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The Data Science Course: Complete Data Science Bootcamp 2025 > Intro to Data Science Flashcards > Flashcards

Intro to Data Science Flashcards

Learn Data Science Into Terms

1
Q

Business Intelligence (BI)

A

Tools and techniques for analyzing and understanding past data to make strategic decisions

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2
Q

Historical Data

A

Collected past data used for analysis

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3
Q

Dashboard

A

A user interface that visually summarizes the key data and metrics

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4
Q

Strategic Deciscions

A

Long-term planning choices

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5
Q

Tactical Decisions

A

Short term, specific actions

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6
Q

Artificial Intelligence (AI)

A

Enabling machines to perform tasks that typically require human intelligence

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7
Q

Machine Learning (ML)

A

A branch of artificial intelligence where computers learn from data to improve their performance on tasks

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8
Q

Data Analytics

A

The process of examining datasets to draw conclusions and find patterns using statistical techniques

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9
Q

Real-time Dashboards

A

Interactive tools that display data and metrics as they are updated in real-time

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10
Q

Third-party Data

A

Data collected by an external entity; Not your own company’s data

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11
Q

Predictive Analytics

A

The process of using data and statistical algorithms to predict future values or trends based on historical data

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12
Q

Algorithm

A

A set of rules or instructions designed to solve problems or perform tasks, often used in computing

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13
Q

Data Pattern

A

A recurring or recognizable element in a dataset, often indicating a trend or relationship

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14
Q

Client Retention

A

Business aiming to understand and predict customer purchasing behaviors to sell more products to existing clients

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15
Q

Client Acquistion

A

The process of gaining new clients or customers for a business, often through marketing and sales strategies

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16
Q

Fraud Prevention

A

Methods and systems used to detect and prevent fraudulent activities, such as unauthorized transactions

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17
Q

Speech Recognition

A

Technology that recognizes and interprets human speech, converting it into text or commands

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18
Q

Image Recognition

A

A computer technology that identifies objects, places, people, and other elements in digital images

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19
Q

Symbolic Reasoning

A

The process in artificial intelligence where symbols represent concepts or entities to make logical deductions

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20
Q

Advanced Analytics

A

Sophisticated data analysis techniques, often involving predictive models, machine learning, and big data

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21
Q

Data Collection

A

Gathering information systematically from various sources to analyze and make informed deciscions

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22
Q

Data Analysis

A

The process of inspecting, cleaning, and modeling data with the goal of discovering useful information

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23
Q

Forecasting

A

The use of historical data to predict future events or trends, often used in business, finance, and weather
predictions

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24
Q

Dataset

A

A collection of related sets of information, usually formatted in a table, used for analysis or processing

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25
Q

Analytical Tools

A

Software and applications used to analyze, visualize, and interpret data

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26
Q

Big Data

A

Extremely large data characterized by volume, variety, and velocity. Often requires cloud storage and processing

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27
Q

Real-time Data Processing

A

The continuous and immediate processing of data as it’s collected or generated

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28
Q

Data Pre-processing

A

The initial steps in data analysis involving cleaning and organizing data for further use

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29
Q

Text Data Mining

A

Extracting useful information and insights from textual data using analytical methods

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30
Q

Data masking

A

The practice of hiding original data with modified content (e.g., characters or other other data) to protect sensitive information

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31
Q

Price Optimization

A

A technique to conceal sensitive information in a dataset by replacing it with fictitious but realistic data, ensuring privacy and security while allowing functional analysis and testing

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32
Q

Inventory Management

A

The practice of overseeing and controlling the ordering, storage, and use of a company’s inventory

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33
Q

Seasonality Patterns

A

Trends or recurring changes in data observed at regular intervals throughout a year, often influenced by seasons

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34
Q

Shipment Logistics

A

The coordination of transporting goods from one place to another, including planning, execution, and tracking

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35
Q

Metrics

A

Quantitative measurers used to track and asses the status of specific processes

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36
Q

KPIs

A

Specific metrics used to evaluate the success of an organization or activity in meetings its objectives

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37
Q

Customer Retention

A

Strategies and activities aimed at keeping customers engaged and continuing to purchase form a business

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38
Q

Business Goal Alignment

A

The process of ensuring that business activities and strategies are focused on achieving the company’s primary objectives

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39
Q

Data Architect

A

A professional responsible for designing and managing an organization’s data architecture to meet business needs

40
Q

Data Engineer

A

A role focused on preparing ‘big data’ for analytical or operational uses, often involving building and maintaining data systems

41
Q

Database Administrator

A

A specialist responsible for managing and maintaining database systems, ensuring their optimal performance and security

42
Q

BI Analyst

A

A professional who analyzes data to provide insights and recommendations for improving business decisions and strategies

43
Q

BI Consultant

A

An expert who advises businesses on how to use data analytics and BI tools to improve decision - making and performance

44
Q

BI Developer

A

A professional who designs, develops, and maintains BI solutions, including data visualization and reporting tools

45
Q

Data Scientist

A

A specialist in extracting insights and knowledge from complex data using various statistical, machine learning, and analytical techniques

46
Q

Data Analyst

A

A professional who collects, processes, and performs statistical analyses on data to help make informed decisions

47
Q

Machine Learning Engineer

A

An engineer specialized in designing and building machine learning models and systems

48
Q

Business Analytics

A

The practice of using data analysis to inform and guide business decisions

49
Q

Data Storytelling

A

The skill of communicating insights from data analyses through compelling narratives and visualizations

50
Q

R

A

A programming language and environment widely used for statistical computing and graphics

51
Q

Python

A

A versatile programming language popular in many fields, including data science, for its readability and vast libraries

52
Q

Digital Signal Processing

A

The analysis and manipulation of digital sounds, often for improving accuracy and reliability of digital communication

53
Q

Supervised Learning

A

A type of machine learning where models are trained on labeled data to predict outcomes or classify data

54
Q

Fraud Detection

A

Banks using machine learning to detect fraudulent credit card transactions

55
Q

Predictive Modeling

A

Creating, testing, and validating a model to best predict the probability of an outcome…

56
Q

Data

A

Information, often in the form of facts or statistics, collected for reference or analysis

57
Q

Model

A

In data science, a representation or abstraction of a real-world process, used for analysis and predictions

58
Q

Objective Function

A

A mathematical formula used in optimization to define the goal of a model or algorithm, often representing the cost, loss, or error which the model seeks to minimize or maximize during training

59
Q

Optimization Algorithm

A

A method or procedure used to make a system or design as effective or functional as possible

60
Q

Trial-and-Error Process

A

A problem-solving method involving repeated, varied attempts until success is achieved

61
Q

Model Training

A

The process of feeding data into a machine learning algorithm to help it learn and adapt, improving its ability to make predictions or decisions based on that data

62
Q

Generalization

A

The ability of a model to perform well on new, unseen data after being trained on a dataset

63
Q

Unsupervised Learning

A

A type of machine learning that finds patterns in data without pre-existing labels

64
Q

Reinforcement Learning

A

A type of machine learning when an agent learns to behave in an environment by performing actions and receiving rewards

65
Q

Support Vector Machines

A

A supervised machine learning model used for classification and regression analysis, effective in high-dimensional spaces

66
Q

Neural Networks

A

Computational models inspired by the human brain, used in machine learning to recognize patterns and make decisions

67
Q

Deep Learning

A

A subset of machine learning involving neural networks with many layers, enabling advanced pattern recognition

68
Q

Random Forest Models

A

A machine learning method involving many decision trees to improve predictive accuracy and prevent overfitting

69
Q

Bayesian Networks

A

A type of probabilistic model that uses Bayesian inference for probability computations

70
Q

K-means

A

A clustering algorithm in machine learning that divides a set of data points into k groups based on feature similarity

71
Q

SQL

A

A programming language used to manage and manipulate relational databases

72
Q

MATLAB

A

A high-level language and interactive environment used for numerical computation, visualization, and programming

73
Q

Excel

A

Microsoft’s spreadsheet software for data organization, analysis, and visual representation using formulas and tools

74
Q

SPSS

A

A software package used for statistical analysis, particularly in social sciences

75
Q

Hadoop

A

An open-source framework for storing data and running applications on clusters of commodity hardware

76
Q

Numerical Data

A

Data that is quantifiable and measurable, like numbers, which can be used in mathematical calculations

77
Q

Categorical Data

A

Data that represents characteristics or descriptors, often grouped into categories or labels. For example data on choices of ice cream flavors like vanilla, chocolate, and stawberrry

78
Q

Raw Data

A

Data in its original form, unprocessed and unfiltered. Example: Sensor readings directly recorded

79
Q

Class Labelling

A

Assigning predefined categories to data points. Example: Tagging emails as ‘spam’ or ‘not spam’

80
Q

Handling Missing Values

A

Techniques. to deal with absent data points. Example: Filling missing values with the average of existing data

81
Q

Balacing

A

Adjusting datasets to have an equal number of instances in each category. Example: Ensuring equal cases of positive and negative outcomes in medical data

82
Q

Data Shuffling

A

Randomly rearranging data points to prevent order bias. Example: Shuffling customer data before analysis

83
Q

Entity-Relationship Diagram

A

A graphical representation of entities and their relationships

84
Q

Relational Schema

A

A blueprint of a database, structure, showing tables and relationships

85
Q

Cluster Analysis

A

Grouping data points based on similarities. Example: Segmenting customers into groups based on buying habits

86
Q

Time Series Analysis

A

Analyzing data points collected over time. Example: Examining stock prices over several months

87
Q

Regression Analysis

A

Evaluating relationships between variables. Example: Predicting house prices based on size and location

88
Q

Factor Analysis

A

Identifying underlying variables that explain observed patterns. Example: Analyzing survey responses to uncover hidden attitudes

89
Q

Data Balancing

A

The process of ensuring a dataset has an evenly distributed class representation. Example: Balancing the number of fraud and non-fraud cases in a financial dataset

90
Q

Traditional Data

A

Tabular data containing numeric or text values, manageable from a single computer

91
Q

Data Volume

A

The size of data, measured in megabytes, gigabytes, terabytes, petabytes, or exabytes

92
Q

Data Variety

A

Diversity in data types, including structured, semi-structured, and unstructured formats like images, audio, and mobile data

93
Q

Data Velocity

A

The rapid rate of data generation and processing, aiming for real-time outputs

94
Q

Traditional Methods

A

Classical statistical methods adapted for business applications. Not including advanced statistical analyses

95
Q
A

The Data Science Course: Complete Data Science Bootcamp 2025 (1 decks)

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