Questions & Answers for Data Analyst Interview

Question 1: Describe a time when you used data analysis to solve a business problem.
Ideal answer: This is your opportunity to showcase your data analysis skills in a real-world context. Be specific and provide examples of your work. For example, you could talk about a time when you used data analysis to identify customer churn, improve marketing campaigns, or optimize product development.

Question 2: What are some of the challenges you have faced in previous data analysis projects, and how did you overcome them?
Ideal answer: This question is designed to assess your problem-solving skills and your ability to learn from your experiences. Be honest and upfront about the challenges you have faced, but also focus on how you overcame them. For example, you could talk about a time when you had to deal with a large and messy dataset, or a time when you had to work with a tight deadline.

Question 3: How do you handle missing values in a dataset?
Ideal answer: Missing values are a common problem in data analysis, so it is important to know how to handle them properly. There are a variety of different methods that you can use, depending on the specific situation. For example, you could delete the rows with missing values, impute the missing values using a statistical method, or assign a default value to the missing values.

Question 4: How do you identify and remove outliers?
Ideal answer: Outliers are data points that are significantly different from the rest of the data. They can be caused by data errors or by natural variation in the data. It is important to identify and remove outliers before performing data analysis, as they can skew the results. There are a variety of different methods that you can use to identify outliers, such as the interquartile range (IQR) method or the standard deviation method.

Question 5: How do you interpret and communicate the results of your data analysis to non-technical audiences?
Ideal answer: It is important to be able to communicate your data analysis findings to both technical and non-technical audiences. When communicating to non-technical audiences, it is important to avoid using jargon and to focus on the key takeaways from your analysis. You can use data visualization tools to help you communicate your findings in a clear and concise way.
In addition to providing specific examples and answers to the questions, it is also important to be enthusiastic and demonstrate your passion for data analysis. Show the interviewer that you are excited about the opportunity to use your skills to solve real-world problems.

Array Sorting Algorithms

Different Types of Data Structures Explained shortly

Top Libraries & Frameworks by Language 📚💻

❯ Python
 • Pandas ➟ Data Analysis
 • NumPy ➟ Math & Arrays
 • Scikit-learn ➟ Machine Learning
 • TensorFlow / PyTorch ➟ Deep Learning
 • Flask / Django ➟ Web Development
 • OpenCV ➟ Image Processing

❯ JavaScript / TypeScript
 • React ➟ UI Development
 • Vue ➟ Lightweight SPAs
 • Angular ➟ Enterprise Apps
 • Next.js ➟ Full-Stack Web
 • Express ➟ Backend APIs
 • Three.js ➟ 3D Web Graphics

❯ Java
 • Spring Boot ➟ Microservices
 • Hibernate ➟ ORM
 • Apache Maven ➟ Build Automation
 • Apache Kafka ➟ Real-Time Data

❯ C++
 • Boost ➟ Utility Libraries
 • Qt ➟ GUI Applications
 • Unreal Engine ➟ Game Development

❯ C#
 • .NET / ASP.NET ➟ Web Apps
 • Unity ➟ Game Development
 • Entity Framework ➟ ORM

❯ R
 • ggplot2 ➟ Data Visualization
 • dplyr ➟ Data Manipulation
 • caret ➟ Machine Learning
 • Shiny ➟ Interactive Dashboards

❯ PHP
 • Laravel ➟ Full-Stack Web
 • Symfony ➟ Web Framework
 • PHPUnit ➟ Testing

❯ Go (Golang)
 • Gin ➟ Web Framework
 • Gorilla ➟ Web Toolkit
 • GORM ➟ ORM for Go

❯ Rust
 • Actix ➟ Web Framework
 • Rocket ➟ Web Development
 • Tokio ➟ Async Runtime

Coding Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17

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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.

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://t.iss.one/DataSimplifier

Hope this helps you 😊

Follow this to optimise your linkedin profile 👇👇

Step 1: Upload a professional (looking) photo as this is your first impression

Step 2: Add your Industry and Location. Location is one of the top 5 fields that LinkedIn prioritizes when doing a key-word search. The other 4 fields are: Name, Headline, Summary and Experience.

Step 3: Customize your LinkedIn URL. To do this click on “Edit your public profile”

Step 4: Write a summary. This is a great opportunity to communicate your brand, as well as, use your key words. As a starting point you can use summary from your resume.

Step 5: Describe your experience with relevant keywords.

Step 6: Add 5 or more relevant skills.

Step 7: List your education with specialization.

Step 8: Connect with 500+ contacts in your industry to expand your network.

Step 9: Turn ON “Let recruiters know you’re open”

🔰 DevOps Roadmap for Beginners 2025

├── 🧠 What is DevOps? Principles & Culture
├── 🧪 Mini Task: Set up Local CI Pipeline with Shell Scripts
├── ⚙️ Linux Basics: Commands, Shell Scripting
├── 📁 Version Control: Git, GitHub, GitLab
├── 🧪 Mini Task: Automate Deployment via GitHub Actions
├── 📦 Package Managers & Artifact Repositories (npm, pip, DockerHub)
├── 🐳 Docker Essentials: Images, Containers, Volumes, Networks
├── 🧪 Mini Project: Dockerize a MERN App
├── ☁️ CI/CD Concepts & Tools (Jenkins, GitHub Actions)
├── 🧪 Mini Project: CI/CD Pipeline for React App
├── 🧩 Infrastructure as Code: Terraform / Ansible Basics
├── 📈 Monitoring & Logging: Prometheus, Grafana, ELK Stack
├── 🔐 Secrets Management & Security Basics (Vault, .env)
├── 🌐 Web Servers: Nginx, Apache (Reverse Proxy, Load Balancer)
├── ☁️ Cloud Providers: AWS (EC2, S3, IAM), GCP, Azure Overview

React with ♥️ if you want me to explain each topic in detail

#devops

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