SQL best practices:
✔ Use EXISTS in place of IN wherever possible
✔ Use table aliases with columns when you are joining multiple tables
✔ Use GROUP BY instead of DISTINCT.
✔ Add useful comments wherever you write complex logic and avoid too many comments.
✔ Use joins instead of subqueries when possible for better performance.
✔ Use WHERE instead of HAVING to define filters on non-aggregate fields
✔ Avoid wildcards at beginning of predicates (something like '%abc' will cause full table scan to get the results)
✔ Considering cardinality within GROUP BY can make it faster (try to consider unique column first in group by list)
✔ Write SQL keywords in capital letters.
✔ Never use select *, always mention list of columns in select clause.
✔ Create CTEs instead of multiple sub queries , it will make your query easy to read.
✔ Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
✔ Never use order by in sub queries , It will unnecessary increase runtime.
✔ If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance
✔ Always start WHERE clause with 1 = 1.This has the advantage of easily commenting out conditions during debugging a query.
✔ Taking care of NULL values before using equality or comparisons operators. Applying window functions. Filtering the query before joining and having clause.
✔ Make sure the JOIN conditions among two table Join are either keys or Indexed attribute.
Hope it helps :)
✔ Use EXISTS in place of IN wherever possible
✔ Use table aliases with columns when you are joining multiple tables
✔ Use GROUP BY instead of DISTINCT.
✔ Add useful comments wherever you write complex logic and avoid too many comments.
✔ Use joins instead of subqueries when possible for better performance.
✔ Use WHERE instead of HAVING to define filters on non-aggregate fields
✔ Avoid wildcards at beginning of predicates (something like '%abc' will cause full table scan to get the results)
✔ Considering cardinality within GROUP BY can make it faster (try to consider unique column first in group by list)
✔ Write SQL keywords in capital letters.
✔ Never use select *, always mention list of columns in select clause.
✔ Create CTEs instead of multiple sub queries , it will make your query easy to read.
✔ Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
✔ Never use order by in sub queries , It will unnecessary increase runtime.
✔ If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance
✔ Always start WHERE clause with 1 = 1.This has the advantage of easily commenting out conditions during debugging a query.
✔ Taking care of NULL values before using equality or comparisons operators. Applying window functions. Filtering the query before joining and having clause.
✔ Make sure the JOIN conditions among two table Join are either keys or Indexed attribute.
Hope it helps :)
❤2👍2
𝗧𝗼𝗽 𝟱 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗜𝗻 𝟮𝟬𝟮𝟱 | 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 😍
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Acquire industry-relevant skills to grow in your career and stand out to prospective employers.
𝗔𝗜 & 𝗠𝗟 :- https://pdlink.in/3U3eZuq
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❤1
Complete Roadmap to learn SQL in 2025 👇👇
1. Basic Concepts
- Understand databases and SQL.
- Learn data types (INT, VARCHAR, DATE, etc.).
2. Basic Queries
- SELECT: Retrieve data.
- WHERE: Filter results.
- ORDER BY: Sort results.
- LIMIT: Restrict results.
3. Aggregate Functions
- COUNT, SUM, AVG, MAX, MIN.
- Use GROUP BY to group results.
4. Joins
- INNER JOIN: Combine rows from two tables based on a condition.
- LEFT JOIN: Include all rows from the left table.
- RIGHT JOIN: Include all rows from the right table.
- FULL OUTER JOIN: Include all rows from both tables.
5. Subqueries
- Use nested queries for complex data retrieval.
6. Data Manipulation
- INSERT: Add new records.
- UPDATE: Modify existing records.
- DELETE: Remove records.
7. Schema Management
- CREATE TABLE: Define new tables.
- ALTER TABLE: Modify existing tables.
- DROP TABLE: Remove tables.
8. Indexes
- Understand how to create and use indexes to optimize queries.
9. Views
- Create and manage views for simplified data access.
10. Transactions
- Learn about COMMIT and ROLLBACK for data integrity.
11. Advanced Topics
- Stored Procedures: Automate complex tasks.
- Triggers: Execute actions automatically based on events.
- Normalization: Understand database design principles.
12. Practice
- Use platforms like LeetCode, HackerRank, or learnsql for hands-on practice.
Here are some free resources to learn & practice SQL 👇👇
SQL For Data Analysis: https://news.1rj.ru/str/sqlanalyst
For Practice- https://stratascratch.com/?via=free
SQL Learning Series: https://news.1rj.ru/str/sqlspecialist/567
Top 10 SQL Projects with Datasets: https://news.1rj.ru/str/DataPortfolio/16
Join for more free resources: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
1. Basic Concepts
- Understand databases and SQL.
- Learn data types (INT, VARCHAR, DATE, etc.).
2. Basic Queries
- SELECT: Retrieve data.
- WHERE: Filter results.
- ORDER BY: Sort results.
- LIMIT: Restrict results.
3. Aggregate Functions
- COUNT, SUM, AVG, MAX, MIN.
- Use GROUP BY to group results.
4. Joins
- INNER JOIN: Combine rows from two tables based on a condition.
- LEFT JOIN: Include all rows from the left table.
- RIGHT JOIN: Include all rows from the right table.
- FULL OUTER JOIN: Include all rows from both tables.
5. Subqueries
- Use nested queries for complex data retrieval.
6. Data Manipulation
- INSERT: Add new records.
- UPDATE: Modify existing records.
- DELETE: Remove records.
7. Schema Management
- CREATE TABLE: Define new tables.
- ALTER TABLE: Modify existing tables.
- DROP TABLE: Remove tables.
8. Indexes
- Understand how to create and use indexes to optimize queries.
9. Views
- Create and manage views for simplified data access.
10. Transactions
- Learn about COMMIT and ROLLBACK for data integrity.
11. Advanced Topics
- Stored Procedures: Automate complex tasks.
- Triggers: Execute actions automatically based on events.
- Normalization: Understand database design principles.
12. Practice
- Use platforms like LeetCode, HackerRank, or learnsql for hands-on practice.
Here are some free resources to learn & practice SQL 👇👇
SQL For Data Analysis: https://news.1rj.ru/str/sqlanalyst
For Practice- https://stratascratch.com/?via=free
SQL Learning Series: https://news.1rj.ru/str/sqlspecialist/567
Top 10 SQL Projects with Datasets: https://news.1rj.ru/str/DataPortfolio/16
Join for more free resources: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
❤1👍1
𝟯 𝗙𝗿𝗲𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to master Python for Data Analytics without spending a single rupee?💰✨️
You don’t need expensive bootcamps or paid certifications to get started. Thanks to the open-source community, there are incredible free GitHub repositories that cover everything you need🧑💻📌
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Don’t just study theory—start coding, analyzing, and building today. Your portfolio (and future self) will thank you✅️
Want to master Python for Data Analytics without spending a single rupee?💰✨️
You don’t need expensive bootcamps or paid certifications to get started. Thanks to the open-source community, there are incredible free GitHub repositories that cover everything you need🧑💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/47hf59F
Don’t just study theory—start coding, analyzing, and building today. Your portfolio (and future self) will thank you✅️
❤1
Complete DSA Roadmap
|-- Basic_Data_Structures
| |-- Arrays
| |-- Strings
| |-- Linked_Lists
| |-- Stacks
| └─ Queues
|
|-- Advanced_Data_Structures
| |-- Trees
| | |-- Binary_Trees
| | |-- Binary_Search_Trees
| | |-- AVL_Trees
| | └─ B-Trees
| |
| |-- Graphs
| | |-- Graph_Representation
| | | |- Adjacency_Matrix
| | | └ Adjacency_List
| | |
| | |-- Depth-First_Search
| | |-- Breadth-First_Search
| | |-- Shortest_Path_Algorithms
| | | |- Dijkstra's_Algorithm
| | | └ Bellman-Ford_Algorithm
| | |
| | └─ Minimum_Spanning_Tree
| | |- Prim's_Algorithm
| | └ Kruskal's_Algorithm
| |
| |-- Heaps
| | |-- Min_Heap
| | |-- Max_Heap
| | └─ Heap_Sort
| |
| |-- Hash_Tables
| |-- Disjoint_Set_Union
| |-- Trie
| |-- Segment_Tree
| └─ Fenwick_Tree
|
|-- Algorithmic_Paradigms
| |-- Brute_Force
| |-- Divide_and_Conquer
| |-- Greedy_Algorithms
| |-- Dynamic_Programming
| |-- Backtracking
| |-- Sliding_Window_Technique
| |-- Two_Pointer_Technique
| └─ Divide_and_Conquer_Optimization
| |-- Merge_Sort_Tree
| └─ Persistent_Segment_Tree
|
|-- Searching_Algorithms
| |-- Linear_Search
| |-- Binary_Search
| |-- Depth-First_Search
| └─ Breadth-First_Search
|
|-- Sorting_Algorithms
| |-- Bubble_Sort
| |-- Selection_Sort
| |-- Insertion_Sort
| |-- Merge_Sort
| |-- Quick_Sort
| └─ Heap_Sort
|
|-- Graph_Algorithms
| |-- Depth-First_Search
| |-- Breadth-First_Search
| |-- Topological_Sort
| |-- Strongly_Connected_Components
| └─ Articulation_Points_and_Bridges
|
|-- Dynamic_Programming
| |-- Introduction_to_DP
| |-- Fibonacci_Series_using_DP
| |-- Longest_Common_Subsequence
| |-- Longest_Increasing_Subsequence
| |-- Knapsack_Problem
| |-- Matrix_Chain_Multiplication
| └─ Dynamic_Programming_on_Trees
|
|-- Mathematical_and_Bit_Manipulation_Algorithms
| |-- Prime_Numbers_and_Sieve_of_Eratosthenes
| |-- Greatest_Common_Divisor
| |-- Least_Common_Multiple
| |-- Modular_Arithmetic
| └─ Bit_Manipulation_Tricks
|
|-- Advanced_Topics
| |-- Trie-based_Algorithms
| | |-- Auto-completion
| | └─ Spell_Checker
| |
| |-- Suffix_Trees_and_Arrays
| |-- Computational_Geometry
| |-- Number_Theory
| | |-- Euler's_Totient_Function
| | └─ Mobius_Function
| |
| └─ String_Algorithms
| |-- KMP_Algorithm
| └─ Rabin-Karp_Algorithm
|
|-- OnlinePlatforms
| |-- LeetCode
| |-- HackerRank
|-- Basic_Data_Structures
| |-- Arrays
| |-- Strings
| |-- Linked_Lists
| |-- Stacks
| └─ Queues
|
|-- Advanced_Data_Structures
| |-- Trees
| | |-- Binary_Trees
| | |-- Binary_Search_Trees
| | |-- AVL_Trees
| | └─ B-Trees
| |
| |-- Graphs
| | |-- Graph_Representation
| | | |- Adjacency_Matrix
| | | └ Adjacency_List
| | |
| | |-- Depth-First_Search
| | |-- Breadth-First_Search
| | |-- Shortest_Path_Algorithms
| | | |- Dijkstra's_Algorithm
| | | └ Bellman-Ford_Algorithm
| | |
| | └─ Minimum_Spanning_Tree
| | |- Prim's_Algorithm
| | └ Kruskal's_Algorithm
| |
| |-- Heaps
| | |-- Min_Heap
| | |-- Max_Heap
| | └─ Heap_Sort
| |
| |-- Hash_Tables
| |-- Disjoint_Set_Union
| |-- Trie
| |-- Segment_Tree
| └─ Fenwick_Tree
|
|-- Algorithmic_Paradigms
| |-- Brute_Force
| |-- Divide_and_Conquer
| |-- Greedy_Algorithms
| |-- Dynamic_Programming
| |-- Backtracking
| |-- Sliding_Window_Technique
| |-- Two_Pointer_Technique
| └─ Divide_and_Conquer_Optimization
| |-- Merge_Sort_Tree
| └─ Persistent_Segment_Tree
|
|-- Searching_Algorithms
| |-- Linear_Search
| |-- Binary_Search
| |-- Depth-First_Search
| └─ Breadth-First_Search
|
|-- Sorting_Algorithms
| |-- Bubble_Sort
| |-- Selection_Sort
| |-- Insertion_Sort
| |-- Merge_Sort
| |-- Quick_Sort
| └─ Heap_Sort
|
|-- Graph_Algorithms
| |-- Depth-First_Search
| |-- Breadth-First_Search
| |-- Topological_Sort
| |-- Strongly_Connected_Components
| └─ Articulation_Points_and_Bridges
|
|-- Dynamic_Programming
| |-- Introduction_to_DP
| |-- Fibonacci_Series_using_DP
| |-- Longest_Common_Subsequence
| |-- Longest_Increasing_Subsequence
| |-- Knapsack_Problem
| |-- Matrix_Chain_Multiplication
| └─ Dynamic_Programming_on_Trees
|
|-- Mathematical_and_Bit_Manipulation_Algorithms
| |-- Prime_Numbers_and_Sieve_of_Eratosthenes
| |-- Greatest_Common_Divisor
| |-- Least_Common_Multiple
| |-- Modular_Arithmetic
| └─ Bit_Manipulation_Tricks
|
|-- Advanced_Topics
| |-- Trie-based_Algorithms
| | |-- Auto-completion
| | └─ Spell_Checker
| |
| |-- Suffix_Trees_and_Arrays
| |-- Computational_Geometry
| |-- Number_Theory
| | |-- Euler's_Totient_Function
| | └─ Mobius_Function
| |
| └─ String_Algorithms
| |-- KMP_Algorithm
| └─ Rabin-Karp_Algorithm
|
|-- OnlinePlatforms
| |-- LeetCode
| |-- HackerRank
❤1
🔥 Recent Data Analyst Interview Q&A at Deloitte 🔥
Question:
👉 Write an SQL query to extract the third highest salary from an employee table with columns EID and ESalary.
Solution:
Explanation of the Query:
1️⃣ Step 1: Create a Subquery
The subquery ranks all salaries in descending order using DENSE_RANK().
2️⃣ Step 2: Rank the Salaries
Assigns ranks: 1 for the highest salary, 2 for the second-highest, and so on.
3️⃣ Step 3: Assign an Alias
The subquery is given an alias (ranked_salaries) to use in the main query.
4️⃣ Step 4: Filter for the Third Highest Salary
The WHERE clause filters the results to include only the salary with rank 3.
5️⃣ Step 5: Display the Third Highest Salary
The main query selects and displays the third-highest salary.
By following these steps, you can easily extract the third-highest salary from the table.
#DataAnalyst #SQL #InterviewTips
Question:
👉 Write an SQL query to extract the third highest salary from an employee table with columns EID and ESalary.
Solution:
SELECT ESalary
FROM (
SELECT ESalary,
DENSE_RANK() OVER (ORDER BY ESalary DESC) AS salary_rank
FROM employee
) AS ranked_salaries
WHERE salary_rank = 3;
Explanation of the Query:
1️⃣ Step 1: Create a Subquery
The subquery ranks all salaries in descending order using DENSE_RANK().
2️⃣ Step 2: Rank the Salaries
Assigns ranks: 1 for the highest salary, 2 for the second-highest, and so on.
3️⃣ Step 3: Assign an Alias
The subquery is given an alias (ranked_salaries) to use in the main query.
4️⃣ Step 4: Filter for the Third Highest Salary
The WHERE clause filters the results to include only the salary with rank 3.
5️⃣ Step 5: Display the Third Highest Salary
The main query selects and displays the third-highest salary.
By following these steps, you can easily extract the third-highest salary from the table.
#DataAnalyst #SQL #InterviewTips
❤2
Here's a concise cheat sheet to help you get started with Python for Data Analytics. This guide covers essential libraries and functions that you'll frequently use.
1. Python Basics
- Variables:
- Data Types:
- Integers:
- Control Structures:
-
- Loops:
- While loop:
2. Importing Libraries
- NumPy:
- Pandas:
- Matplotlib:
- Seaborn:
3. NumPy for Numerical Data
- Creating Arrays:
- Array Operations:
- Reshaping Arrays:
- Indexing and Slicing:
4. Pandas for Data Manipulation
- Creating DataFrames:
- Reading Data:
- Basic Operations:
- Selecting Columns:
- Filtering Data:
- Handling Missing Data:
- GroupBy:
5. Data Visualization
- Matplotlib:
- Seaborn:
6. Common Data Operations
- Merging DataFrames:
- Pivot Table:
- Applying Functions:
7. Basic Statistics
- Denoscriptive Stats:
- Correlation:
This cheat sheet should give you a solid foundation in Python for data analytics. As you get more comfortable, you can delve deeper into each library's documentation for more advanced features.
I have curated the best resources to learn Python 👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this 👍❤️
1. Python Basics
- Variables:
x = 10 y = "Hello"
- Data Types:
- Integers:
x = 10
- Floats: y = 3.14
- Strings: name = "Alice"
- Lists: my_list = [1, 2, 3]
- Dictionaries: my_dict = {"key": "value"}
- Tuples: my_tuple = (1, 2, 3)
- Control Structures:
-
if, elif, else statements- Loops:
for i in range(5):
print(i)
- While loop:
while x < 5:
print(x)
x += 1
2. Importing Libraries
- NumPy:
import numpy as np
- Pandas:
import pandas as pd
- Matplotlib:
import matplotlib.pyplot as plt
- Seaborn:
import seaborn as sns
3. NumPy for Numerical Data
- Creating Arrays:
arr = np.array([1, 2, 3, 4])
- Array Operations:
arr.sum()
arr.mean()
- Reshaping Arrays:
arr.reshape((2, 2))
- Indexing and Slicing:
arr[0:2] # First two elements
4. Pandas for Data Manipulation
- Creating DataFrames:
df = pd.DataFrame({
'col1': [1, 2, 3],
'col2': ['A', 'B', 'C']
})
- Reading Data:
df = pd.read_csv('file.csv')
- Basic Operations:
df.head() # First 5 rows
df.describe() # Summary statistics
df.info() # DataFrame info
- Selecting Columns:
df['col1']
df[['col1', 'col2']]
- Filtering Data:
df[df['col1'] > 2]
- Handling Missing Data:
df.dropna() # Drop missing values
df.fillna(0) # Replace missing values
- GroupBy:
df.groupby('col2').mean()
5. Data Visualization
- Matplotlib:
plt.plot(df['col1'], df['col2'])
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.noscript('Title')
plt.show()
- Seaborn:
sns.histplot(df['col1'])
sns.boxplot(x='col1', y='col2', data=df)
6. Common Data Operations
- Merging DataFrames:
pd.merge(df1, df2, on='key')
- Pivot Table:
df.pivot_table(index='col1', columns='col2', values='col3')
- Applying Functions:
df['col1'].apply(lambda x: x*2)
7. Basic Statistics
- Denoscriptive Stats:
df['col1'].mean()
df['col1'].median()
df['col1'].std()
- Correlation:
df.corr()
This cheat sheet should give you a solid foundation in Python for data analytics. As you get more comfortable, you can delve deeper into each library's documentation for more advanced features.
I have curated the best resources to learn Python 👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this 👍❤️
❤1
𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 😍
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✅ Industry-Relevant Curriculum
✅ No Cost – Lifetime Free Access
✅ Boost Your Resume & Job Readiness
Perfect for Students, Freshers & Career Switchers!
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🎓 Enroll Now & Get Certified
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𝗧𝗼𝗽 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗔𝘀𝗸𝗲𝗱 𝗯𝘆 𝗠𝗡𝗖𝘀😍
If you can answer these Python questions, you’re already ahead of 90% of candidates.🧑💻✨️
These aren’t your average textbook questions. These are real interview questions asked in top MNCs — designed to test how deeply you understand Python.📊📍
𝐋𝐢𝐧𝐤👇:-
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This is the smart way to prepare✅️
If you can answer these Python questions, you’re already ahead of 90% of candidates.🧑💻✨️
These aren’t your average textbook questions. These are real interview questions asked in top MNCs — designed to test how deeply you understand Python.📊📍
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4mu4oVx
This is the smart way to prepare✅️
❤1
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◀️ https://news.1rj.ru/str/sql_engineer
6. Data Science ➝
◀️ https://news.1rj.ru/str/datasciencefun
7. Python ➝
◀️ http://pythontutorial.net
8. SQL ➝
◀️ https://news.1rj.ru/str/sqlanalyst
9. Git and GitHub ➝
◀️ http://GitFluence.com
10. Blockchain ➝
◀️ https://news.1rj.ru/str/Bitcoin_Crypto_Web
11. Mongo DB ➝
◀️ http://mongodb.com
12. Node JS ➝
◀️ http://nodejsera.com
13. English Speaking ➝
◀️ https://news.1rj.ru/str/englishlearnerspro
14. C#➝
◀️ https://learn.microsoft.com/en-us/training/paths/get-started-c-sharp-part-1/
15. Excel➝
◀️ https://news.1rj.ru/str/excel_analyst
16. Generative AI➝
◀️ https://news.1rj.ru/str/generativeai_gpt
17. Java
◀️ https://news.1rj.ru/str/Java_Programming_Notes
18. Artificial Intelligence
◀️ https://news.1rj.ru/str/machinelearning_deeplearning
19. Data Structure & Algorithms
◀️ https://news.1rj.ru/str/dsabooks
20. Backend Development
◀️ https://imp.i115008.net/rn2nyy
21. Python for AI
◀️ https://deeplearning.ai/short-courses/ai-python-for-beginners/
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING👍👍
1. Web Development ➝
◀️ https://news.1rj.ru/str/webdevcoursefree
2. CSS ➝
◀️ http://css-tricks.com
3. JavaScript ➝
◀️ http://t.me/javanoscript_courses
4. React ➝
◀️ http://react-tutorial.app
5. Data Engineering ➝
◀️ https://news.1rj.ru/str/sql_engineer
6. Data Science ➝
◀️ https://news.1rj.ru/str/datasciencefun
7. Python ➝
◀️ http://pythontutorial.net
8. SQL ➝
◀️ https://news.1rj.ru/str/sqlanalyst
9. Git and GitHub ➝
◀️ http://GitFluence.com
10. Blockchain ➝
◀️ https://news.1rj.ru/str/Bitcoin_Crypto_Web
11. Mongo DB ➝
◀️ http://mongodb.com
12. Node JS ➝
◀️ http://nodejsera.com
13. English Speaking ➝
◀️ https://news.1rj.ru/str/englishlearnerspro
14. C#➝
◀️ https://learn.microsoft.com/en-us/training/paths/get-started-c-sharp-part-1/
15. Excel➝
◀️ https://news.1rj.ru/str/excel_analyst
16. Generative AI➝
◀️ https://news.1rj.ru/str/generativeai_gpt
17. Java
◀️ https://news.1rj.ru/str/Java_Programming_Notes
18. Artificial Intelligence
◀️ https://news.1rj.ru/str/machinelearning_deeplearning
19. Data Structure & Algorithms
◀️ https://news.1rj.ru/str/dsabooks
20. Backend Development
◀️ https://imp.i115008.net/rn2nyy
21. Python for AI
◀️ https://deeplearning.ai/short-courses/ai-python-for-beginners/
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING👍👍
❤1
📱 25 YouTube Channels to Learn Programming for FREE 💻🚀
✅ freeCodeCamp
✅ The Net Ninja
✅ Traversy Media
✅ Programming with Mosh
✅ Fireship
✅ Amigoscode
✅ CS50 by Harvard
✅ CodeWithHarry
✅ Tech with Tim
✅ Academind
✅ Web Dev Simplified
✅ The Odin Project
✅ JavaScript Mastery
✅ Derek Banas
✅ Bro Code
✅ Simplilearn
✅ Codevolution
✅ Hussein Nasser
✅ Dev Ed
✅ Sonny Sangha
✅ Telusko
✅ Caleb Curry
✅ Python Engineer
✅ Clever Programmer
✅ GeeksforGeeks
🔥 React “❤️” if you found this helpful!
✅ freeCodeCamp
✅ The Net Ninja
✅ Traversy Media
✅ Programming with Mosh
✅ Fireship
✅ Amigoscode
✅ CS50 by Harvard
✅ CodeWithHarry
✅ Tech with Tim
✅ Academind
✅ Web Dev Simplified
✅ The Odin Project
✅ JavaScript Mastery
✅ Derek Banas
✅ Bro Code
✅ Simplilearn
✅ Codevolution
✅ Hussein Nasser
✅ Dev Ed
✅ Sonny Sangha
✅ Telusko
✅ Caleb Curry
✅ Python Engineer
✅ Clever Programmer
✅ GeeksforGeeks
🔥 React “❤️” if you found this helpful!
❤7🥰1
When to Use Which Programming Language?
C ➝ OS Development, Embedded Systems, Game Engines
C++ ➝ Game Dev, High-Performance Apps, Finance
Java ➝ Enterprise Apps, Android, Backend
C# ➝ Unity Games, Windows Apps
Python ➝ AI/ML, Data, Automation, Web Dev
JavaScript ➝ Frontend, Full-Stack, Web Games
Golang ➝ Cloud Services, APIs, Networking
Swift ➝ iOS/macOS Apps
Kotlin ➝ Android, Backend
PHP ➝ Web Dev (WordPress, Laravel)
Ruby ➝ Web Dev (Rails), Prototypes
Rust ➝ System Apps, Blockchain, HPC
Lua ➝ Game Scripting (Roblox, WoW)
R ➝ Stats, Data Science, Bioinformatics
SQL ➝ Data Analysis, DB Management
TypeScript ➝ Scalable Web Apps
Node.js ➝ Backend, Real-Time Apps
React ➝ Modern Web UIs
Vue ➝ Lightweight SPAs
Django ➝ AI/ML Backend, Web Dev
Laravel ➝ Full-Stack PHP
Blazor ➝ Web with .NET
Spring Boot ➝ Microservices, Java Enterprise
Ruby on Rails ➝ MVPs, Startups
HTML/CSS ➝ UI/UX, Web Design
Git ➝ Version Control
Linux ➝ Server, Security, DevOps
DevOps ➝ Infra Automation, CI/CD
CI/CD ➝ Testing + Deployment
Docker ➝ Containerization
Kubernetes ➝ Cloud Orchestration
Microservices ➝ Scalable Backends
Selenium ➝ Web Testing
Playwright ➝ Modern Web Automation
Credits: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
ENJOY LEARNING 👍👍
C ➝ OS Development, Embedded Systems, Game Engines
C++ ➝ Game Dev, High-Performance Apps, Finance
Java ➝ Enterprise Apps, Android, Backend
C# ➝ Unity Games, Windows Apps
Python ➝ AI/ML, Data, Automation, Web Dev
JavaScript ➝ Frontend, Full-Stack, Web Games
Golang ➝ Cloud Services, APIs, Networking
Swift ➝ iOS/macOS Apps
Kotlin ➝ Android, Backend
PHP ➝ Web Dev (WordPress, Laravel)
Ruby ➝ Web Dev (Rails), Prototypes
Rust ➝ System Apps, Blockchain, HPC
Lua ➝ Game Scripting (Roblox, WoW)
R ➝ Stats, Data Science, Bioinformatics
SQL ➝ Data Analysis, DB Management
TypeScript ➝ Scalable Web Apps
Node.js ➝ Backend, Real-Time Apps
React ➝ Modern Web UIs
Vue ➝ Lightweight SPAs
Django ➝ AI/ML Backend, Web Dev
Laravel ➝ Full-Stack PHP
Blazor ➝ Web with .NET
Spring Boot ➝ Microservices, Java Enterprise
Ruby on Rails ➝ MVPs, Startups
HTML/CSS ➝ UI/UX, Web Design
Git ➝ Version Control
Linux ➝ Server, Security, DevOps
DevOps ➝ Infra Automation, CI/CD
CI/CD ➝ Testing + Deployment
Docker ➝ Containerization
Kubernetes ➝ Cloud Orchestration
Microservices ➝ Scalable Backends
Selenium ➝ Web Testing
Playwright ➝ Modern Web Automation
Credits: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
ENJOY LEARNING 👍👍
❤2
𝗔𝗜 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🚀
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❤4
TOP 10 SQL Concepts for Job Interview
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Denoscriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Denoscriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
❤1
One day or Day one. You decide.
Data Science edition.
𝗢𝗻𝗲 𝗗𝗮𝘆 : I will learn SQL.
𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my projects for my portfolio.
𝗗𝗮𝘆 𝗢𝗻𝗲: Look on Kaggle for a dataset to work on.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will master statistics.
𝗗𝗮𝘆 𝗢𝗻𝗲: Start the free Khan Academy Statistics and Probability course.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn to tell stories with data.
𝗗𝗮𝘆 𝗢𝗻𝗲: Install Tableau Public and create my first chart.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will become a Data Scientist.
𝗗𝗮𝘆 𝗢𝗻𝗲: Update my resume and apply to some Data Science job postings.
Data Science edition.
𝗢𝗻𝗲 𝗗𝗮𝘆 : I will learn SQL.
𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my projects for my portfolio.
𝗗𝗮𝘆 𝗢𝗻𝗲: Look on Kaggle for a dataset to work on.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will master statistics.
𝗗𝗮𝘆 𝗢𝗻𝗲: Start the free Khan Academy Statistics and Probability course.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn to tell stories with data.
𝗗𝗮𝘆 𝗢𝗻𝗲: Install Tableau Public and create my first chart.
𝗢𝗻𝗲 𝗗𝗮𝘆: I will become a Data Scientist.
𝗗𝗮𝘆 𝗢𝗻𝗲: Update my resume and apply to some Data Science job postings.
❤1
AI tools for job hunt.pdf
2.4 MB
AI Tools
15 built in hooks react js.pdf
454.5 KB
React Js ➡️ 15 Built in hooks✅️
❤5
Breaking into Data Analytics doesn’t need to be complicated.
If you’re just starting out,
Here’s how to simplify your approach:
Avoid:
🚫 Jumping into advanced tools like Hadoop or Spark before mastering the basics.
🚫 Focusing only on tools, not on business problem-solving.
🚫 Collecting certificates instead of solving real problems.
🚫 Thinking you need to know everything from SQL to machine learning right away.
Instead:
✅ Start with Excel, SQL, and one visualization tool (like Power BI or Tableau).
✅ Learn how to clean, explore, and interpret data to solve business questions.
✅ Understand core concepts like KPIs, dashboards, and business metrics.
✅ Pick real datasets and analyze them with clear goals and insights.
✅ Build a portfolio that shows you can translate data into decisions.
React ❤️ for more
If you’re just starting out,
Here’s how to simplify your approach:
Avoid:
🚫 Jumping into advanced tools like Hadoop or Spark before mastering the basics.
🚫 Focusing only on tools, not on business problem-solving.
🚫 Collecting certificates instead of solving real problems.
🚫 Thinking you need to know everything from SQL to machine learning right away.
Instead:
✅ Start with Excel, SQL, and one visualization tool (like Power BI or Tableau).
✅ Learn how to clean, explore, and interpret data to solve business questions.
✅ Understand core concepts like KPIs, dashboards, and business metrics.
✅ Pick real datasets and analyze them with clear goals and insights.
✅ Build a portfolio that shows you can translate data into decisions.
React ❤️ for more
❤1
🚀 Become an Agentic AI Builder — Free 12‑Week Certification by Ready Tensor
Ready Tensor’s Agentic AI Developer Certification is a free, project first 12‑week program designed to help you build and deploy real-world agentic AI systems. You'll complete three portfolio-ready projects using tools like LangChain, LangGraph, and vector databases, while deploying production-ready agents with FastAPI or Streamlit.
The course focuses on developing autonomous AI agents that can plan, reason, use memory, and act safely in complex environments. Certification is earned not by watching lectures, but by building — each project is reviewed against rigorous standards.
You can start anytime, and new cohorts begin monthly. Ideal for developers and engineers ready to go beyond chat prompts and start building true agentic systems.
👉 Apply now: https://www.readytensor.ai/agentic-ai-cert/
Ready Tensor’s Agentic AI Developer Certification is a free, project first 12‑week program designed to help you build and deploy real-world agentic AI systems. You'll complete three portfolio-ready projects using tools like LangChain, LangGraph, and vector databases, while deploying production-ready agents with FastAPI or Streamlit.
The course focuses on developing autonomous AI agents that can plan, reason, use memory, and act safely in complex environments. Certification is earned not by watching lectures, but by building — each project is reviewed against rigorous standards.
You can start anytime, and new cohorts begin monthly. Ideal for developers and engineers ready to go beyond chat prompts and start building true agentic systems.
👉 Apply now: https://www.readytensor.ai/agentic-ai-cert/
❤1