What techniques are used in Data Science?
Core Data Science Techniques
Data Science is the art of extracting actionable insights from raw data using a combination of scientific methods, algorithms, and systems.
1 Data Preprocessing & Wrangling
Feature Engineering
FoundationThe process of using domain knowledge to create variables that help machine learning algorithms work better. Techniques include One-Hot Encoding, Scaling, and Binning.
Dimensionality Reduction
EfficiencyReducing the number of random variables under consideration by obtaining a set of principal variables. Common methods: PCA (Principal Component Analysis) and t-SNE.
2 Statistical & Machine Learning
Regression
Predicting continuous values (e.g., house prices).
- • Linear Regression
- • Polynomial Regression
- • Ridge & Lasso
Classification
Predicting discrete categories (e.g., Spam vs. Not Spam).
- • Random Forest
- • Support Vector Machines (SVM)
- • XGBoost / LightGBM
Clustering
Grouping similar data points without labels.
- • K-Means Clustering
- • Hierarchical Clustering
- • DBSCAN
3 Deep Learning & AI Integration
Natural Language Processing (NLP)
Techniques used to help computers understand and interpret human language. In 2026, this focuses heavily on:
- Transformer Architectures (Attention Mechanism)
- Retrieval-Augmented Generation (RAG)
- Sentiment Analysis & Named Entity Recognition
Time Series Analysis
Techniques for analyzing data points collected or recorded at specific time intervals.
Master These Techniques
Learning the techniques is step one. Applying them to multi-million dollar business problems is where you become a Senior Data Scientist.
