Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstraโs algorithm for shortest path
- Kruskalโs and Primโs algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstraโs algorithm for shortest path
- Kruskalโs and Primโs algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
๐2โค1
7 Most Popular Programming Languages in 2025
1. Python
The Jack of All Trades
Why it's loved: Simple syntax, huge community, beginner-friendly.
Used for: Data Science, Machine Learning, Web Development, Automation.
Who uses it: Data analysts, backend developers, researchers, even kids learning to code.
2. JavaScript
The Language of the Web
Why it's everywhere: Runs in every browser, now also on servers (Node.js).
Used for: Frontend & backend web apps, interactive UI, full-stack apps.
Who uses it: Web developers, app developers, UI/UX enthusiasts.
3. Java
The Enterprise Backbone
Why it stands strong: Portable, secure, scalable โ runs on everything from desktops to Android devices.
Used for: Android apps, enterprise software, backend systems.
Who uses it: Large corporations, Android developers, system architects.
4. C/C++
The Power Players
Why they matter: Super fast, close to the hardware, great for performance-critical apps.
Used for: Game engines, operating systems, embedded systems.
Who uses it: System programmers, game developers, performance-focused engineers.
5. C#
Microsoftโs Darling
Why it's growing: Built into the .NET ecosystem, great for Windows apps and games.
Used for: Desktop applications, Unity game development, enterprise tools.
Who uses it: Game developers, enterprise app developers, Windows lovers.
6. SQL
The Language of Data
Why itโs essential: Every application needs a database โ SQL helps you talk to it.
Used for: Querying databases, reporting, analytics.
Who uses it: Data analysts, backend devs, business intelligence professionals.
7. Go (Golang)
The Modern Minimalist
Why itโs rising: Simple, fast, and built for scale โ ideal for cloud-native apps.
Used for: Web servers, microservices, distributed systems.
Who uses it: Backend engineers, DevOps, cloud developers.
Free Coding Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
1. Python
The Jack of All Trades
Why it's loved: Simple syntax, huge community, beginner-friendly.
Used for: Data Science, Machine Learning, Web Development, Automation.
Who uses it: Data analysts, backend developers, researchers, even kids learning to code.
2. JavaScript
The Language of the Web
Why it's everywhere: Runs in every browser, now also on servers (Node.js).
Used for: Frontend & backend web apps, interactive UI, full-stack apps.
Who uses it: Web developers, app developers, UI/UX enthusiasts.
3. Java
The Enterprise Backbone
Why it stands strong: Portable, secure, scalable โ runs on everything from desktops to Android devices.
Used for: Android apps, enterprise software, backend systems.
Who uses it: Large corporations, Android developers, system architects.
4. C/C++
The Power Players
Why they matter: Super fast, close to the hardware, great for performance-critical apps.
Used for: Game engines, operating systems, embedded systems.
Who uses it: System programmers, game developers, performance-focused engineers.
5. C#
Microsoftโs Darling
Why it's growing: Built into the .NET ecosystem, great for Windows apps and games.
Used for: Desktop applications, Unity game development, enterprise tools.
Who uses it: Game developers, enterprise app developers, Windows lovers.
6. SQL
The Language of Data
Why itโs essential: Every application needs a database โ SQL helps you talk to it.
Used for: Querying databases, reporting, analytics.
Who uses it: Data analysts, backend devs, business intelligence professionals.
7. Go (Golang)
The Modern Minimalist
Why itโs rising: Simple, fast, and built for scale โ ideal for cloud-native apps.
Used for: Web servers, microservices, distributed systems.
Who uses it: Backend engineers, DevOps, cloud developers.
Free Coding Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
โค1๐1
Java coding interview questions
1. Reverse a String:
Write a Java program to reverse a given string.
2. Find the Largest Element in an Array:
Find and print the largest element in an array.
3. Check for Palindrome:
Determine if a given string is a palindrome (reads the same backward as forward).
4. Factorial Calculation:
Write a function to calculate the factorial of a number.
5. Fibonacci Series:
Generate the first n numbers in the Fibonacci sequence.
6. Check for Prime Number:
Write a program to check if a given number is prime.
7. String Anagrams:
Determine if two strings are anagrams of each other.
8. Array Sorting:
Implement sorting algorithms like bubble sort, merge sort, or quicksort.
9. Binary Search:
Implement a binary search algorithm to find an element in a sorted array.
10. Duplicate Elements in an Array:
Find and print duplicate elements in an array.
11. Linked List Reversal:
Reverse a singly-linked list.
12. Matrix Operations:
Perform matrix operations like addition, multiplication, or transpose.
13. Implement a Stack:
Create a stack data structure and implement basic operations (push, pop).
14. Implement a Queue:
Create a queue data structure and implement basic operations (enqueue, dequeue).
15. Inheritance and Polymorphism:
Implement a class hierarchy with inheritance and demonstrate polymorphism.
16. Exception Handling:
Write code that demonstrates the use of try-catch blocks to handle exceptions.
17. File I/O:
Read from and write to a file using Java's file I/O capabilities.
18. Multithreading:
Create a simple multithreaded program and demonstrate thread synchronization.
19. Lambda Expressions:
Use lambda expressions to implement functional interfaces.
20. Recursive Algorithms:
Solve a problem using recursion, such as computing the factorial or Fibonacci sequence.
Best Java Resources: https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
Like for more โค๏ธ
1. Reverse a String:
Write a Java program to reverse a given string.
2. Find the Largest Element in an Array:
Find and print the largest element in an array.
3. Check for Palindrome:
Determine if a given string is a palindrome (reads the same backward as forward).
4. Factorial Calculation:
Write a function to calculate the factorial of a number.
5. Fibonacci Series:
Generate the first n numbers in the Fibonacci sequence.
6. Check for Prime Number:
Write a program to check if a given number is prime.
7. String Anagrams:
Determine if two strings are anagrams of each other.
8. Array Sorting:
Implement sorting algorithms like bubble sort, merge sort, or quicksort.
9. Binary Search:
Implement a binary search algorithm to find an element in a sorted array.
10. Duplicate Elements in an Array:
Find and print duplicate elements in an array.
11. Linked List Reversal:
Reverse a singly-linked list.
12. Matrix Operations:
Perform matrix operations like addition, multiplication, or transpose.
13. Implement a Stack:
Create a stack data structure and implement basic operations (push, pop).
14. Implement a Queue:
Create a queue data structure and implement basic operations (enqueue, dequeue).
15. Inheritance and Polymorphism:
Implement a class hierarchy with inheritance and demonstrate polymorphism.
16. Exception Handling:
Write code that demonstrates the use of try-catch blocks to handle exceptions.
17. File I/O:
Read from and write to a file using Java's file I/O capabilities.
18. Multithreading:
Create a simple multithreaded program and demonstrate thread synchronization.
19. Lambda Expressions:
Use lambda expressions to implement functional interfaces.
20. Recursive Algorithms:
Solve a problem using recursion, such as computing the factorial or Fibonacci sequence.
Best Java Resources: https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
Like for more โค๏ธ
๐2
Top 9 websites for practicing algorithms and Data structure.
โ https://www.hackerrank.com/
โ https://leetcode.com/
โ https://www.codewars.com/
โ https://www.hackerearth.com/for-developers
โ https://coderbyte.com/
โ https://www.coursera.org/browse/computer-science/algorithms
โ https://www.codechef.com/
โ https://codeforces.com/
โ https://www.geeksforgeeks.org/
โ https://www.hackerrank.com/
โ https://leetcode.com/
โ https://www.codewars.com/
โ https://www.hackerearth.com/for-developers
โ https://coderbyte.com/
โ https://www.coursera.org/browse/computer-science/algorithms
โ https://www.codechef.com/
โ https://codeforces.com/
โ https://www.geeksforgeeks.org/
โค1
๐ป Want to Clear Your Next Java Developer Interview?
Prepare these topics to ace your next Java interview! ๐
๐๐จ๐ฉ๐ข๐ ๐: ๐๐ซ๐จ๐ฃ๐๐๐ญ ๐ ๐ฅ๐จ๐ฐ ๐๐ง๐ ๐๐ซ๐๐ก๐ข๐ญ๐๐๐ญ๐ฎ๐ซ๐
๐น Describe your project and its architecture.
๐น Challenges faced and your role in the project.
๐น Tech stack used and the reasoning behind it.
๐น Problem-solving, collaboration, and lessons learned.
๐น Reflect on what you'd do differently.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐จ๐ซ๐ ๐๐๐ฏ๐
๐น String concepts (hashcode, equals).
๐น Immutability, OOPS concepts.
๐น Serialization, Collection Framework.
๐น Exception handling, multithreading.
๐น Java Memory Model, garbage collection.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐๐ฏ๐ ๐/๐๐/๐๐ Features
๐น Java 8 features: Lambda expressions, Stream API, Optional API.
๐น Functional interfaces, default/static methods.
๐น Pattern matching, text blocks, modules.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐ฉ๐ซ๐ข๐ง๐ & ๐๐ฉ๐ซ๐ข๐ง๐ ๐๐จ๐จ๐ญ
๐น Dependency Injection, IOC, Spring MVC.
๐น Bean lifecycle, scopes, profiles.
๐น REST API, CRUD, AOP, Exception handling.
๐น JWT, OAuth, actuators, WebFlux.
๐น Microservices, Spring Cloud, JPA.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐๐ญ๐๐๐๐ฌ๐ (๐๐๐/๐๐จ๐๐๐)
๐น JPA repositories, entity relationships.
๐น SQL queries (e.g., Nth highest salary).
๐น Relational and non-relational DB concepts.
๐น Joins, indexing, stored procedures.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐จ๐๐ข๐ง๐
๐น DSA-based questions.
๐น Sorting/searching with Java APIs.
๐น Stream API coding questions.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐๐ฏ๐๐ฉ๐ฌ & ๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ
๐น Familiarize yourself with tools like Jenkins, Kubernetes, Kafka, and cloud technologies.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐๐ฌ๐ญ ๐๐ซ๐๐๐ญ๐ข๐๐๐ฌ
๐น Master design patterns like singleton, factory, observer, etc.
โจ Nail your interview with confidence! โจ
#JavaInterview #JavaDeveloper
Prepare these topics to ace your next Java interview! ๐
๐๐จ๐ฉ๐ข๐ ๐: ๐๐ซ๐จ๐ฃ๐๐๐ญ ๐ ๐ฅ๐จ๐ฐ ๐๐ง๐ ๐๐ซ๐๐ก๐ข๐ญ๐๐๐ญ๐ฎ๐ซ๐
๐น Describe your project and its architecture.
๐น Challenges faced and your role in the project.
๐น Tech stack used and the reasoning behind it.
๐น Problem-solving, collaboration, and lessons learned.
๐น Reflect on what you'd do differently.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐จ๐ซ๐ ๐๐๐ฏ๐
๐น String concepts (hashcode, equals).
๐น Immutability, OOPS concepts.
๐น Serialization, Collection Framework.
๐น Exception handling, multithreading.
๐น Java Memory Model, garbage collection.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐๐ฏ๐ ๐/๐๐/๐๐ Features
๐น Java 8 features: Lambda expressions, Stream API, Optional API.
๐น Functional interfaces, default/static methods.
๐น Pattern matching, text blocks, modules.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐ฉ๐ซ๐ข๐ง๐ & ๐๐ฉ๐ซ๐ข๐ง๐ ๐๐จ๐จ๐ญ
๐น Dependency Injection, IOC, Spring MVC.
๐น Bean lifecycle, scopes, profiles.
๐น REST API, CRUD, AOP, Exception handling.
๐น JWT, OAuth, actuators, WebFlux.
๐น Microservices, Spring Cloud, JPA.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐๐ญ๐๐๐๐ฌ๐ (๐๐๐/๐๐จ๐๐๐)
๐น JPA repositories, entity relationships.
๐น SQL queries (e.g., Nth highest salary).
๐น Relational and non-relational DB concepts.
๐น Joins, indexing, stored procedures.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐จ๐๐ข๐ง๐
๐น DSA-based questions.
๐น Sorting/searching with Java APIs.
๐น Stream API coding questions.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐๐ฏ๐๐ฉ๐ฌ & ๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ
๐น Familiarize yourself with tools like Jenkins, Kubernetes, Kafka, and cloud technologies.
๐๐จ๐ฉ๐ข๐ ๐: ๐๐๐ฌ๐ญ ๐๐ซ๐๐๐ญ๐ข๐๐๐ฌ
๐น Master design patterns like singleton, factory, observer, etc.
โจ Nail your interview with confidence! โจ
#JavaInterview #JavaDeveloper
๐1
Complete Syllabus for Data Analytics interview:
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting - Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Like for more ๐โค๏ธ
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting - Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Like for more ๐โค๏ธ
๐1
Java for Everything: โ
Java + Spring = Enterprise Applications
Java + Hibernate = Object-Relational Mapping
Java + Android = Mobile App Development
Java + Swing = Desktop GUI Applications
Java + JavaFX = Modern GUI Applications
Java + JUnit = Unit Testing
Java + Maven = Project Management
Java + Jenkins = Continuous Integration
Java + Apache Kafka = Stream Processing
Java + Apache Hadoop = Big Data Processing
Java + Microservices = Scalable Services
Best Programming Resources: https://topmate.io/coding/886839
All the best ๐๐
Java + Spring = Enterprise Applications
Java + Hibernate = Object-Relational Mapping
Java + Android = Mobile App Development
Java + Swing = Desktop GUI Applications
Java + JavaFX = Modern GUI Applications
Java + JUnit = Unit Testing
Java + Maven = Project Management
Java + Jenkins = Continuous Integration
Java + Apache Kafka = Stream Processing
Java + Apache Hadoop = Big Data Processing
Java + Microservices = Scalable Services
Best Programming Resources: https://topmate.io/coding/886839
All the best ๐๐
๐2
Here are 20 essential VS Code shortcuts for beginners:
1. Ctrl + P: Open any file quickly ๐
2. Ctrl + /: Toggle line comment ๐
3. Alt + Up/Down: Move a line up or down โ๏ธ
4. Ctrl + Shift + K: Delete the current line โ
5. Ctrl + B: Show/hide the sidebar ๐
6. Ctrl + Space: Trigger IntelliSense for code suggestions ๐ก
7. Ctrl + Shift + F: Search across files ๐
8. Ctrl + D: Select the next occurrence of the selected text ๐
9. Ctrl + Shift + L: Select all occurrences of the current selection ๐
10. Ctrl + Shift + P: Open the Command Palette ๐
11. Ctrl + F2: Rename all occurrences of a variable โ๏ธ
12. Ctrl + J: Show/hide the integrated terminal ๐ป
13. Ctrl + `: Open a new terminal ๐ง
14. Ctrl + Shift + N: Open a new window ๐ผ๏ธ
15. Ctrl + W: Close the current editor tab ๐๏ธ
16. Ctrl + Shift + E: Focus on the file explorer ๐๏ธ
17. Ctrl + Shift + G: Open the Git view ๐
18. Ctrl + Shift + M: Open the Problems panel ๐จ
19. Alt + Shift + Up/Down: Copy the line up or down ๐
20. Ctrl + Alt + Arrow keys: Split the editor window โ๏ธ
Master these and level up your coding speed! ๐
1. Ctrl + P: Open any file quickly ๐
2. Ctrl + /: Toggle line comment ๐
3. Alt + Up/Down: Move a line up or down โ๏ธ
4. Ctrl + Shift + K: Delete the current line โ
5. Ctrl + B: Show/hide the sidebar ๐
6. Ctrl + Space: Trigger IntelliSense for code suggestions ๐ก
7. Ctrl + Shift + F: Search across files ๐
8. Ctrl + D: Select the next occurrence of the selected text ๐
9. Ctrl + Shift + L: Select all occurrences of the current selection ๐
10. Ctrl + Shift + P: Open the Command Palette ๐
11. Ctrl + F2: Rename all occurrences of a variable โ๏ธ
12. Ctrl + J: Show/hide the integrated terminal ๐ป
13. Ctrl + `: Open a new terminal ๐ง
14. Ctrl + Shift + N: Open a new window ๐ผ๏ธ
15. Ctrl + W: Close the current editor tab ๐๏ธ
16. Ctrl + Shift + E: Focus on the file explorer ๐๏ธ
17. Ctrl + Shift + G: Open the Git view ๐
18. Ctrl + Shift + M: Open the Problems panel ๐จ
19. Alt + Shift + Up/Down: Copy the line up or down ๐
20. Ctrl + Alt + Arrow keys: Split the editor window โ๏ธ
Master these and level up your coding speed! ๐
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