MIS - Data Analytics

What is data analytics?

The process of investigating raw data to uncover trends, correlations, and answer specifically crafted questions to transform data into actionable information for decision-making.

What are the 3 main types of analytics?

• Descriptive: What happened? (past trends)

• Predictive: What will happen? (future forecasts)

• Prescriptive: What should we do? (recommended actions)

What does descriptive analytics do and give 2 examples?

• Purpose: Uncovers historical trends, answers "what happened?"

• Examples: ROI calculations, sales summaries, operational efficiency metrics

What are the 5 core methods of predictive analytics?

1. Probability analysis

2. Data mining

3. Statistical modeling

4. Machine learning

5. Deep learning

What makes prescriptive analytics the most advanced form?

It not only predicts WHAT, WHEN, and WHY scenarios might occur, but also recommends specific ACTIONS to optimize outcomes.

How do data warehouses and data marts differ?

• Data Warehouse: Large, centralized repository for all organizational data

• Data Mart: Smaller, focused subset for specific departments/functions

What are the 3 types of data stored in systems?

1. Structured: Organized in rows/columns (databases)

2. Semi-structured: Data with some organization (JSON files)

3. Unstructured: Text, images, videos (data lakes)

What is the key formula for data analyst project success?

Data + Organizational and Business Knowledge = Problem Solved/Opportunity Realized (Data alone is insufficient without business context)

What are the 6 essential data analyst skills?

• Communication (technical writing/presentation)

• Programming (SQL, Python, R)

• Analytical problem-solving

• Data visualization (Tableau, Power BI)

• Spreadsheet expertise (Excel)

• Business context understanding

When do you use SQL vs NoSQL?

• SQL: Structured data in relational databases (MySQL, Oracle)

• NoSQL: Unstructured data in data lakes (JSON format)

What are the key differences between spreadsheets and databases?

• Spreadsheets: Simple analysis, limited scale, individual use

• Databases: Complex relationships, multiple users, high data integrity, large-scale

What does OLAP do and where is it used?

• Function: Multidimensional analysis of data warehouses

• Uses: Performance management, financial reporting, planning, real-time decision-making

What is data mining and what are its 4 key properties?

• Definition: Automatically searching large datasets for hidden patterns

• Properties: 1) Automatic discovery, 2) Prediction capabilities, 3) Actionable insights 4.) Focus on large datasets

What are the 3 main data visualization tools and their strengths?

• Tableau: Cross-platform, user-friendly, connects to many sources

• Power BI: Microsoft integration, enterprise-focused

• Looker: Cloud-based, enterprise environments

What are 4 critical principles to avoid misleading visualizations?

1. Proper scaling: Always include zero on axes

2. Avoid truncation: Don't shorten axes

3. Uniform scaling: Consistent scales in charts

4. Represent all data: Don't cherry-pick data