Data Science Interview Preparation

 🔧 1. Python Programming

Data structures (lists, dicts, sets, tuples)

List comprehensions, lambda functions

Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn

📊 2. Statistics & Probability

Descriptive stats: mean, median, mode, variance

Probability distributions: Normal, Binomial, Poisson

Hypothesis testing (t-tests, p-values, confidence intervals)

Bayes’ Theorem

📈 3. Machine Learning Algorithms

Supervised: Linear/Logistic Regression, Decision Trees, SVMs, k-NN

Unsupervised: k-Means, Hierarchical Clustering, PCA

Ensemble: Random Forest, Gradient Boosting (XGBoost, LightGBM)

🔍 4. Model Evaluation Metrics

Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC

Regression: MAE, MSE, RMSE, R²

🔧 5. Feature Engineering

Handling missing data

Encoding categorical variables

Feature scaling (StandardScaler, MinMaxScaler)

Feature selection techniques

💡 6. Data Cleaning & Preprocessing

Handling outliers

Imputing missing values

Text cleaning (for NLP tasks)

📂 7. Data Analysis & Visualization

EDA using pandas, matplotlib, seaborn, plotly

Correlation analysis

Visual storytelling

🧠 8. Deep Learning (Basics)

Neural Networks: architecture, activation functions

CNNs and RNNs (basic understanding)

Frameworks: TensorFlow, PyTorch (optional for DS roles)

🏗️ 9. SQL & Databases

SELECT, JOINs, GROUP BY, HAVING, subqueries, window functions

Writing efficient queries

Normalization

🧠 10. Problem Solving & Case Studies

Business scenario interpretation

Data-driven decision-making

Common case study themes: churn prediction, A/B testing, fraud detection

⚖️ 11. A/B Testing & Experimentation

Hypothesis testing in experiments

Understanding control vs treatment

Significance levels and power

🧮 12. Linear Algebra (Basics)

Vectors, matrices, matrix multiplication

Eigenvalues and eigenvectors

Applications in PCA, ML models

💻 13. Algorithms & Data Structures (DSA)

Big-O complexity

Trees, graphs, stacks, heaps (if applying to tech-heavy DS roles)

🧰 14. Version Control (Git)

Basic Git commands: clone, commit, push, pull, merge

Using GitHub for code sharing

🌐 15. APIs & Web Scraping

Using requests, BeautifulSoup, or Selenium

Consuming REST APIs

Basic knowledge of JSON data handling

📦 16. Pipelines & MLOps (Optional but a plus)

Data pipelines: Airflow, Luigi

Model deployment basics: Flask, Docker, FastAPI

Model monitoring

🧑‍💼 17. Behavioral Interview Prep

STAR method (Situation, Task, Action, Result)

Tell me about yourself, strength/weakness, conflict resolution

Team collaboration and communication

📚 18. Portfolio & Projects

Kaggle competitions

End-to-end personal projects

GitHub repositories with clean code and documentation

🧑‍🏫 19. Explaining Complex Topics Simply

Practice explaining models like Random Forests to non-technical people

Use analogies, visuals, and storytelling

❓ 20. Mock Interviews & Practice

Leetcode for coding

Interview practice platforms: Interviewing.io, Pramp

Discussing projects and whiteboarding ML solutions

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