Data Science/ Data Analyst Interviews

Questions-answers/ materials I use to study for data science interviews
Some credits to: this repo

Note: Do contribute with PRs! Repo is very messy atm, sorry.

Coding Interviews:

• Used Stratascratch a lot for data analytics interview preparation
• Leetcode - Database
• HackerRank
• Grind75
• Leetcode - Top 150 Questions
• deep-ml

Video Materials:

1. Statquest - Machine Learning/ Statistics/ Deep Learning
2. 3b1b - Math for Machine Learning
3. Andrej kaparthy - LLM Legend
4. Indently - Good coding practices

Reading Materials:

Supervised Learning Algorithms:

1. Linear Regression
   • https://www.analyticsvidhya.com/blog/2021/06/linear-regression-in-machine-learning/
   • Used for regression tasks to predict a continuous target variable.
2. Logistic Regression
   • https://www.analyticsvidhya.com/blog/2021/10/building-an-end-to-end-logistic-regression-model/
   • Used for binary classification problems.
3. Decision Trees
   • https://www.analyticsvidhya.com/blog/2021/08/decision-tree-algorithm/
   • A tree-based model for both classification and regression tasks.
4. Random Forest
   • https://www.analyticsvidhya.com/blog/2021/06/understanding-random-forest/
   • An ensemble method based on multiple decision trees.
5. Support Vector Machines (SVM)
   • https://www.analyticsvidhya.com/blog/2021/10/support-vector-machinessvm-a-complete-guide-for-beginners/
   • Used for classification tasks by finding the optimal hyperplane.
6. K-Nearest Neighbors (KNN)
   • https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/
   • A simple classification algorithm that assigns a class based on the majority class of nearest neighbors.
7. Naive Bayes
   • https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/
   • A probabilistic classifier based on Bayes' theorem with strong independence assumptions.
8. Gradient Boosting Machines (GBM)
   • https://www.analyticsvidhya.com/blog/2021/09/gradient-boosting-algorithm-a-complete-guide-for-beginners/
   • An ensemble method that builds models sequentially to reduce errors.
9. AdaBoost
   • https://www.analyticsvidhya.com/blog/2021/09/adaboost-algorithm-a-complete-guide-for-beginners/
   • https://www.analyticsvidhya.com/blog/2021/03/introduction-to-adaboost-algorithm-with-python/
   • A boosting method that combines weak learners to form a strong classifier.
10. XGBoost
    • https://www.analyticsvidhya.com/blog/2018/09/an-end-to-end-guide-to-understand-the-math-behind-xgboost/
    • https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble-models/
    • A popular gradient-boosting framework optimized for performance.

Unsupervised Learning Algorithms:

1. K-Means Clustering
   • https://www.analyticsvidhya.com/blog/2019/08/comprehensive-guide-k-means-clustering/
   • A popular clustering algorithm that partitions data into K clusters.
2. Hierarchical Clustering
   • https://www.analyticsvidhya.com/blog/2022/11/hierarchical-clustering-in-machine-learning/
   • https://www.analyticsvidhya.com/blog/2021/06/single-link-hierarchical-clustering-clearly-explained/
   • A clustering technique that builds a hierarchy of clusters.
3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
   • https://www.analyticsvidhya.com/blog/2021/06/understand-the-dbscan-clustering-algorithm/
   • https://www.analyticsvidhya.com/blog/2020/09/how-dbscan-clustering-works/
   • Clustering algorithm that groups together closely packed points and marks outliers.
4. Principal Component Analysis (PCA)
   • https://www.analyticsvidhya.com/blog/2016/03/pca-practical-guide-principal-component-analysis-python/
   • https://towardsdatascience.com/the-mathematics-behind-principal-component-analysis-fff2d7f4b643
   • A dimensionality reduction technique used to project data onto fewer dimensions.
5. t-SNE (t-distributed Stochastic Neighbor Embedding)
   • https://medium.com/@sachinsoni600517/mastering-t-sne-t-distributed-stochastic-neighbor-embedding-0e365ee898ea
   • A non-linear dimensionality reduction method for visualizing high-dimensional data.

Reinforcement Learning Algorithms:

• Intro: https://ketanhdoshi.github.io/Reinforcement-Learning-Intro/
• Solution Approaches: https://ketanhdoshi.github.io/Reinforcement-Learning-Solutions/
• Model Free Solution: https://ketanhdoshi.github.io/Reinforcement-Learning-Model/

1. Q-Learning
   • https://ketanhdoshi.github.io/Reinforcement-Learning-Q-Learning/
   • A model-free reinforcement learning algorithm based on learning a Q-value function.
2. Deep Q-Networks (DQN)
   • https://ketanhdoshi.github.io/Reinforcement-Learning-Deep-Q-Network/
   • A combination of Q-learning and deep neural networks.
3. Policy Gradient Methods
   • https://ketanhdoshi.github.io/Reinforcement-Learning-Policy-Gradients/
   • Learn policies directly instead of learning a value function.
4. Proximal Policy Optimization (PPO)
   • https://towardsdatascience.com/proximal-policy-optimization-ppo-explained-abed1952457b
   • A modern, stable policy optimization method used in reinforcement learning.
5. SARSA (State-Action-Reward-State-Action)
   • A reinforcement learning algorithm that updates policies based on current actions.

Neural Networks and Deep Learning Algorithms:

1. Artificial Neural Networks (ANN)
   • The basic neural network model used for various tasks.
2. Convolutional Neural Networks (CNN)
   • https://medium.com/thedeephub/convolutional-neural-networks-a-comprehensive-guide-5cc0b5eae175
   • https://medium.com/@beingfarina/breaking-down-the-mathematics-behind-cnn-models-a-comprehensive-guide-1853aa6b011e
   • Primarily used for image recognition tasks.
3. Recurrent Neural Networks (RNN)
   • https://www.analyticsvidhya.com/blog/2017/12/introduction-to-recurrent-neural-networks/
   • https://www.analyticsvidhya.com/blog/2022/03/a-brief-overview-of-recurrent-neural-networks-rnn/
   • https://karpathy.github.io/2015/05/21/rnn-effectiveness/
   • Used for sequential data tasks like time series or natural language processing.
4. Long Short-Term Memory Networks (LSTM)
   • https://medium.com/@ottaviocalzone/an-intuitive-explanation-of-lstm-a035eb6ab42c
   • https://colah.github.io/posts/2015-08-Understanding-LSTMs/
   • https://weberna.github.io/blog/2017/11/15/LSTM-Vanishing-Gradients.html
   • https://data-science-blog.com/blog/2020/09/07/back-propagation-of-lstm/
   • A type of RNN capable of learning long-term dependencies.
5. Transformer Networks
   • A deep learning architecture primarily used in NLP tasks (e.g., BERT, GPT).
6. Generative Adversarial Networks (GANs)
   • https://www.analyticsvidhya.com/blog/2021/10/an-end-to-end-introduction-to-generative-adversarial-networksgans/
   • A framework involving two neural networks to generate new data.

Recommendation Systems:

1. https://towardsdatascience.com/recommender-systems-a-complete-guide-to-machine-learning-models-96d3f94ea748
2. Collaborative Filtering:
   • https://medium.com/@deepapandithu/recommender-system-user-collaborative-filtering-37613f0c6a9
3. Content-Based Filtering:
   • https://medium.com/@zbeyza/recommendation-systems-content-based-filtering-e19e3b0a309e

Ensemble Learning Algorithms:

1. Bagging

   • Combines the predictions of several base models (e.g., Random Forest).

2. Boosting

   • Sequentially builds models that correct the errors of previous models (e.g., XGBoost, AdaBoost).

3. Stacking

   • Combines multiple models by training a meta-model on their predictions.