🚀 Introduction
This structured knowledge graph maps out the key concepts in Data Science, breaking them into topics, subtopics, and granular details.
A teacher can use a map like this as the skeleton of a unit: pick the few topics a class actually needs, say descriptive statistics before hypothesis testing, and assign those rather than a whole course. In DIY Degree, assigning topics to a class that joined by code is built and waiting for its first classroom pilot, as is the view of who is not started, in progress, mastered or stuck. When the open commons defines a data science path from this graph (planned), it will be one a student can follow and a teacher can point to. What a teacher can do today.
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.