Power BI Cheat Sheet ✍

This Power BI cheatsheet is designed to be your quick reference guide for creating impactful reports and dashboards. Whether you’re a beginner exploring the basics or an experienced developer looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting Data
   - Import Data: *Home > Get Data > Select Data Source*
   - Direct Query: *Home > Get Data > Select Data Source > Direct Query*

2. Data Transformation
   - Power Query Editor: *Home > Transform Data*
   - Remove Columns: *Transform > Remove Columns*
   - Split Columns: *Transform > Split Column by Delimiter*
   - Replace Values: *Transform > Replace Values*

3. Data Modeling
   - Create Relationships: *Model > Manage Relationships > New*
   - Edit Relationships: *Model > Manage Relationships > Edit*

4. DAX Calculations
   - New Measure: *Modeling > New Measure*
   - Common DAX Functions:
     - SUM: SUM(table[column])
     - AVERAGE: AVERAGE(table[column])
     - IF: IF(condition, true_value, false_value)
     - COUNTROWS: COUNTROWS(table)
     - CALCULATE: CALCULATE(expression, filter)

5. Creating Visuals
   - Select Visualization: *Visualizations Pane > Select Visual Type*
   - Bar Chart: *Bar Chart Icon*
   - Pie Chart: *Pie Chart Icon*
   - Map Visual: *Map Icon*

6. Formatting Visuals
   - Change Colors: *Format > Data Colors*
   - Customize Titles: *Format > Title > Text*
   - Adjust Axis: *Format > Y-Axis / X-Axis*

7. Filters
   - Visual Level Filter: *Filter Pane > Add Filter for Selected Visual*
   - Page Level Filter: *Filter Pane > Add Filter for Entire Page*
   - Report Level Filter: *Filter Pane > Add Filter for Entire Report*

8. Slicers
   - Add Slicer: *Visualizations > Slicer Icon*
   - Customize Slicer: *Format > Edit Interactions*

9. Drillthrough
   - Add Drillthrough: *Pages > Right Click on Field > Drillthrough*
   - Back Button: *Insert > Button > Back Button*

10. Publishing & Sharing
    - Publish Report: *Home > Publish > Select Workspace*
    - Share Report: *File > Share > Publish to Web or Power BI Service*

11. Dashboards
    - Create Dashboard: *Power BI Service > New Dashboard*
    - Pin Visuals: *Pin Icon on Visual > Pin to Dashboard*

12. Export Options
    - Export to PDF: *File > Export > PDF*
    - Export Data: *Visual Options > Export Data*

Hope it helps :)

16 Power BI Interview Questions with Answers for you

1. How do you improve the performance of a Power BI report?
Optimize data models, use aggregations, limit visuals on a report page, apply filters to reduce data load, and enable query folding.

2. What type of data sources did you use in your previous project?
Depending on the project requirements, common sources include SQL Server, Azure, Excel, and APIs.

3. What is the difference between Star Schema and Snowflake Schema?
A Star Schema has a central fact table connected to dimension tables, while Snowflake Schema normalizes dimension tables into sub-tables.

4. What is the CALCULATED function in Power BI?
CALCULATED modifies the context of a calculation and returns the result based on the new filters.

5. Tell me about complex DAX functions you used.
I have used functions like CALCULATE, FILTER, and RANKX to create complex calculations for performance metrics.

6. How do you install the Data Gateway in Power BI?
Download the Gateway from Power BI service, install it, and configure it with the on-premises data source.

7. What is the difference between Data Query and Data Flow?
Data Query refers to queries in Power Query Editor, while Data Flow is used to transform and reuse data in multiple datasets.

8. What is the difference between Calculated Columns and Measures?
Calculated columns are static and stored in the model, while measures are dynamic and calculated during report interaction.

9. What is Query Folding in Power BI?
A: Query folding optimizes queries by delegating transformations back to the data source to improve performance.

10. What is the difference between RANK and DENSE RANK in SQL?
A: RANK skips numbers for ties, whereas DENSE RANK assigns consecutive ranks even with ties.

11. What are the types of Context in Power BI?
There are three types: Row Context, Filter Context, and Query Context, used to manage how data is filtered and displayed.

12. Give an example of INNER JOIN in SQL.
SELECT * FROM Orders o INNER JOIN Customers c ON o.CustomerID = c.CustomerID;
returns rows where the condition is met.

13. What is the difference between Filter and Slicer in Power BI?
Filters are applied in the backend, while slicers are visual controls for users to filter data interactively on the report.

14. What are the fundamentals of Power BI?
Power BI fundamentals include data modeling, DAX calculations, data visualization, and report sharing through the Power BI Service.

15. What is Row Level Security in Power BI?
It defines rules to control which data users can view in Power BI reports, based on their roles.

16. What are the different types of subscriptions in Power BI Report Server?
Subscriptions include email-based report delivery, SharePoint delivery, and Power BI Service for scheduled data refresh.

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4. Resume and Cover Letter: Craft a tailored resume and cover letter for each job application. Highlight relevant skills and experiences that match the job requirements.📇

5. Job Search Platforms: Utilize job search websites like LinkedIn, Indeed, Glassdoor, and specialized platforms like Stack Overflow Jobs, GitHub Jobs, or AngelList for tech-related positions. 🔍

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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
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s 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.)

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🚀 Roadmap to Become a Java Developer 🔰

📂 Programming Basics
 ∟📂 Master Java Syntax, Variables & Data Types
  ∟📂 Learn Control Flow, Loops & Exception Handling
   ∟📂 Practice with Basic Programs

📂 Object-Oriented Programming (OOP)
 ∟📂 Understand Classes, Objects & Inheritance
  ∟📂 Dive into Encapsulation, Polymorphism & Abstraction
   ∟📂 Explore Design Patterns in Java

📂 Tools & Build Systems
 ∟📂 Get Comfortable with IDEs (e.g., IntelliJ, Eclipse)
  ∟📂 Master Maven & Gradle for Dependency Management
   ∟📂 Learn Git & Version Control Systems

📂 Data Structures & Algorithms
 ∟📂 Learn Java Collections (Lists, Sets, Maps, etc.)
  ∟📂 Understand Algorithms & Problem Solving
   ∟📂 Tackle Coding Challenges

📂 Web Development & APIs
 ∟📂 Learn Servlets & JSP Basics
  ∟📂 Dive into Spring Boot & RESTful Services
   ∟📂 Understand Microservices Architecture

📂 Database & Persistence
 ∟📂 Master JDBC & SQL Fundamentals
  ∟📂 Explore ORM Tools like Hibernate & JPA
   ∟📂 Practice with Real-World Data

📂 Testing & Debugging
 ∟📂 Get Started with JUnit & Test-Driven Development
  ∟📂 Learn Debugging Techniques & Best Practices
   ∟📂 Automate Testing with Tools

📂 Advanced Topics
 ∟📂 Learn Multithreading & Concurrency
  ∟📂 Understand JVM Internals & Garbage Collection
   ∟📂 Explore Performance Optimization

📂 Projects & Real-World Applications
 ∟📂 Build End-to-End Java Applications
  ∟📂 Contribute to Open-Source Projects
   ∟📂 Showcase on GitHub & Portfolio

📂 Interview Preparation & Job Hunting
 ∟📂 Solve Java Coding Challenges
  ∟📂 Master System Design & Architecture Concepts
   ∟📂 Network & Apply for Java Roles

✅️ Get Hired

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