V3_L2:Data understanding

Question 1

Automation

Answer 1

Using tools and techniques to streamline data collection and preparation processes.

Question 2

Data Collection

Answer 2

The phase of gathering and assembling data from various sources.

Question 3

Data Compilation

Answer 3

The process of organizing and structuring data to create a comprehensive data set.

Question 4

Data Formatting

Answer 4

The process of standardizing the data to ensure uniformity and ease of analysis.

Question 5

Data Manipulation

Answer 5

The process of transforming data into a usable format.

Question 6

Data Preparation

Answer 6

The phase where data is cleaned, transformed, and formatted for further analysis, including feature engineering and text analysis.

Question 7

Data Preparation

Answer 7

The stage where data is transformed and organized to facilitate effective analysis and modeling.

Question 8

Data Quality

Answer 8

Assessment of data integrity and completeness, addressing missing, invalid, or misleading values.

Question 9

Data Quality Assessment

Answer 9

The evaluation of data integrity, accuracy, and completeness.

Question 10

Data Set

Answer 10

A collection of data used for analysis and modeling.

Question 11

Data Understanding

Answer 11

The stage in the data science methodology focused on exploring and analyzing the collected data to ensure that the data is representative of the problem to be solved.

Question 12

Descriptive Statistics

Answer 12

Summary statistics that data scientists use to describe and understand the distribution of variables, such as mean, median, minimum, maximum, and standard deviation.

Question 13

Feature

Answer 13

A characteristic or attribute within the data that helps in solving the problem.

Question 14

Feature Engineering

Answer 14

The process of creating new features or variables based on domain knowledge to improve machine learning algorithms’ performance.

Question 15

Feature Extraction

Answer 15

Identifying and selecting relevant features or attributes from the data set.

Question 16

Interactive Processes

Answer 16

Iterative and continuous refinement of the methodology based on insights and feedback from data analysis.

Question 17

Missing Values

Answer 17

Values that are absent or unknown in the dataset, requiring careful handling during data preparation.

Question 18

Model Calibration

Answer 18

Adjusting model parameters to improve accuracy and alignment with the initial design.

Question 19

Pairwise Correlations

Answer 19

An analysis to determine the relationships and correlations between different variables.

Question 20

Text Analysis

Answer 20

Steps to analyze and manipulate textual data, extracting meaningful information and patterns.

Question 21

Text Analysis Groupings

Answer 21

Creating meaningful groupings and categories from textual data for analysis.

Question 22

Visualization techniques

Answer 22

Methods and tools that data scientists use to create visual representations or graphics that enhance the accessibility and understanding of data patterns, relationships, and insights.