🚀 Introduction
This structured knowledge graph maps out the key concepts in Data Science, breaking them into topics, subtopics, and granular details.
Programming for Data Science
Python
- Syntax & Basics: Understanding Python syntax, variables, and control flow.
- Data Structures: Lists, Dictionaries, Tuples, and their applications.
- Object-Oriented Programming (OOP): Classes, inheritance, and encapsulation in Python.
- File Handling & Automation: Reading/writing files, automating tasks using scripts.
Mathematics & Statistics
Probability & Statistics
- Descriptive Statistics: Mean, median, mode, variance, and standard deviation.
- Inferential Statistics: Confidence intervals, p-values, and hypothesis testing.
- Bayesian vs. Frequentist Methods: Comparing probability interpretation approaches.
- Probability Distributions: Normal, Poisson, and Binomial distributions.
Machine Learning
Supervised Learning
- Linear Regression: Predicting continuous values with regression models.
- Logistic Regression: Binary classification and sigmoid function.
- Decision Trees & Random Forests: Ensemble learning and feature importance.
- Gradient Boosting: XGBoost, LightGBM, and CatBoost for model improvements.
Big Data & Data Engineering
Cloud Data Engineering
- Google BigQuery: Serverless data warehouse for big data analytics.
- AWS Redshift: Scalable cloud-based data warehousing.
- Data Pipelines & ETL Processing: Building automated data transformation workflows.
- Streaming Data with Kafka: Handling real-time data ingestion and processing.