LangChain for Data Scientists in 2026: LLMs, Agents and Interview Questions
Master LangChain 0.3 for data science: LCEL chains, RAG patterns, ReAct agents, and memory systems. Includes interview questions and production deployment strategies.
Data Science & ML
DATA
Comprehensive Data Science and Machine Learning curriculum with Python as the main language. From data manipulation with Pandas and NumPy to implementing Deep Learning models with TensorFlow/Keras, through classic ML with Scikit-Learn. Also includes MLOps skills to deploy and maintain models in production with Docker, FastAPI and cloud platforms.
What you'll learn
Modern Python with object-oriented programming and best practices
Data manipulation with Pandas, NumPy and SQL (BigQuery)
Visualization with Matplotlib, Seaborn and Plotly
Descriptive and inferential statistics with Statsmodel
Machine Learning with Scikit-Learn and XGBoost (regression, classification, clustering)
Deep Learning with TensorFlow and Keras (CNN, RNN, Transformers)
NLP and GenAI with Hugging Face, LangChain and LLMs (GPT, Gemini)
MLOps with MLflow, Docker, FastAPI and Streamlit
Development environments: Jupyter, Google Colab
Cloud deployment with Google Compute, Cloud Storage and GPU
Key topics to master
The most important concepts to understand this technology and ace your interviews
1
Python: types, data structures, OOP, decorators, generators, context managers
2
NumPy: arrays, broadcasting, indexing, vectorized operations, linear algebra
3
Pandas: DataFrames, Series, indexing, groupby, merge, pivot, time series
4
SQL: SELECT, JOIN, GROUP BY, window functions, CTEs, query optimization
5
Visualization: Matplotlib (figures, axes, subplots), Seaborn (statistical plots), Plotly (interactive)
6
Statistics: distributions, hypothesis testing, confidence intervals, regression
7
Feature Engineering: encoding, scaling, feature selection, feature creation
8
Supervised ML: linear/logistic regression, trees, Random Forest, XGBoost, metrics
9
Unsupervised ML: K-Means, hierarchical clustering, PCA, t-SNE
10
ML Pipeline: train/test split, cross-validation, hyperparameter tuning, overfitting
11
Deep Learning: perceptrons, backpropagation, activation functions, optimizers, loss functions
12
CNN: convolutions, pooling, architectures (ResNet, VGG), transfer learning
13
RNN/LSTM: sequences, vanishing gradient, attention mechanism, Transformers
14
NLP: tokenization, embeddings, word2vec, BERT, LLM fine-tuning
15
MLOps: versioning (MLflow), containerization (Docker), API (FastAPI), monitoring
16
Cloud: Google Cloud (Compute, Storage, BigQuery), GPU training, Vertex AI
17
AI Ethics: bias, explainability (SHAP, LIME), fairness, GDPR
Recent Data Science & ML articles
Discover our latest articles and guides on Data Science & ML
MLOps in 2026: MLflow, Model Registry and Technical Interview Questions
MLOps interview questions covering the ML lifecycle, MLflow experiment tracking, model registry promotion, deployment patterns, drift monitoring, and system design for 2026, with Python code and answers.
RAG and LLMs in 2026: Retrieval-Augmented Generation for Data Science Interviews
Retrieval-Augmented Generation (RAG) explained for data science interviews in 2026. Covers vector databases, chunking strategies, embedding models, agentic RAG, Graph RAG, and production-ready pipeline architecture.