate complex visualizations with minimal code.

TensorFlow or PyTorch

TensorFlow, Keras, or PyTorch are three prominent deep learning frameworks utilized by data scientists to construct, train, and deploy neural networks for various applications, each offering distinct advantages and capabilities tailored to different preferences and requirements.

SciPy

Collection of mathematical algorithms and functions built on top of NumPy, providing additional capabilities for optimization, integration, interpolation, signal processing, linear algebra, and more, which are commonly used in scientific computing and data analysis workflows.

Enjoy 😄👍

Many people pay too much to learn SQL, but my mission is to break down barriers. I have shared complete learning series to learn SQL from scratch.

Here are the links to the SQL series

Complete SQL Topics for Data Analyst: https://t.iss.one/sqlspecialist/523

Part-1: https://t.iss.one/sqlspecialist/524

Part-2: https://t.iss.one/sqlspecialist/525

Part-3: https://t.iss.one/sqlspecialist/526

Part-4: https://t.iss.one/sqlspecialist/527

Part-5: https://t.iss.one/sqlspecialist/529

Part-6: https://t.iss.one/sqlspecialist/534

Part-7: https://t.iss.one/sqlspecialist/534

Part-8: https://t.iss.one/sqlspecialist/536

Part-9: https://t.iss.one/sqlspecialist/537

Part-10: https://t.iss.one/sqlspecialist/539

Part-11: https://t.iss.one/sqlspecialist/540

Part-12:
https://t.iss.one/sqlspecialist/541

Part-13: https://t.iss.one/sqlspecialist/542

Part-14: https://t.iss.one/sqlspecialist/544

Part-15: https://t.iss.one/sqlspecialist/545

Part-16: https://t.iss.one/sqlspecialist/546

Part-17: https://t.iss.one/sqlspecialist/549

Part-18: https://t.iss.one/sqlspecialist/552

Part-19: https://t.iss.one/sqlspecialist/555

Part-20: https://t.iss.one/sqlspecialist/556

I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.

But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.

Complete Python Topics for Data Analysts: https://t.iss.one/sqlspecialist/548

Complete Excel Topics for Data Analysts: https://t.iss.one/sqlspecialist/547

I'll continue with learning series on Python, Power BI, Excel & Tableau.

Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.

Hope it helps :)

SQL Commands ✅

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 👍♥️

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

Hope it helps :)