Data Science Fundamentals

Data Science Fundamentals

• Data Science is primarily used for extracting knowledge and insights from data.
• Python is the most commonly used programming language in data science.

Big Data and Data Visualization

• Big Data is characterized by Volume, Variety, and Velocity.
• Tableau is a popular tool for data visualization.

Machine Learning

• Clustering is a method used for finding groups in data.
• Overfitting occurs when a model performs well on training data but poorly on unseen data.
• Supervised, Unsupervised, and Semi-supervised learning are types of machine learning, while Over-learning is not a type of machine learning.
• Neural Networks refer to algorithms modeled after the human brain.
• A decision tree is a type of Predictive model.

Python Libraries

• NumPy is a commonly used library for scientific computing in Python.
• The pandas library is used for data manipulation and analysis.

Data Analysis and Visualization

• A scatter plot is used to show correlations between two variables.
• A/B testing is used to compare different models or approaches.

SQL and Data Management

• The SQL command SELECT is used to retrieve data from a database.

Machine Learning Concepts

• A feature in a machine learning context refers to an attribute used as input for a model.
• The 'train-test split' is used to prevent overfitting by using separate data for training and testing a model.
• Principal component analysis (PCA) is used for data reduction.

Machine Learning Tasks

• Regression is an example of a supervised learning task.
• Data wrangling refers to the process of cleaning and unifying messy and complex data sets for easy access and analysis.