intro to data science final

Data Literacy

Understanding and interpreting data effectively.

Causality

Establishing a cause-and-effect relationship.

Association

Identifying correlations between variables.

Observational Studies

Research analyzing existing data without manipulation.

Chocolate and Health

Example of association in health studies.

Death Penalty and Murder Rates

Example of potential causal analysis.

Public Data

Freely available datasets for experimentation.

Existing Product Data

User interaction data from current products.

Human-in-the-Loop Systems

Combining automation with human oversight.

Brute Force Collection

Costly data gathering methods for unique datasets.

Purchased Data

Acquired datasets from third-party vendors.

Filtering Impurities

Managing errors in raw data for quality.

Merging Diverse Data Sources

Integrating datasets from different origins.

Data Labeling

Annotating data for machine learning context.

External Services

Platforms for scalable data annotation tasks.

Internal Teams

In-house capabilities for data annotation.

User-Generated Labels

User contributions to data labeling processes.

Annotation Acceleration Tools

Technologies enhancing data annotation efficiency.

Data Science

Field combining statistics

Collaboration in Data Science

Teamwork essential for solving complex data problems.

Skills of Data Scientists

Mix of statistics

5 C's of Data Ethics

Consent

IBM Data Estimate

2.5 quintillion bytes of data generated daily.

Prediction in Data Science

Forecasting events based on data analysis.

Productivity Paradox

Technological shifts may delay visible economic benefits.

Big Data

Large datasets requiring responsible usage for impact.

Human Limitations

Memory and objectivity constraints in data interpretation.

Python Basics

Fundamental functions for data manipulation in Python.

NumPy

Library for numerical operations in Python.

Pandas

Data manipulation library for structured data.

PyPlot

Matplotlib module for creating visualizations.

Seaborn

Statistical data visualization library based on Matplotlib.

Linear Regression

Predicting continuous variables using linear relationships.