Business Analytics: Key Concepts, Tools, and Techniques

Posted on Mar 28, 2025 in Management Assistance

Here’s a condensed and organized cheat sheet based on the provided content:

Business Analytics Cheat Sheet

Definition of Business Analytics:

  • Purpose: Analyzing past business performance to guide future decisions and identify growth opportunities.

Five Stages of Business Analytics:

  1. Data Wrangling: Cleaning, structuring, and integrating raw data for analysis.
  2. Descriptive Analytics: Summarizing historical data to answer “What has happened?” (e.g., histograms, means).
  3. Predictive Analytics: Using historical data to predict future outcomes (“What will happen?”) using models like regression.
  4. Prescriptive Analytics: Recommending actions based on optimization models (“What should we do?”).
  5. Storytelling: Communicating insights through visualization to help decision-makers take action.

The Three Legs of Business Analytics:

  • Data: Collection and preparation.
  • Analytics: Statistical and mathematical models for insights.
  • Visualization: Communicating insights visually.

Steps to Prepare Data:

  • Clean: Handle missing values, outliers, and inconsistent data.
  • Structure: Organize data into relational formats.
  • Integrate: Combine data from multiple sources.

Data Visualization Overview

Introduction to Data Visualization:

  • Purpose: To represent complex data visually for easier understanding, identifying patterns, and informed decision-making.

Benefits of Data Visualization:

  • Ease of Understanding: Visuals are processed faster than raw data.
  • Cross-Language Communication: Visuals are universally understood.
  • Flexibility: Adaptable to different contexts.

Color in Visualization:

  • Purpose: Differentiate data points, highlight patterns, and direct attention.
  • Accessibility: Use color schemes that accommodate color blindness (e.g., blue & orange).

Types of Visualizations:

  • Basic:
    • Bar Chart: Compares categories.
    • Line Chart: Shows trends over time.
    • Pie Chart: Displays proportions.
    • Scatter Plot: Shows relationships between two variables.
  • Advanced:
    • Bubble Chart: Adds a third dimension.
    • Tree Map: Displays hierarchical data.
    • Radar Chart: Compares multiple variables.
    • Geographical Maps: Shows regional data distribution.

Interactive Visualizations:

  • Allows users to explore data by interacting with visuals.

Business Analytics Tools

Spreadsheets in Business Analytics (Excel):

  • Use: Clean, structure, and analyze small to medium datasets.
  • Data Transformation: Handle missing data, outliers, and erroneous values.

Programming Tools for Data Analytics:

  • Python:
    • Pros: Versatile, supports machine learning, great for large data.
    • Libraries: Pandas, NumPy.
  • R:
    • Pros: Statistical computing, extensive library support.
    • Libraries: ggplot2.

Excel vs. R/Python:

  • Excel: Best for small datasets, basic analysis.
  • R/Python: Suited for large datasets and advanced analytics.

Big Data & NoSQL

Big Data Challenges:

  • Volume, Velocity, Variety: Traditional databases struggle with big data management.

Relational Databases:

  • SQL: Used for structured data management with tables and relationships (e.g., MySQL, PostgreSQL).
  • Key Concepts: Primary & Foreign Keys, SQL Queries (SELECT, JOIN, WHERE).

NoSQL Databases:

  • Types:
    • Key-Value Stores: Store data as key-value pairs.
    • Document Databases: Store complex data in formats like JSON.
    • Graph Databases: Focus on relationships between data (e.g., social networks).
  • Benefits: Scalability, flexibility, and ideal for unstructured data.

Structured Data & SQL

Relational Databases:

  • Structured Data: Organized in rows and columns, ideal for querying and analysis.
  • Key Concepts: Primary Key, Foreign Key.

SQL Commands:

  • SELECT: Retrieve data.
  • WHERE: Filter records.
  • JOIN: Combine data from multiple tables (INNER, LEFT, RIGHT, FULL OUTER).
  • Aggregation: COUNT(), SUM(), AVG(), MAX(), MIN().
  • Advanced: ORDER BY, DISTINCT, AS (renaming).

Data Mining & Cluster Analysis

Data Mining:

  • Purpose: Discover hidden patterns and relationships in large datasets.
  • Techniques:
    • Anomaly Detection: Identifying rare events.
    • Association Rule Analysis: Market basket analysis.
    • Cluster Analysis: Group similar data points.

Cluster Analysis:

  • Methods:
    • Hierarchical Clustering: Builds a tree structure.
    • K-means Clustering: Divides data into K clusters.
  • Math: Euclidean distance is commonly used to measure similarity.

Segmentation:

  • Purpose: Divide markets or datasets into smaller groups based on shared characteristics.
    • Demographic: Age, gender, income.
    • Psychographic: Lifestyle, values.
    • Behavioral: Purchase history, brand loyalty.

This condensed cheat sheet captures the key concepts and tools in business analytics, data visualization, big data, SQL, and data mining.