Data Analytics Essentials (Video)

Vocabulary flashcards covering key terms and concepts from the data analytics lecture notes.

Data Analytics

Process of examining data to extract insights, generate reports, and support decision making.

Hidden Insights

Valuable patterns discovered in data that inform business decisions.

Reports

Documents produced from data analysis to communicate findings to teams.

Market Analysis

Evaluation of market conditions, competitors, and opportunities using data.

Business Requirements

Data-driven needs of an organization reflected in analytics goals and outcomes.

Improved Decision Making

Using data-driven insights to replace guesswork and guide choices.

Personalization

Tailoring customer service and recommendations to individual preferences using data.

Efficient Operations

Streamlined processes, cost savings, and optimized production from data insights.

Effective Marketing

Assessing and refining campaigns with data to improve performance and target audiences.

Data Analytics Lifecycle

Sequential steps from problem understanding to result interpretation.

Understand the Problem

Define business goals and problems to guide analytics efforts.

Data Collection

Gathering relevant transactional and customer data for analysis.

Data Cleaning

Removing errors, duplicates, and missing values to prepare data for analysis.

Data Exploration and Analysis

Investigating data to uncover patterns and insights using visualization and modeling.

Interpret the Results

Evaluating outcomes to see if they meet expectations and extracting actionable insights.

Exploratory Data Analysis (EDA)

Approach to summarize data characteristics, often with visuals.

Data Visualization

Graphical representation of data to reveal patterns and support interpretation.

Business Intelligence Tools

Software for analyzing data and creating dashboards and reports.

Predictive Modelling

Techniques to forecast future outcomes based on historical data.

Data Mining

Process of discovering patterns and relationships in large datasets.

Open-source Tools

Free software options used in data analytics (e.g., R, Python libraries).

R Programming

Statistical programming language used for analytics and modeling.

Python

Open-source language with libraries for machine learning and visualization.

Tableau Public

Free visualization tool that connects to data sources and creates dashboards with real-time updates.

QlikView

In-memory data processing tool enabling fast visualization and data association.

SAS

Programming language/environment for data manipulation and analytics.

Microsoft Excel

Widely used spreadsheet tool for data analysis and pivot tables.

RapidMiner

Platform for predictive analytics, data mining, and machine learning.

KNIME

Open-source analytics platform with visual workflows for data modeling.

Open Refine

Data cleaning tool for transforming and parsing messy data.

Apache Spark

Fast large-scale data processing engine for in-memory computation and ML pipelines.

Retail Analytics

Using data to understand customer needs, predict trends, and optimize the supply chain and customer journey.

Healthcare Analytics

Applying data analysis to improve diagnoses, treatments, and drug development.

Manufacturing Analytics

Using data to reduce costs, solve supply chain and equipment issues, and optimize operations.

Banking Analytics

Assessing loan risk, detecting fraud, and reducing customer churn using data.

Logistics Analytics

Optimizing routes and ensuring timely delivery through data insights.