FOUNDATION IN BA QUIZ 1 PRELIM

Data Preprocessing

1. ______ is the process of transforming raw data into a clean, organized, and usable format before feeding it into a data analysis or machine learning model.

quality

1. The main goal of data preprocessing is to improve the ______ of the data, making it more suitable for analysis and model training.

missing

1. Raw data collected from real-world sources (like surveys, databases, sensors, or social media) often has ______ values.

formats

1. Raw data collected from real-world sources often has incorrect or inconsistent ______.

duplicate

1. Raw data collected from real-world sources often has ______ records.

features

1. Raw data collected from real-world sources often has irrelevant ______.

outliers

1. Raw data collected from real-world sources often has ______ or noise.

Reduction

1. Key aspects and common techniques involved in data preprocessing include Data Cleaning, Data Transformation, Data Integration, and Data ______.

imputing

1. Addressing incomplete data entries by either removing rows/columns with missing values or ______ (filling in) them using various strategies is part of handling missing values.

reduntant

1. Removing Duplicates involves identifying and eliminating ______ records to ensure each entry is unique and relevant.

consistency

1. Correcting Inconsistent Formats involves standardizing data formats (e.g., date formats, string cases, units of measurement) to maintain ______.

extreme

1. Handling Outliers involves identifying and addressing ______ values that can significantly skew results or model training.

suitable

1. Data Transformation involves converting data into a format that is more ______ for analysis or modeling.

numerical

1. Normalization and Scaling involves adjusting ______ features to a standard range or distribution.

Standarization

1. Common techniques for adjusting numerical features include Min-Max Scaling and ______ (scaling to zero mean and unit variance).

[0,1]

1. Min-Max Scaling involves normalizing numerical features to a ______ range.

text-based

1. Encoding Categorical Variables involves converting non-numerical (______) categorical data into a numerical representation that machine learning algorithms can understand.

One-Hot Encoding

1. ______ involves creating new binary columns for each category.

unique integer

1. Label Encoding involves assigning a ______ to each category.

Feature Engineering

1. ______ is the process of creating new features from existing ones to better capture underlying patterns and improve model performance.

summary

1. Data Aggregation involves combining multiple data points into ______ forms (e.g., averages, sums) to simplify analysis or reduce dimensionality.

Schema Matching

1. ______ is the process of aligning fields and data structures from different sources during data integration.

duplicate entries

1. Data Deduplication involves identifying and removing ______ across integrated datasets.

features

1. Dimensionality Reduction involves reducing the number of ______ in a dataset.

essential

1. Data Reduction is the process of reducing the volume of data while retaining ______ information to improve efficiency and reduce computational costs.

Principal Component Analysis

1. Techniques like ______ (PCA) are used to create new, lower-dimensional representations of the data.

overfitting

1. Feature Selection involves choosing a subset of the most relevant features to improve model performance and reduce ______.

graphical representation

1. Data visualization is the ______ of information and data.

visual forms

1. It's the art and science of translating data into ______ like charts, graphs, maps, and dashboards.

discrete categories

1. Bar Charts show comparisons between ______ or changes over a period.

proportional

1. Bar charts feature rectangular bars with lengths ______ to the values they represent.

progression and movement

1. Line Charts show trends or changes over a continuous period and are excellent for illustrating ______.

connected by lines

1. Line charts feature data points ______, representing continuous data.

whole

1. Pie Charts / Donut Charts show proportions or parts of a ______.

hole

1. Donut charts are similar to pie charts but have a ______ in the center.

small differences

1. While popular, pie charts can be less effective for comparing many categories or showing ______ between slices.

correlation

1. Scatter Plots show the relationship or ______ between two numerical variables.

two-dimensional graph

1. Scatter plots feature individual data points plotted on a ______, with one variable on the x-axis and the other on the y-axis.

single numerical variable

1. Histograms show the distribution of a ______.

no gaps

1. Histograms feature bars representing the frequency of data points falling into a continuous range, with ______ between bars (unless a bin is empty).

five-number summary

1. Box Plots provide a summary of its ______: minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum.

interquartile range (IQR)

1. In box plots, a box represents the ______.

color

Heat Maps show the magnitude of a phenomenon as a ______ in two dimensions

main characteristics

1. Exploratory Data Analysis (EDA) is a crucial step in the data analysis process that involves examining and visualizing data to understand its ______, uncover patterns, identify anomalies, and formulate hypotheses.

underlying structure

1. The primary goal of EDA is to gain insights into the ______ of the data.

skewness and kurtosis

1. Under EDA key aspects, data summarization includes understanding the distribution of data through measures like ______.

square root transformations

1. Data transformation in EDA involves applying transformations to variables, such as logarithmic or ______, to achieve a more normal distribution.

correlation analysis

1. Understanding relationships in EDA involves exploring relationships between variables through ______.

iterative process

1. EDA is often an ______, where insights gained lead to further exploration or refinement of analysis techniques.

dynamic exploration

Utilizing interactive tools and dashboards for ___ of data.