Karteikarten: Introduction to Data Science Fundamentals — 16 Karten

Data Science — definition?

Interdisciplinary field extracting knowledge from data.

Data collection methods?

Surveys, web scraping, sensors, handling missing data, removing duplicates, transformation.

Data cleaning — purpose?

Ensure data quality for accurate analysis.

Exploratory Data Analysis — role?

Understand data patterns, relationships, and outliers.

Techniques of EDA?

Summary stats, visualization, correlation analysis.

Statistical inference — purpose?

Draw conclusions about populations from samples.

Hypothesis testing — role?

Evaluate assumptions using sample data.

Model validation — methods?

Cross-validation, train/test split.

Performance metrics?

Accuracy, precision, recall, F1 score.

Overfitting — meaning?

Model captures noise, poor generalization.

Data visualization — purpose?

Communicate data insights visually.

Common visualization tools?

Tableau, Matplotlib, Seaborn.

Big Data — definition?

Handling large datasets beyond traditional tools.

Hadoop — function?

Distributed storage and processing using HDFS and MapReduce.

Spark — advantage?

Fast, in-memory distributed data processing.

Supervised learning — example?

Linear regression, decision trees.