Flat file storage

Relational database table

XML document

JSON object

Artificial Intelligence

Advanced Integration

Artificial Interaction

Automated Insight

A program that analyzes existing texts.

A tool that summarizes articles.

A system that categorizes images.

A model that creates original artwork.

To enable computers to learn from data and make predictions or decisions.

To replace human intelligence with automated systems.

To create complex algorithms that require no data input.

To program computers to follow strict rules without learning.

Semi-supervised learning

Reinforcement learning

Supervised learning

Unsupervised learning

Unstructured data is data that is easily searchable and categorized.

Unstructured data is information that follows a strict set of rules and guidelines.

Unstructured data is highly organized information with a clear format.

Unstructured data is information that lacks a predefined format or structure.

Data sanitization

Data purification

Data scrubbing

Data cleaning

K-Means Clustering

Support Vector Machines

Linear Regression

Decision Trees

Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data to find patterns.

Supervised learning is faster than unsupervised learning in processing data.

Unsupervised learning uses feedback from users, while supervised learning does not.

Supervised learning finds patterns in data, while unsupervised learning requires labeled data.

Data collected once a year

Unstructured data without a time component

Temporal data collected at regular intervals

Random data from various sources

Google Sheets

Power BI

Excel Charts

Tableau

Speed, Size, and Structure

Volume, Velocity, and Variety

Quality, Quantity, and Quirkiness

Density, Depth, and Direction

A feature is a type of algorithm used in machine learning.

A feature is a collection of data points in a dataset.

A feature is an individual measurable property or characteristic used as input in machine learning models.

A feature is a method for evaluating model performance.

The purpose of a training dataset is to generate random predictions.

The purpose of a training dataset is to store raw data for analysis.

The purpose of a training dataset is to train a machine learning model.

The purpose of a training dataset is to validate a model's performance.

Python

Java

Ruby

C++

The role of a data analyst is to create marketing strategies for products.

The role of a data analyst is to manage company finances and budgets.

The role of a data analyst is to design software applications for users.

The role of a data analyst is to analyze data and provide insights to inform business decisions.

Overfitting refers to a model that generalizes well to new data but struggles with training data.

Overfitting is when a model performs equally well on both training and unseen data.

Overfitting is when a model performs well on training data but poorly on unseen data due to excessive complexity.

Overfitting occurs when a model is too simple and fails to capture the underlying patterns.

Reinforcement learning

Unsupervised learning

Supervised learning

Semi-supervised learning

Data preprocessing improves data quality and ensures accurate analysis.

Data preprocessing complicates the analysis process.

Data preprocessing is only necessary for large datasets.

Data preprocessing eliminates the need for data analysis.