Business Intelligence (BI)
Explore the Flashcards:
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Tools and techniques for analyzing and understanding past data to make strategic decisions.
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Historical Data
Collected past data used for analysis.
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Dashboard
A user interface that visually summarizes key data and metrics.
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Strategic Decisions
Long-term planning choices.
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Tactical Decisions
Short-term, specific actions.
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Artificial Intelligence (AI)
Enabling machines to perform tasks that typically require human intelligence.
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Machine Learning (ML)
A branch of artificial intelligence where computers learn from data to improve their performance on tasks.
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Data Analytics
The process of examining datasets to draw conclusions and find patterns using statistical techniques.
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Real-time Dashboards
Interactive tools that display data and metrics as they are updated in real-time.
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Third-party Data
Data collected by an external entity; Not your own company's data.
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Predictive Analytics
The process of using data and statistical algorithms to predict future values or trends based on historical data.
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Algorithm
A set of rules or instructions designed to solve problems or perform tasks, often used in computing.
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Data Pattern
A recurring or recognizable element in a dataset, often indicating a trend or relationship.
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Client Retention
Businesses aiming to understand and predict customer purchasing behaviors to sell more products to existing clients.
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Client Acquisition
The process of gaining new clients or customers for a business, often through marketing and sales strategies.
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Fraud Prevention
Methods and systems used to detect and prevent fraudulent activities, such as unauthorized transactions.
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Speech Recognition
Technology that recognizes and interprets human speech, converting it into text or commands.
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Image Recognition
A computer technology that identifies objects, places, people, and other elements in digital images.
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Symbolic Reasoning
The process in artificial intelligence where symbols represent concepts or entities to make logical deductions.
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Advanced Analytics
Sophisticated data analysis techniques, often involving predictive models, machine learning, and big data.
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Data Collection
Gathering information systematically from various sources to analyze and make informed decisions.
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Data Analysis
The process of inspecting, cleaning, and modeling data with the goal of discovering useful information.
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Forecasting
The use of historical data to predict future events or trends, often used in business, finance, and weather predictions.
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Dataset
A collection of related sets of information, usually formatted in a table, used for analysis or processing.
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Analytical Tools
Software and applications used to analyze, visualize, and interpret data.
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Big Data
Extremely large data characterized by volume, variety, and velocity. Often requires cloud storage and processing.
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Real-time Data Processing
The continuous and immediate processing of data as it's collected or generated.
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Data Pre-processing
The initial steps in data analysis involving cleaning and organizing data for further use.
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Text Data Mining
Extracting useful information and insights from textual data using analytical methods.
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Data Masking
The practice of hiding original data with modified content (e.g., characters or other data) to protect sensitive information.
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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.
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Inventory Management
The practice of overseeing and controlling the ordering, storage, and use of a company's inventory.
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Seasonality Patterns
Trends or recurring changes in data observed at regular intervals throughout a year, often influenced by seasons.
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Shipment Logistics
The coordination of transporting goods from one place to another, including planning, execution, and tracking.
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Metrics
Quantitative measures used to track and assess the status of specific processes.
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KPIs
Specific metrics used to evaluate the success of an organization or activity in meeting its objectives.
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Customer Retention
Strategies and activities aimed at keeping customers engaged and continuing to purchase from a business.
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Business Goal Alignment
The process of ensuring that business activities and strategies are focused on achieving the company's primary objectives.
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Data Architect
A professional responsible for designing and managing an organization's data architecture to meet business needs.
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Data Engineer
A role focused on preparing 'big data' for analytical or operational uses, often involving building and maintaining data systems.
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Database Administrator
A specialist responsible for managing and maintaining database systems, ensuring their optimal performance and security.
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BI Analyst
A professional who analyzes data to provide insights and recommendations for improving business decisions and strategies.
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BI Consultant
An expert who advises businesses on how to use data analytics and BI tools to improve decision-making and performance.
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BI Developer
A professional who designs, develops, and maintains BI solutions, including data visualization and reporting tools.
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Data Scientist
A specialist in extracting insights and knowledge from complex data using various statistical, machine learning, and analytical techniques.
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Data Analyst
A professional who collects, processes, and performs statistical analyses on data to help make informed decisions.
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Machine Learning Engineer
An engineer specialized in designing and building machine learning models and systems.
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Business Analytics
The practice of using data analysis to inform and guide business decisions.
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Data Storytelling
The skill of communicating insights from data analyses through compelling narratives and visualizations.
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R
A programming language and environment widely used for statistical computing and graphics.
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Python
A versatile programming language popular in many fields, including data science, for its readability and vast libraries.
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Digital Signal Processing
The analysis and manipulation of digital signals, often for improving accuracy and reliability of digital communication.
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Supervised Learning
A type of machine learning where models are trained on labeled data to predict outcomes or classify data.
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Fraud Detection
Banks using machine learning to detect fraudulent credit card transactions.
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Predictive Modeling
Creating, testing, and validating a model to best predict the probability of an outcome..
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Data
Information, often in the form of facts or statistics, collected for reference or analysis.
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Model
In data science, a representation or abstraction of a real-world process, used for analysis and predictions.
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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.
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Optimization Algorithm
A method or procedure used to make a system or design as effective or functional as possible.
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Trial-and-Error Process
A problem-solving method involving repeated, varied attempts until success is achieved.
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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.
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Generalization
The ability of a model to perform well on new, unseen data after being trained on a dataset.
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Unsupervised Learning
A type of machine learning that finds patterns in data without pre-existing labels.
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Reinforcement Learning
A type of machine learning where an agent learns to behave in an environment by performing actions and receiving rewards.
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Support Vector Machines
A supervised machine learning model used for classification and regression analysis, effective in high-dimensional spaces.
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Neural Networks
Computational models inspired by the human brain, used in machine learning to recognize patterns and make decisions.
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Deep Learning
A subset of machine learning involving neural networks with many layers, enabling advanced pattern recognition.
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Random Forest Models
A machine learning method involving many decision trees to improve predictive accuracy and prevent overfitting.
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Bayesian Networks
A type of probabilistic model that uses Bayesian inference for probability computations.
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K-Means
A clustering algorithm in machine learning that divides a set of data points into k groups based on feature similarity.
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SQL
A programming language used to manage and manipulate relational databases.
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MATLAB
A high-level language and interactive environment used for numerical computation, visualization, and programming.
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Excel
Microsoft's spreadsheet software for data organization, analysis, and visual representation using formulas and tools.
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SPSS
A software package used for statistical analysis, particularly in social sciences.
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Hadoop
An open-source framework for storing data and running applications on clusters of commodity hardware,
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Numerical Data
Data that is quantifiable and measurable, like numbers, which can be used in mathematical calculations.
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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, vhocolate, and strawberry.
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Raw Data
Data in its original form, unprocessed and unfiltered. Example: Sensor readings directly recorded.
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Class Labelling
Assigning predefined categories to data points. Example: Tagging emails as 'spam' or 'not spam'.
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Handling Missing Values
Techniques to deal with absent data points. Example: Filling missing values with the average of existing data.
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Balancing
Adjusting datasets to have an equal number of instances in each category. Example: Ensuring equal cases of positive and negative outcomes in medical data.
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Data Shuffling
Randomly rearranging data points to prevent order bias. Example: Shuffling customer data before analysis.
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Entity-Relationship Diagram
A graphical representation of entities and their relationships.
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Relational Schema
A blueprint of a database structure, showing tables and relationships.
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Cluster Analysis
Grouping data points based on similarities. Example: Segmenting customers into groups based on buying habits.
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Time Series Analysis
Analyzing data points collected over time. Example: Examining stock prices over several months.
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Regression Analysis
Evaluating relationships between variables. Example: Predicting house prices based on size and location.
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Factor Analysis
Identifying underlying variables that explain observed patterns. Example: Analyzing survey responses to uncover hidden attitudes.
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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.
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Traditional Data
Tabular data containing numeric or text values, manageable from a single computer.
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Data Volume
The size of data, measured in megabytes, gigabytes, terabytes, petabytes, or exabytes.
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Data Variety
Diversity in data types, including structured, semi-structured, and unstructured formats like images, audio, and mobile data.
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Data Velocity
The rapid rate of data generation and processing, aiming for real-time outputs.
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Traditional Methods
Classical statistical methods adapted for business applications. Not including advanced statistical analyses.