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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
Analytical Tools
Software and applications used to analyze, visualize, and interpret data
26
Big Data
Extremely large data characterized by volume, variety, and velocity. Often requires cloud storage and processing
27
Real-time Data Processing
The continuous and immediate processing of data as it's collected or generated
28
Data Pre-processing
The initial steps in data analysis involving cleaning and organizing data for further use
29
Text Data Mining
Extracting useful information and insights from textual data using analytical methods
30
Data masking
The practice of hiding original data with modified content (e.g., characters or other other data) to protect sensitive information
31
Price Optimization
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
32
Inventory Management
The practice of overseeing and controlling the ordering, storage, and use of a company's inventory
33
Seasonality Patterns
Trends or recurring changes in data observed at regular intervals throughout a year, often influenced by seasons
34
Shipment Logistics
The coordination of transporting goods from one place to another, including planning, execution, and tracking
35
Metrics
Quantitative measurers used to track and asses the status of specific processes
36
KPIs
Specific metrics used to evaluate the success of an organization or activity in meetings its objectives
37
Customer Retention
Strategies and activities aimed at keeping customers engaged and continuing to purchase form a business
38
Business Goal Alignment
The process of ensuring that business activities and strategies are focused on achieving the company's primary objectives
39
Data Architect
A professional responsible for designing and managing an organization's data architecture to meet business needs
40
Data Engineer
A role focused on preparing 'big data' for analytical or operational uses, often involving building and maintaining data systems
41
Database Administrator
A specialist responsible for managing and maintaining database systems, ensuring their optimal performance and security
42
BI Analyst
A professional who analyzes data to provide insights and recommendations for improving business decisions and strategies
43
BI Consultant
An expert who advises businesses on how to use data analytics and BI tools to improve decision - making and performance
44
BI Developer
A professional who designs, develops, and maintains BI solutions, including data visualization and reporting tools
45
Data Scientist
A specialist in extracting insights and knowledge from complex data using various statistical, machine learning, and analytical techniques
46
Data Analyst
A professional who collects, processes, and performs statistical analyses on data to help make informed decisions
47
Machine Learning Engineer
An engineer specialized in designing and building machine learning models and systems
48
Business Analytics
The practice of using data analysis to inform and guide business decisions
49
Data Storytelling
The skill of communicating insights from data analyses through compelling narratives and visualizations
50
R
A programming language and environment widely used for statistical computing and graphics
51
Python
A versatile programming language popular in many fields, including data science, for its readability and vast libraries
52
Digital Signal Processing
The analysis and manipulation of digital sounds, often for improving accuracy and reliability of digital communication
53
Supervised Learning
A type of machine learning where models are trained on labeled data to predict outcomes or classify data
54
Fraud Detection
Banks using machine learning to detect fraudulent credit card transactions
55
Predictive Modeling
Creating, testing, and validating a model to best predict the probability of an outcome...
56
Data
Information, often in the form of facts or statistics, collected for reference or analysis
57
Model
In data science, a representation or abstraction of a real-world process, used for analysis and predictions
58
Objective Function
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
Optimization Algorithm
A method or procedure used to make a system or design as effective or functional as possible
60
Trial-and-Error Process
A problem-solving method involving repeated, varied attempts until success is achieved
61
Model Training
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
Generalization
The ability of a model to perform well on new, unseen data after being trained on a dataset
63
Unsupervised Learning
A type of machine learning that finds patterns in data without pre-existing labels
64
Reinforcement Learning
A type of machine learning when an agent learns to behave in an environment by performing actions and receiving rewards
65
Support Vector Machines
A supervised machine learning model used for classification and regression analysis, effective in high-dimensional spaces
66
Neural Networks
Computational models inspired by the human brain, used in machine learning to recognize patterns and make decisions
67
Deep Learning
A subset of machine learning involving neural networks with many layers, enabling advanced pattern recognition
68
Random Forest Models
A machine learning method involving many decision trees to improve predictive accuracy and prevent overfitting
69
Bayesian Networks
A type of probabilistic model that uses Bayesian inference for probability computations
70
K-means
A clustering algorithm in machine learning that divides a set of data points into k groups based on feature similarity
71
SQL
A programming language used to manage and manipulate relational databases
72
MATLAB
A high-level language and interactive environment used for numerical computation, visualization, and programming
73
Excel
Microsoft's spreadsheet software for data organization, analysis, and visual representation using formulas and tools
74
SPSS
A software package used for statistical analysis, particularly in social sciences
75
Hadoop
An open-source framework for storing data and running applications on clusters of commodity hardware
76
Numerical Data
Data that is quantifiable and measurable, like numbers, which can be used in mathematical calculations
77
Categorical Data
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
Raw Data
Data in its original form, unprocessed and unfiltered. Example: Sensor readings directly recorded
79
Class Labelling
Assigning predefined categories to data points. Example: Tagging emails as 'spam' or 'not spam'
80
Handling Missing Values
Techniques. to deal with absent data points. Example: Filling missing values with the average of existing data
81
Balacing
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
Data Shuffling
Randomly rearranging data points to prevent order bias. Example: Shuffling customer data before analysis
83
Entity-Relationship Diagram
A graphical representation of entities and their relationships
84
Relational Schema
A blueprint of a database, structure, showing tables and relationships
85
Cluster Analysis
Grouping data points based on similarities. Example: Segmenting customers into groups based on buying habits
86
Time Series Analysis
Analyzing data points collected over time. Example: Examining stock prices over several months
87
Regression Analysis
Evaluating relationships between variables. Example: Predicting house prices based on size and location
88
Factor Analysis
Identifying underlying variables that explain observed patterns. Example: Analyzing survey responses to uncover hidden attitudes
89
Data Balancing
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
Traditional Data
Tabular data containing numeric or text values, manageable from a single computer
91
Data Volume
The size of data, measured in megabytes, gigabytes, terabytes, petabytes, or exabytes
92
Data Variety
Diversity in data types, including structured, semi-structured, and unstructured formats like images, audio, and mobile data
93
Data Velocity
The rapid rate of data generation and processing, aiming for real-time outputs
94
Traditional Methods
Classical statistical methods adapted for business applications. Not including advanced statistical analyses
95

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

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