Intro to Analytics Chapter 1.1.2

Descriptive Analytics

Focuses on summarizing and describing historical data to provide insights into past trends and patterns.

Diagnostic Analytics

Analyzes past data to identify the root causes of specific outcomes or events, answering the question 'Why did it happen?'

Predictive Analytics

Uses historical data to forecast future outcomes, addressing questions like 'What is likely to happen in the future?'

Prescriptive Analytics

Recommends actions to optimize or improve a situation, answering questions such as 'What should we do?'

Exploratory Analytics

Involves exploring and analyzing data to identify potential trends and insights when there is no clear objective.

Data Collection

The process of gathering large datasets from various sources, including databases and surveys.

Data Cleaning

The process of correcting data errors, removing duplicates, and ensuring dataset accuracy for analysis.

Data Reporting

Creating reports and visualizations that communicate findings to team members and stakeholders clearly.

Predictive Analysis

Utilizes algorithms to predict future trends and outcomes based on historical data.

Data-Driven Decision-Making

Making decisions based on the analysis of data to identify opportunities for improvement.

Continuous Improvement in Data Analytics

Involves professional development and adapting to new data sources, tools, and techniques.

Data Pipeline Development

Designing and developing processes that move data from source systems to data storage systems.

Data Storage Management

Managing data in databases, data lakes, and warehouses, including tasks like data partitioning and indexing.

Data Quality Control

Ensuring that data is accurate, consistent, and free of errors.

Machine Learning Model Training

The process of training machine learning models on data using various algorithms.

Data Integration

Combining data from various sources to create a unified view for analysis.

A/B Testing

A statistical method used to compare two versions of a product or service to determine which performs better.

Data Storytelling

The practice of presenting complex technical concepts and insights in a clear and understandable way.

Collaboration in Data Science

Working with engineers, developers, and stakeholders to design solutions that align with business goals.

Cloud Computing in Data Engineering

Using cloud platforms to deploy and manage data infrastructure.