DATA SCIENCE INTERVIEW QUESTIONS WITH ANSWERS


1. What are the assumptions required for linear regression? What if some of these assumptions are violated?

Ans: The assumptions are as follows:

The sample data used to fit the model is representative of the population

The relationship between X and the mean of Y is linear

The variance of the residual is the same for any value of X (homoscedasticity)

Observations are independent of each other

For any value of X, Y is normally distributed.

Extreme violations of these assumptions will make the results redundant. Small violations of these assumptions will result in a greater bias or variance of the estimate.


2.What is multicollinearity and how to remove it?

Ans: Multicollinearity exists when an independent variable is highly correlated with another independent variable in a multiple regression equation. This can be problematic because it undermines the statistical significance of an independent variable.

You could use the Variance Inflation Factors (VIF) to determine if there is any multicollinearity between independent variables — a standard benchmark is that if the VIF is greater than 5 then multicollinearity exists.


3. What is overfitting and how to prevent it?

Ans: Overfitting is an error where the model ‘fits’ the data too well, resulting in a model with high variance and low bias. As a consequence, an overfit model will inaccurately predict new data points even though it has a high accuracy on the training data.

Few approaches to prevent overfitting are:

- Cross-Validation:Cross-validation is a powerful preventative measure against overfitting. Here we use our initial training data to generate multiple mini train-test splits. Now we use these splits to tune our model.

- Train with more data: It won’t work every time, but training with more data can help algorithms detect the signal better or it can help my model to understand general trends in particular.

- We can remove irrelevant information or the noise from our dataset.

- Early Stopping: When you’re training a learning algorithm iteratively, you can measure how well each iteration of the model performs.

Up until a certain number of iterations, new iterations improve the model. After that point, however, the model’s ability to generalize can weaken as it begins to overfit the training data.

Early stopping refers stopping the training process before the learner passes that point.

- Regularization: It refers to a broad range of techniques for artificially forcing your model to be simpler. There are mainly 3 types of Regularization techniques:L1, L2,&,Elastic- net.

- Ensembling : Here we take number of learners and using these we get strong model. They are of two types : Bagging and Boosting.


4. Given two fair dices, what is the probability of getting scores that sum to 4 and 8?

Ans: There are 4 combinations of rolling a 4 (1+3, 3+1, 2+2):
P(rolling a 4) = 3/36 = 1/12

There are 5 combinations of rolling an 8 (2+6, 6+2, 3+5, 5+3, 4+4):
P(rolling an 8) = 5/36

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If you want to Excel at Frontend Development and build stunning user interfaces, master these essential skills:

Core Technologies:

• HTML5 & Semantic Tags – Clean and accessible structure
• CSS3 & Preprocessors (SASS, SCSS) – Advanced styling
• JavaScript ES6+ – Arrow functions, Promises, Async/Await

CSS Frameworks & UI Libraries:

• Bootstrap & Tailwind CSS – Speed up styling
• Flexbox & CSS Grid – Modern layout techniques
• Material UI, Ant Design, Chakra UI – Prebuilt UI components

JavaScript Frameworks & Libraries:

• React.js – Component-based UI development
• Vue.js / Angular – Alternative frontend frameworks
• Next.js & Nuxt.js – Server-side rendering (SSR) & static site generation

State Management:

• Redux / Context API (React) – Manage complex state
• Pinia / Vuex (Vue) – Efficient state handling

API Integration & Data Handling:

• Fetch API & Axios – Consume RESTful APIs
• GraphQL & Apollo Client – Query APIs efficiently

Frontend Optimization & Performance:

• Lazy Loading & Code Splitting – Faster load times
• Web Performance Optimization (Lighthouse, Core Web Vitals)

Version Control & Deployment:

• Git & GitHub – Track changes and collaborate
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AI Engineering has levels to it:

– Level 1: Using AI
Start by mastering the fundamentals:
-- Prompt engineering (zero-shot, few-shot, chain-of-thought)
-- Calling APIs (OpenAI, Anthropic, Cohere, Hugging Face)
-- Understanding tokens, context windows, and parameters (temperature, top-p)

With just these basics, you can already solve real problems.

– Level 2: Integrating AI
Move from using AI to building with it:
-- Retrieval Augmented Generation (RAG) with vector databases (Pinecone, FAISS, Weaviate, Milvus)
-- Embeddings and similarity search (cosine, Euclidean, dot product)
-- Caching and batching for cost and latency improvements
-- Agents and tool use (safe function calling, API orchestration)

This is the foundation of most modern AI products.

– Level 3: Engineering AI Systems
Level up from prototypes to production-ready systems:
-- Fine-tuning vs instruction-tuning vs RLHF (know when each applies)
-- Guardrails for safety and compliance (filters, validators, adversarial testing)
-- Multi-model architectures (LLMs + smaller specialized models)
-- Evaluation frameworks (BLEU, ROUGE, perplexity, win-rates, human evals)

Here’s where you shift from “it works” to “it works reliably.”

– Level 4: Optimizing AI at Scale
Finally, learn how to run AI systems efficiently and responsibly:
-- Distributed inference (vLLM, Ray Serve, Hugging Face TGI)
-- Managing context length and memory (chunking, summarization, attention strategies)
-- Balancing cost vs performance (open-source vs proprietary tradeoffs)
-- Privacy, compliance, and governance (PII redaction, SOC2, HIPAA, GDPR)

At this stage, you’re not just building AI—you’re designing systems that scale in the real world.

Tableau Cheat Sheet ✅

This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting to Data
- Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.).

2. Data Preparation
- Data Interpreter: Clean data automatically using the Data Interpreter.
- Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
- Union Data: Stack data from multiple tables with the same structure.

3. Creating Views
- Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
- Show Me: Use the *Show Me* panel to select different visualization types.

4. Types of Visualizations
- Bar Chart: Compare values across categories.
- Line Chart: Display trends over time.
- Pie Chart: Show proportions of a whole (use sparingly).
- Map: Visualize geographic data.
- Scatter Plot: Show relationships between two variables.

5. Filters
- Dimension Filters: Filter data based on categorical values.
- Measure Filters: Filter data based on numerical values.
- Context Filters: Set a context for other filters to improve performance.

6. Calculated Fields
- Create calculated fields to derive new data:
- Example: Sales Growth = SUM([Sales]) - SUM([Previous Sales])

7. Parameters
- Use parameters to allow user input and control measures dynamically.

8. Formatting
- Format fonts, colors, borders, and lines using the Format pane for better visual appeal.

9. Dashboards
- Combine multiple sheets into a dashboard using the *Dashboard* tab.
- Use dashboard actions (filter, highlight, URL) to create interactivity.

10. Story Points
- Create a story to guide users through insights with narrative and visualizations.

11. Publishing & Sharing
- Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.

12. Export Options
- Export to PDF or image for offline use.

13. Keyboard Shortcuts
- Show/Hide Sidebar: Ctrl+Alt+T
- Duplicate Sheet: Ctrl + D
- Undo: Ctrl + Z
- Redo: Ctrl + Y

14. Performance Optimization
- Use extracts instead of live connections for faster performance.
- Optimize calculations and filters to improve dashboard loading times.

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Important Excel, Tableau, Statistics, SQL related Questions with answers

1. What are the common problems that data analysts encounter during analysis?

The common problems steps involved in any analytics project are:

Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues

2. Explain the Type I and Type II errors in Statistics?

In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.

A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.

3. How do you make a dropdown list in MS Excel?

First, click on the Data tab that is present in the ribbon.
Under the Data Tools group, select Data Validation.
Then navigate to Settings > Allow > List.
Select the source you want to provide as a list array.

4. How do you subset or filter data in SQL?

To subset or filter data in SQL, we use WHERE and HAVING clauses which give us an option of including only the data matching certain conditions.

5. What is a Gantt Chart in Tableau?

A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project

5 Easy Projects to Build as a Beginner

(No AI degree needed. Just curiosity & coffee.)

❯ 1. Calculator App
 • Learn logic building
 • Try it in Python, JavaScript or C++
 • Bonus: Add GUI using Tkinter or HTML/CSS

❯ 2. Quiz App (with Score Tracker)
 • Build a fun MCQ quiz
 • Use basic conditions, loops, and arrays
 • Add a timer for extra challenge!

❯ 3. Rock, Paper, Scissors Game
 • Classic game using random choice
 • Great to practice conditions and user input
 • Optional: Add a scoreboard

❯ 4. Currency Converter
 • Convert from USD to INR, EUR, etc.
 • Use basic math or try fetching live rates via API
 • Build a mini web app for it!

❯ 5. To-Do List App
 • Create, read, update, delete tasks
 • Perfect for learning arrays and functions
 • Bonus: Add local storage (in JS) or file saving (in Python)


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©How fresher can get a job as a data scientist?©

India as a job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?

The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.

Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.

All the major data science jobs for freshers will only be available through off-campus interviews.

Some companies that hires data scientists are:

Siemens

Accenture

IBM

Cerner

Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.

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A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

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Technical skills might get you through initial rounds, but behavioral rounds are where many stumble — especially with senior managers who really want to know if you fit the team.

Here’s how to ace it:

1️⃣ When HR shares your interviewer's name, hunt for their LinkedIn profile.

2️⃣ Check out their work history and interests to find common ground.

3️⃣ Mention something relevant during the chat — it shows you’ve done your homework and builds rapport.

4️⃣ Remember, this round is two-way: they’re checking if you suit their culture, and you’re seeing if they suit your career goals.

5️⃣ So, ask smart questions about the role and company culture — it proves you’re genuinely interested.

💡 𝗣𝗿𝗼 𝘁𝗶𝗽: Stay polite but confident; senior leaders love that mix!

Creating a data science and machine learning project involves several steps, from defining the problem to deploying the model. Here is a general outline of how you can create a data science and ML project:

1. Define the Problem: Start by clearly defining the problem you want to solve. Understand the business context, the goals of the project, and what insights or predictions you aim to derive from the data.

2. Collect Data: Gather relevant data that will help you address the problem. This could involve collecting data from various sources, such as databases, APIs, CSV files, or web scraping.

3. Data Preprocessing: Clean and preprocess the data to make it suitable for analysis and modeling. This may involve handling missing values, encoding categorical variables, scaling features, and other data cleaning tasks.

4. Exploratory Data Analysis (EDA): Perform exploratory data analysis to understand the data better. Visualize the data, identify patterns, correlations, and outliers that may impact your analysis.

5. Feature Engineering: Create new features or transform existing features to improve the performance of your machine learning model. Feature engineering is crucial for building a successful ML model.

6. Model Selection: Choose the appropriate machine learning algorithm based on the problem you are trying to solve (classification, regression, clustering, etc.). Experiment with different models and hyperparameters to find the best-performing one.

7. Model Training: Split your data into training and testing sets and train your machine learning model on the training data. Evaluate the model's performance on the testing data using appropriate metrics.

8. Model Evaluation: Evaluate the performance of your model using metrics like accuracy, precision, recall, F1-score, ROC-AUC, etc. Make sure to analyze the results and iterate on your model if needed.

9. Deployment: Once you have a satisfactory model, deploy it into production. This could involve creating an API for real-time predictions, integrating it into a web application, or any other method of making your model accessible.

10. Monitoring and Maintenance: Monitor the performance of your deployed model and ensure that it continues to perform well over time. Update the model as needed based on new data or changes in the problem domain.

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