Master Power BI with this Cheat Sheet🔥

If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision!

📝 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗕𝗮𝘀𝗶𝗰𝘀:

DAX Functions:

- SUMX: Sum of values based on a condition.
- FILTER: Filter data based on a given condition.
- RELATED: Retrieve a related column from another table.
- CALCULATE: Perform dynamic calculations.
- EARLIER: Access a column from a higher context.
- CROSSJOIN: Create a Cartesian product of two tables.
- UNION: Combine the results from multiple tables.
- RANKX: Rank data within a column.
- DISTINCT: Filter unique rows.

Data Modeling:

- Relationships: Create, manage, and modify relationships.
- Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year).
- Calculated Columns: Create calculated columns to extend data.
- Measures: Write powerful measures to analyze data effectively.

Data Visualization:

- Charts: Bar charts, line charts, pie charts, and more.
- Table & Matrix: Display tabular data and matrix visuals.
- Slicers: Create interactive filters.
- Tooltips: Enhance visual interactivity with tooltips.
- Map: Display geographical data effectively.

✨ 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗧𝗶𝗽𝘀:

✅ Use DAX for efficient data analysis.

✅ Optimize data models for performance.

✅ Utilize drill-through and drill-down for deeper insights.

✅ Leverage bookmarks for enhanced navigation.

✅ Annotate your reports with comments for clarity.

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Some important questions to crack data science interview

Q. Describe how Gradient Boosting works.

A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.


Q. Describe the decision tree model.

A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.


Q. What is a neural network?

A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.


Q. Explain the Bias-Variance Tradeoff

A. The bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.


Q. What’s the difference between L1 and L2 regularization?

A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.

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Here is an A-Z list of essential programming terms:

1. Array: A data structure that stores a collection of elements of the same type in contiguous memory locations.

2. Boolean: A data type that represents true or false values.

3. Conditional Statement: A statement that executes different code based on a condition.

4. Debugging: The process of identifying and fixing errors or bugs in a program.

5. Exception: An event that occurs during the execution of a program that disrupts the normal flow of instructions.

6. Function: A block of code that performs a specific task and can be called multiple times in a program.

7. GUI (Graphical User Interface): A visual way for users to interact with a computer program using graphical elements like windows, buttons, and menus.

8. HTML (Hypertext Markup Language): The standard markup language used to create web pages.

9. Integer: A data type that represents whole numbers without any fractional part.

10. JSON (JavaScript Object Notation): A lightweight data interchange format commonly used for transmitting data between a server and a web application.

11. Loop: A programming construct that allows repeating a block of code multiple times.

12. Method: A function that is associated with an object in object-oriented programming.

13. Null: A special value that represents the absence of a value.

14. Object-Oriented Programming (OOP): A programming paradigm based on the concept of "objects" that encapsulate data and behavior.

15. Pointer: A variable that stores the memory address of another variable.

16. Queue: A data structure that follows the First-In-First-Out (FIFO) principle.

17. Recursion: A programming technique where a function calls itself to solve a problem.

18. String: A data type that represents a sequence of characters.

19. Tuple: An ordered collection of elements, similar to an array but immutable.

20. Variable: A named storage location in memory that holds a value.

21. While Loop: A loop that repeatedly executes a block of code as long as a specified condition is true.

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🏟 Here is a complete roadmap to learn Data Structures and Algorithms (DSA) 🏟


1. Basics of Programming: Start by learning the basics of a programming language like Python, Java, or C++. Understand concepts like variables, loops, functions, and arrays.

2. Data Structures: Study fundamental data structures like arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Understand the operations that can be performed on these data structures and their time complexities.

3. Algorithms: Learn common algorithms like searching, sorting, recursion, dynamic programming, greedy algorithms, and divide and conquer. Understand how these algorithms work and their time complexities.

4. Problem Solving: Practice solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Start with easy problems and gradually move to medium and hard problems.

5. Complexity Analysis: Learn how to analyze the time and space complexity of algorithms. Understand Big O notation and how to calculate the complexity of different algorithms.

6. Advanced Data Structures: Study advanced data structures like AVL trees, B-trees, tries, segment trees, and fenwick trees. Understand when and how to use these data structures in problem-solving.

7. Graph Algorithms: Learn graph traversal algorithms like BFS and DFS. Study algorithms like Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm for shortest path problems.

8. Dynamic Programming: Master dynamic programming techniques for solving complex problems efficiently. Practice solving dynamic programming problems to build your skills.

9. Practice and Review: Regularly practice coding problems and review your solutions. Analyze your mistakes and learn from them to improve your problem-solving skills.

10. Mock Interviews: Prepare for technical interviews by participating in mock interviews and solving interview-style coding problems. Practice explaining your thought process and reasoning behind your solutions.

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Goldman Sachs senior data analyst interview asked questions

SQL

1 find avg of salaries department wise from table
2 Write a SQL query to see employee name and manager name using a self-join on 'employees' table with columns 'emp_id', 'name', and 'manager_id'.
3 newest joinee for every department (solved using lead lag)

POWER BI

1. What does Filter context in DAX mean?
2. Explain how to implement Row-Level Security (RLS) in Power BI.
3. Describe different types of filters in Power BI.
4. Explain the difference between 'ALL' and 'ALLSELECTED' in DAX.
5. How do you calculate the total sales for a specific product using DAX?

PYTHON

1. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
2. Find unique values in a list of assorted numbers and print the count of how many times each value is repeated.
3. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.

Hope this helps you 😊

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For a data analytics interview, focusing on key SQL topics can be crucial. Here's a list of last-minute SQL topics to revise:

1. SQL Basics:
• SELECT statements: Syntax, SELECT DISTINCT
• WHERE clause: Conditions and operators (>, <, =, LIKE, IN, BETWEEN)
• ORDER BY clause: Sorting results
• LIMIT clause: Limiting the number of rows returned

2. Joins:
• INNER JOIN
• LEFT (OUTER) JOIN
• RIGHT (OUTER) JOIN
• FULL (OUTER) JOIN
• CROSS JOIN
• Understanding join conditions and scenarios for each type of join

3. Aggregation and Grouping:
• GROUP BY clause
• HAVING clause: Filtering grouped results
• Aggregate functions: COUNT, SUM, AVG, MIN, MAX

4. Subqueries:
• Nested subqueries: Using subqueries in SELECT, FROM, WHERE, and HAVING clauses
• Correlated subqueries

5. Common Table Expressions (CTEs):
• Syntax and use cases for CTEs (WITH clause)

6. Window Functions:
• ROW_NUMBER()
• RANK()
• DENSE_RANK()
• LEAD() and LAG()
• PARTITION BY clause

7. Data Manipulation:
• INSERT, UPDATE, DELETE statements
• Understanding transaction control with COMMIT and ROLLBACK

8. Data Definition:
• CREATE TABLE
• ALTER TABLE
• DROP TABLE
• Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL

9. Indexing:
• Purpose and types of indexes
• How indexing affects query performance

10. Performance Optimization:
• Understanding query execution plans
• Identifying and resolving common performance issues

11. SQL Functions:
• String functions: CONCAT, SUBSTRING, LENGTH
• Date functions: DATEADD, DATEDIFF, GETDATE
• Mathematical functions: ROUND, CEILING, FLOOR

12. Stored Procedures and Triggers:
• Basics of writing and using stored procedures
• Basics of writing and using triggers

13. ETL (Extract, Transform, Load):
• Understanding the process and SQL's role in ETL operations

14. Advanced Topics (if time permits):
• Understanding complex data types (JSON, XML)
• Working with large datasets and big data considerations

Hope it helps :)

Top 9 Http Methods-

GET 🧐 - Retrieve data from a resource.
HEAD 🎧 - Retrieve the headers of a resource.
POST 📮 - Submit data to a resource.
PUT 📥 - Update an existing resource or create a new resource.
DELETE 🗑️ - Remove a resource.
CONNECT 🔗 - Establish a network connection for a resource.
OPTIONS ⚙️ - Describe communication options for the target resource.
TRACE 🕵️‍♂️ - Retrieve a diagnostic trace of the request.
PATCH 🩹 - Apply a partial update to a resource.

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