Core Skills Every Data Scientist Uses

Statistics & Probability – hypothesis testing, regression, Bayesian thinking

Machine Learning – supervised, unsupervised, and ensemble methods

Data Wrangling – cleaning, feature engineering, ETL

Programming – Python (pandas, NumPy, sklearn), SQL

Visualization – storytelling with plots (Matplotlib, Seaborn, Plotly)

Experimentation – A/B testing, causal inference

🔹 Essential Techniques & Algorithms

Regression models (linear, logistic, regularized)

Tree-based methods (Random Forest, XGBoost, LightGBM)

Clustering (k-means, DBSCAN, hierarchical)

Dimensionality reduction (PCA, t-SNE, UMAP)

Time-series forecasting (ARIMA, Prophet, LSTM)

Deep learning (CNNs, RNNs, Transformers)

🔹 Key Tools & Ecosystem

Python stack: pandas, scikit-learn, PyTorch, TensorFlow

Data platforms: Spark, Databricks, BigQuery, Snowflake

MLOps: MLflow, Airflow, Kubeflow, Docker

Visualization: Tableau, Power BI

🔹 Current Trends (2024–2025)

Generative AI & LLM-powered analytics

Agentic AI-driven workflows (auto-analysis, automated feature engineering)

RAG & hybrid search systems

Vector databases (Pinecone, Weaviate, Milvus)

Real-time ML (online learning, streaming pipelines)

Responsible/Explainable AI (SHAP, fairness metrics)

Synthetic data for model training

🔹 What “Good” Data Science Looks Like

Clear problem framing

High-quality data and reproducible pipelines

Baseline models before complexity

Transparent evaluation and monitoring

Communicating insights that drive decisions