SQL best practices:
โ Use EXISTS in place of IN wherever possible
โ Use table aliases with columns when you are joining multiple tables
โ Use GROUP BY instead of DISTINCT.
โ Add useful comments wherever you write complex logic and avoid too many comments.
โ Use joins instead of subqueries when possible for better performance.
โ Use WHERE instead of HAVING to define filters on non-aggregate fields
โ Avoid wildcards at beginning of predicates (something like '%abc' will cause full table scan to get the results)
โ Considering cardinality within GROUP BY can make it faster (try to consider unique column first in group by list)
โ Write SQL keywords in capital letters.
โ Never use select *, always mention list of columns in select clause.
โ Create CTEs instead of multiple sub queries , it will make your query easy to read.
โ Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
โ Never use order by in sub queries , It will unnecessary increase runtime.
โ If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance
โ Always start WHERE clause with 1 = 1.This has the advantage of easily commenting out conditions during debugging a query.
โ Taking care of NULL values before using equality or comparisons operators. Applying window functions. Filtering the query before joining and having clause.
โ Make sure the JOIN conditions among two table Join are either keys or Indexed attribute.
Hope it helps :)
โ Use EXISTS in place of IN wherever possible
โ Use table aliases with columns when you are joining multiple tables
โ Use GROUP BY instead of DISTINCT.
โ Add useful comments wherever you write complex logic and avoid too many comments.
โ Use joins instead of subqueries when possible for better performance.
โ Use WHERE instead of HAVING to define filters on non-aggregate fields
โ Avoid wildcards at beginning of predicates (something like '%abc' will cause full table scan to get the results)
โ Considering cardinality within GROUP BY can make it faster (try to consider unique column first in group by list)
โ Write SQL keywords in capital letters.
โ Never use select *, always mention list of columns in select clause.
โ Create CTEs instead of multiple sub queries , it will make your query easy to read.
โ Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
โ Never use order by in sub queries , It will unnecessary increase runtime.
โ If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance
โ Always start WHERE clause with 1 = 1.This has the advantage of easily commenting out conditions during debugging a query.
โ Taking care of NULL values before using equality or comparisons operators. Applying window functions. Filtering the query before joining and having clause.
โ Make sure the JOIN conditions among two table Join are either keys or Indexed attribute.
Hope it helps :)
โค2๐2
๐ง๐ผ๐ฝ ๐ฑ ๐ง๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ถ๐ฒ๐ ๐ง๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ป ๐ฎ๐ฌ๐ฎ๐ฑ | ๐๐ป๐ฟ๐ผ๐น๐น ๐๐ผ๐ฟ ๐๐ฅ๐๐ ๐
Acquire industry-relevant skills to grow in your career and stand out to prospective employers.
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Acquire industry-relevant skills to grow in your career and stand out to prospective employers.
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Complete Roadmap to learn SQL in 2025 ๐๐
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 ๐๐
SQL For Data Analysis: https://t.iss.one/sqlanalyst
For Practice- https://stratascratch.com/?via=free
SQL Learning Series: https://t.iss.one/sqlspecialist/567
Top 10 SQL Projects with Datasets: https://t.iss.one/DataPortfolio/16
Join for more free resources: https://t.iss.one/free4unow_backup
ENJOY LEARNING ๐๐
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 ๐๐
SQL For Data Analysis: https://t.iss.one/sqlanalyst
For Practice- https://stratascratch.com/?via=free
SQL Learning Series: https://t.iss.one/sqlspecialist/567
Top 10 SQL Projects with Datasets: https://t.iss.one/DataPortfolio/16
Join for more free resources: https://t.iss.one/free4unow_backup
ENJOY LEARNING ๐๐
โค1๐1
๐ฏ ๐๐ฟ๐ฒ๐ฒ ๐๐ถ๐๐๐๐ฏ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ฃ๐๐๐ต๐ผ๐ป ๐ณ๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Want to master Python for Data Analytics without spending a single rupee?๐ฐโจ๏ธ
You donโt 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 need๐งโ๐ป๐
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https://pdlink.in/47hf59F
Donโt just study theoryโstart coding, analyzing, and building today. Your portfolio (and future self) will thank youโ ๏ธ
Want to master Python for Data Analytics without spending a single rupee?๐ฐโจ๏ธ
You donโt 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 need๐งโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/47hf59F
Donโt just study theoryโstart coding, analyzing, and building today. Your portfolio (and future self) will thank youโ ๏ธ
โค1
Complete DSA Roadmap
|-- Basic_Data_Structures
| |-- Arrays
| |-- Strings
| |-- Linked_Lists
| |-- Stacks
| โโ Queues
|
|-- Advanced_Data_Structures
| |-- Trees
| | |-- Binary_Trees
| | |-- Binary_Search_Trees
| | |-- AVL_Trees
| | โโ B-Trees
| |
| |-- Graphs
| | |-- Graph_Representation
| | | |- Adjacency_Matrix
| | | โ Adjacency_List
| | |
| | |-- Depth-First_Search
| | |-- Breadth-First_Search
| | |-- Shortest_Path_Algorithms
| | | |- Dijkstra's_Algorithm
| | | โ Bellman-Ford_Algorithm
| | |
| | โโ Minimum_Spanning_Tree
| | |- Prim's_Algorithm
| | โ Kruskal's_Algorithm
| |
| |-- Heaps
| | |-- Min_Heap
| | |-- Max_Heap
| | โโ Heap_Sort
| |
| |-- Hash_Tables
| |-- Disjoint_Set_Union
| |-- Trie
| |-- Segment_Tree
| โโ Fenwick_Tree
|
|-- Algorithmic_Paradigms
| |-- Brute_Force
| |-- Divide_and_Conquer
| |-- Greedy_Algorithms
| |-- Dynamic_Programming
| |-- Backtracking
| |-- Sliding_Window_Technique
| |-- Two_Pointer_Technique
| โโ Divide_and_Conquer_Optimization
| |-- Merge_Sort_Tree
| โโ Persistent_Segment_Tree
|
|-- Searching_Algorithms
| |-- Linear_Search
| |-- Binary_Search
| |-- Depth-First_Search
| โโ Breadth-First_Search
|
|-- Sorting_Algorithms
| |-- Bubble_Sort
| |-- Selection_Sort
| |-- Insertion_Sort
| |-- Merge_Sort
| |-- Quick_Sort
| โโ Heap_Sort
|
|-- Graph_Algorithms
| |-- Depth-First_Search
| |-- Breadth-First_Search
| |-- Topological_Sort
| |-- Strongly_Connected_Components
| โโ Articulation_Points_and_Bridges
|
|-- Dynamic_Programming
| |-- Introduction_to_DP
| |-- Fibonacci_Series_using_DP
| |-- Longest_Common_Subsequence
| |-- Longest_Increasing_Subsequence
| |-- Knapsack_Problem
| |-- Matrix_Chain_Multiplication
| โโ Dynamic_Programming_on_Trees
|
|-- Mathematical_and_Bit_Manipulation_Algorithms
| |-- Prime_Numbers_and_Sieve_of_Eratosthenes
| |-- Greatest_Common_Divisor
| |-- Least_Common_Multiple
| |-- Modular_Arithmetic
| โโ Bit_Manipulation_Tricks
|
|-- Advanced_Topics
| |-- Trie-based_Algorithms
| | |-- Auto-completion
| | โโ Spell_Checker
| |
| |-- Suffix_Trees_and_Arrays
| |-- Computational_Geometry
| |-- Number_Theory
| | |-- Euler's_Totient_Function
| | โโ Mobius_Function
| |
| โโ String_Algorithms
| |-- KMP_Algorithm
| โโ Rabin-Karp_Algorithm
|
|-- OnlinePlatforms
| |-- LeetCode
| |-- HackerRank
|-- Basic_Data_Structures
| |-- Arrays
| |-- Strings
| |-- Linked_Lists
| |-- Stacks
| โโ Queues
|
|-- Advanced_Data_Structures
| |-- Trees
| | |-- Binary_Trees
| | |-- Binary_Search_Trees
| | |-- AVL_Trees
| | โโ B-Trees
| |
| |-- Graphs
| | |-- Graph_Representation
| | | |- Adjacency_Matrix
| | | โ Adjacency_List
| | |
| | |-- Depth-First_Search
| | |-- Breadth-First_Search
| | |-- Shortest_Path_Algorithms
| | | |- Dijkstra's_Algorithm
| | | โ Bellman-Ford_Algorithm
| | |
| | โโ Minimum_Spanning_Tree
| | |- Prim's_Algorithm
| | โ Kruskal's_Algorithm
| |
| |-- Heaps
| | |-- Min_Heap
| | |-- Max_Heap
| | โโ Heap_Sort
| |
| |-- Hash_Tables
| |-- Disjoint_Set_Union
| |-- Trie
| |-- Segment_Tree
| โโ Fenwick_Tree
|
|-- Algorithmic_Paradigms
| |-- Brute_Force
| |-- Divide_and_Conquer
| |-- Greedy_Algorithms
| |-- Dynamic_Programming
| |-- Backtracking
| |-- Sliding_Window_Technique
| |-- Two_Pointer_Technique
| โโ Divide_and_Conquer_Optimization
| |-- Merge_Sort_Tree
| โโ Persistent_Segment_Tree
|
|-- Searching_Algorithms
| |-- Linear_Search
| |-- Binary_Search
| |-- Depth-First_Search
| โโ Breadth-First_Search
|
|-- Sorting_Algorithms
| |-- Bubble_Sort
| |-- Selection_Sort
| |-- Insertion_Sort
| |-- Merge_Sort
| |-- Quick_Sort
| โโ Heap_Sort
|
|-- Graph_Algorithms
| |-- Depth-First_Search
| |-- Breadth-First_Search
| |-- Topological_Sort
| |-- Strongly_Connected_Components
| โโ Articulation_Points_and_Bridges
|
|-- Dynamic_Programming
| |-- Introduction_to_DP
| |-- Fibonacci_Series_using_DP
| |-- Longest_Common_Subsequence
| |-- Longest_Increasing_Subsequence
| |-- Knapsack_Problem
| |-- Matrix_Chain_Multiplication
| โโ Dynamic_Programming_on_Trees
|
|-- Mathematical_and_Bit_Manipulation_Algorithms
| |-- Prime_Numbers_and_Sieve_of_Eratosthenes
| |-- Greatest_Common_Divisor
| |-- Least_Common_Multiple
| |-- Modular_Arithmetic
| โโ Bit_Manipulation_Tricks
|
|-- Advanced_Topics
| |-- Trie-based_Algorithms
| | |-- Auto-completion
| | โโ Spell_Checker
| |
| |-- Suffix_Trees_and_Arrays
| |-- Computational_Geometry
| |-- Number_Theory
| | |-- Euler's_Totient_Function
| | โโ Mobius_Function
| |
| โโ String_Algorithms
| |-- KMP_Algorithm
| โโ Rabin-Karp_Algorithm
|
|-- OnlinePlatforms
| |-- LeetCode
| |-- HackerRank
โค1
๐ฅ Recent Data Analyst Interview Q&A at Deloitte ๐ฅ
Question:
๐ Write an SQL query to extract the third highest salary from an employee table with columns EID and ESalary.
Solution:
Explanation of the Query:
1๏ธโฃ Step 1: Create a Subquery
The subquery ranks all salaries in descending order using DENSE_RANK().
2๏ธโฃ Step 2: Rank the Salaries
Assigns ranks: 1 for the highest salary, 2 for the second-highest, and so on.
3๏ธโฃ Step 3: Assign an Alias
The subquery is given an alias (ranked_salaries) to use in the main query.
4๏ธโฃ Step 4: Filter for the Third Highest Salary
The WHERE clause filters the results to include only the salary with rank 3.
5๏ธโฃ 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:
๐ 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:
1๏ธโฃ Step 1: Create a Subquery
The subquery ranks all salaries in descending order using DENSE_RANK().
2๏ธโฃ Step 2: Rank the Salaries
Assigns ranks: 1 for the highest salary, 2 for the second-highest, and so on.
3๏ธโฃ Step 3: Assign an Alias
The subquery is given an alias (ranked_salaries) to use in the main query.
4๏ธโฃ Step 4: Filter for the Third Highest Salary
The WHERE clause filters the results to include only the salary with rank 3.
5๏ธโฃ 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
โค2
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
- Descriptive 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 ๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
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.title('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
- Descriptive 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 ๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
โค1
๐ญ๐ฌ๐ฌ% ๐๐ฅ๐๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐
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If you can answer these Python questions, youโre already ahead of 90% of candidates.๐งโ๐ปโจ๏ธ
These arenโt your average textbook questions. These are real interview questions asked in top MNCs โ designed to test how deeply you understand Python.๐๐
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This is the smart way to prepareโ ๏ธ
If you can answer these Python questions, youโre already ahead of 90% of candidates.๐งโ๐ปโจ๏ธ
These arenโt your average textbook questions. These are real interview questions asked in top MNCs โ designed to test how deeply you understand Python.๐๐
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This is the smart way to prepareโ ๏ธ
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โ๏ธ https://t.iss.one/englishlearnerspro
14. C#โ
โ๏ธ https://learn.microsoft.com/en-us/training/paths/get-started-c-sharp-part-1/
15. Excelโ
โ๏ธ https://t.iss.one/excel_analyst
16. Generative AIโ
โ๏ธ https://t.iss.one/generativeai_gpt
17. Java
โ๏ธ https://t.iss.one/Java_Programming_Notes
18. Artificial Intelligence
โ๏ธ https://t.iss.one/machinelearning_deeplearning
19. Data Structure & Algorithms
โ๏ธ https://t.iss.one/dsabooks
20. Backend Development
โ๏ธ https://imp.i115008.net/rn2nyy
21. Python for AI
โ๏ธ https://deeplearning.ai/short-courses/ai-python-for-beginners/
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING๐๐
1. Web Development โ
โ๏ธ https://t.iss.one/webdevcoursefree
2. CSS โ
โ๏ธ https://css-tricks.com
3. JavaScript โ
โ๏ธ https://t.iss.one/javascript_courses
4. React โ
โ๏ธ https://react-tutorial.app
5. Data Engineering โ
โ๏ธ https://t.iss.one/sql_engineer
6. Data Science โ
โ๏ธ https://t.iss.one/datasciencefun
7. Python โ
โ๏ธ https://pythontutorial.net
8. SQL โ
โ๏ธ https://t.iss.one/sqlanalyst
9. Git and GitHub โ
โ๏ธ https://GitFluence.com
10. Blockchain โ
โ๏ธ https://t.iss.one/Bitcoin_Crypto_Web
11. Mongo DB โ
โ๏ธ https://mongodb.com
12. Node JS โ
โ๏ธ https://nodejsera.com
13. English Speaking โ
โ๏ธ https://t.iss.one/englishlearnerspro
14. C#โ
โ๏ธ https://learn.microsoft.com/en-us/training/paths/get-started-c-sharp-part-1/
15. Excelโ
โ๏ธ https://t.iss.one/excel_analyst
16. Generative AIโ
โ๏ธ https://t.iss.one/generativeai_gpt
17. Java
โ๏ธ https://t.iss.one/Java_Programming_Notes
18. Artificial Intelligence
โ๏ธ https://t.iss.one/machinelearning_deeplearning
19. Data Structure & Algorithms
โ๏ธ https://t.iss.one/dsabooks
20. Backend Development
โ๏ธ https://imp.i115008.net/rn2nyy
21. Python for AI
โ๏ธ https://deeplearning.ai/short-courses/ai-python-for-beginners/
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING๐๐
โค1
๐ฑ 25 YouTube Channels to Learn Programming for FREE ๐ป๐
โ freeCodeCamp
โ The Net Ninja
โ Traversy Media
โ Programming with Mosh
โ Fireship
โ Amigoscode
โ CS50 by Harvard
โ CodeWithHarry
โ Tech with Tim
โ Academind
โ Web Dev Simplified
โ The Odin Project
โ JavaScript Mastery
โ Derek Banas
โ Bro Code
โ Simplilearn
โ Codevolution
โ Hussein Nasser
โ Dev Ed
โ Sonny Sangha
โ Telusko
โ Caleb Curry
โ Python Engineer
โ Clever Programmer
โ GeeksforGeeks
๐ฅ React โโค๏ธโ if you found this helpful!
โ freeCodeCamp
โ The Net Ninja
โ Traversy Media
โ Programming with Mosh
โ Fireship
โ Amigoscode
โ CS50 by Harvard
โ CodeWithHarry
โ Tech with Tim
โ Academind
โ Web Dev Simplified
โ The Odin Project
โ JavaScript Mastery
โ Derek Banas
โ Bro Code
โ Simplilearn
โ Codevolution
โ Hussein Nasser
โ Dev Ed
โ Sonny Sangha
โ Telusko
โ Caleb Curry
โ Python Engineer
โ Clever Programmer
โ GeeksforGeeks
๐ฅ React โโค๏ธโ if you found this helpful!
โค5๐ฅฐ1
When to Use Which Programming Language?
C โ OS Development, Embedded Systems, Game Engines
C++ โ Game Dev, High-Performance Apps, Finance
Java โ Enterprise Apps, Android, Backend
C# โ Unity Games, Windows Apps
Python โ AI/ML, Data, Automation, Web Dev
JavaScript โ Frontend, Full-Stack, Web Games
Golang โ Cloud Services, APIs, Networking
Swift โ iOS/macOS Apps
Kotlin โ Android, Backend
PHP โ Web Dev (WordPress, Laravel)
Ruby โ Web Dev (Rails), Prototypes
Rust โ System Apps, Blockchain, HPC
Lua โ Game Scripting (Roblox, WoW)
R โ Stats, Data Science, Bioinformatics
SQL โ Data Analysis, DB Management
TypeScript โ Scalable Web Apps
Node.js โ Backend, Real-Time Apps
React โ Modern Web UIs
Vue โ Lightweight SPAs
Django โ AI/ML Backend, Web Dev
Laravel โ Full-Stack PHP
Blazor โ Web with .NET
Spring Boot โ Microservices, Java Enterprise
Ruby on Rails โ MVPs, Startups
HTML/CSS โ UI/UX, Web Design
Git โ Version Control
Linux โ Server, Security, DevOps
DevOps โ Infra Automation, CI/CD
CI/CD โ Testing + Deployment
Docker โ Containerization
Kubernetes โ Cloud Orchestration
Microservices โ Scalable Backends
Selenium โ Web Testing
Playwright โ Modern Web Automation
Credits: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
ENJOY LEARNING ๐๐
C โ OS Development, Embedded Systems, Game Engines
C++ โ Game Dev, High-Performance Apps, Finance
Java โ Enterprise Apps, Android, Backend
C# โ Unity Games, Windows Apps
Python โ AI/ML, Data, Automation, Web Dev
JavaScript โ Frontend, Full-Stack, Web Games
Golang โ Cloud Services, APIs, Networking
Swift โ iOS/macOS Apps
Kotlin โ Android, Backend
PHP โ Web Dev (WordPress, Laravel)
Ruby โ Web Dev (Rails), Prototypes
Rust โ System Apps, Blockchain, HPC
Lua โ Game Scripting (Roblox, WoW)
R โ Stats, Data Science, Bioinformatics
SQL โ Data Analysis, DB Management
TypeScript โ Scalable Web Apps
Node.js โ Backend, Real-Time Apps
React โ Modern Web UIs
Vue โ Lightweight SPAs
Django โ AI/ML Backend, Web Dev
Laravel โ Full-Stack PHP
Blazor โ Web with .NET
Spring Boot โ Microservices, Java Enterprise
Ruby on Rails โ MVPs, Startups
HTML/CSS โ UI/UX, Web Design
Git โ Version Control
Linux โ Server, Security, DevOps
DevOps โ Infra Automation, CI/CD
CI/CD โ Testing + Deployment
Docker โ Containerization
Kubernetes โ Cloud Orchestration
Microservices โ Scalable Backends
Selenium โ Web Testing
Playwright โ Modern Web Automation
Credits: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
ENJOY LEARNING ๐๐
โค2
๐๐ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
AI is the future now & highly in demand
๐ผ Learn in-demand AI skills
๐ Beginner-friendly โ No experience needed
โ Get Certified & Boost Your Career
๐ฏ 100% Free โ Limited Time!
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐๐ผ๐ฟ ๐๐ฅ๐๐ ๐ก๐ผ๐ ๐:-
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๐ Enroll today & start your AI journey!
AI is the future now & highly in demand
๐ผ Learn in-demand AI skills
๐ Beginner-friendly โ No experience needed
โ Get Certified & Boost Your Career
๐ฏ 100% Free โ Limited Time!
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐๐ผ๐ฟ ๐๐ฅ๐๐ ๐ก๐ผ๐ ๐:-
https://pdlink.in/3U3eZuq
๐ Enroll today & start your AI journey!
โค4
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. Descriptive 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. Descriptive 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
โค1
One day or Day one. You decide.
Data Science edition.
๐ข๐ป๐ฒ ๐๐ฎ๐ : I will learn SQL.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Download mySQL Workbench.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will build my projects for my portfolio.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Look on Kaggle for a dataset to work on.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will master statistics.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Start the free Khan Academy Statistics and Probability course.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will learn to tell stories with data.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Install Tableau Public and create my first chart.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will become a Data Scientist.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Update my resume and apply to some Data Science job postings.
Data Science edition.
๐ข๐ป๐ฒ ๐๐ฎ๐ : I will learn SQL.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Download mySQL Workbench.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will build my projects for my portfolio.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Look on Kaggle for a dataset to work on.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will master statistics.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Start the free Khan Academy Statistics and Probability course.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will learn to tell stories with data.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Install Tableau Public and create my first chart.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will become a Data Scientist.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Update my resume and apply to some Data Science job postings.
โค1
AI tools for job hunt.pdf
2.4 MB
AI Tools
15 built in hooks react js.pdf
454.5 KB
React Js โก๏ธ 15 Built in hooksโ
๏ธ
โค5
Breaking into Data Analytics doesnโt need to be complicated.
If youโre just starting out,
Hereโs how to simplify your approach:
Avoid:
๐ซ Jumping into advanced tools like Hadoop or Spark before mastering the basics.
๐ซ Focusing only on tools, not on business problem-solving.
๐ซ Collecting certificates instead of solving real problems.
๐ซ Thinking you need to know everything from SQL to machine learning right away.
Instead:
โ Start with Excel, SQL, and one visualization tool (like Power BI or Tableau).
โ Learn how to clean, explore, and interpret data to solve business questions.
โ Understand core concepts like KPIs, dashboards, and business metrics.
โ Pick real datasets and analyze them with clear goals and insights.
โ Build a portfolio that shows you can translate data into decisions.
React โค๏ธ for more
If youโre just starting out,
Hereโs how to simplify your approach:
Avoid:
๐ซ Jumping into advanced tools like Hadoop or Spark before mastering the basics.
๐ซ Focusing only on tools, not on business problem-solving.
๐ซ Collecting certificates instead of solving real problems.
๐ซ Thinking you need to know everything from SQL to machine learning right away.
Instead:
โ Start with Excel, SQL, and one visualization tool (like Power BI or Tableau).
โ Learn how to clean, explore, and interpret data to solve business questions.
โ Understand core concepts like KPIs, dashboards, and business metrics.
โ Pick real datasets and analyze them with clear goals and insights.
โ Build a portfolio that shows you can translate data into decisions.
React โค๏ธ for more
โค1