UNIT-1-Lesson2_The-Analytics-Process

What is the first step in the Analytics Process?

Problem Definition.

What is the purpose of Problem Definition in the Analytics Process?

Clearly define the problem or question you aim to solve.

What are some key questions to ask during the Problem Definition phase?

What problem are you solving? What decisions will the insights inform?

What is the purpose of Data Collection in the Analytics Process?

Gather data relevant to the defined objectives.

What are key questions regarding data to consider during Data Collection?

What data is needed? Where can the data be sourced? Is the data reliable?

What is the goal of Data Preparation/Cleaning?

Clean and organize data for analysis by removing incorrect or irrelevant data.

Which key questions should be addressed during Data Preparation/Cleaning?

What data needs to be cleaned? How will missing data be handled?

What is the purpose of Data Analysis?

Analyze data to uncover patterns, trends, and insights.

What key activities are involved in Data Analysis?

Exploration, Visualization, and Modeling.

What is the significance of Interpretation of Results?

Translate analysis into actionable insights and relate findings to the original problem.

What key questions should be answered during the Interpretation of Results?

What do the results mean? Are they consistent with expectations?

What is the final step in the Analytics Process?

Implementation and Iteration.

What key areas are focused on during Implementation and Iteration?

Apply findings, monitor the process, and gather feedback.

What methodology is based on the Life Cycle of Analytics?

CRISP-DM methodology.

What does CRISP-DM stand for?

Cross-Industry Standard Process for Data Mining.

What is emphasized in the Data Analytics Life Cycle?

Data cleaning, transformation, and enhancement.