Essential Data Science and Decision-Making Concepts
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Part A: Fundamental Concepts
1. What is Data Science?
Data Science is an interdisciplinary field that uses scientific methods, statistics, algorithms, and computing techniques to extract knowledge and insights from data for decision-making.
2. Structured vs. Unstructured Data
| Feature | Structured Data | Unstructured Data |
|---|---|---|
| Format | Organized in rows and columns | No predefined format |
| Storage | Easy to store in databases | Difficult to analyze |
| Examples | Excel sheets | Images, videos, emails |
3. What is Data Cleaning?
Data cleaning is the process of identifying and correcting errors, missing values, duplicate records, and inconsistencies in data to improve its quality.
4. Define Data Visualization
Data visualization is the graphical representation of data using charts, graphs, and dashboards to understand patterns and trends.
5. What is Machine Learning?
Machine Learning is a branch of Artificial Intelligence that enables computers to learn from data and make predictions without being explicitly programmed.
6. The 5 V's of Big Data
- Volume: The scale of data.
- Velocity: The speed of data processing.
- Variety: The diversity of data types.
- Veracity: The accuracy and quality of data.
- Value: The actionable insights derived.
7. What is Cloud Computing?
Cloud Computing is the delivery of computing services such as storage, servers, databases, and software over the internet.
8. What is Risk in Decision-Making?
Risk refers to a situation where the probabilities of different outcomes are known, but the actual outcome is uncertain.
9. What is Sensitivity Analysis?
Sensitivity Analysis is a technique used to determine how changes in input variables affect decision outcomes.
10. What is a Heuristic?
A heuristic is a simple rule or mental shortcut used to make decisions quickly when complete information is unavailable.
Part B: Applied Data Science
11. Role of Data Science in Business
- Identifies customer behavior.
- Supports forecasting and planning.
- Improves operational efficiency.
- Reduces business risks.
- Enables evidence-based decisions.
- Example: Retail companies analyze sales data to optimize inventory.
12. Data Preprocessing Steps
- Data Cleaning: Remove errors and missing values.
- Data Integration: Combine data from multiple sources.
- Data Transformation: Convert data into suitable formats.
- Data Reduction: Reduce data size while retaining information.
- Data Normalization: Scale data for analysis.
13. Supervised vs. Unsupervised Learning
| Feature | Supervised Learning | Unsupervised Learning |
|---|---|---|
| Data Type | Uses labeled data | Uses unlabeled data |
| Goal | Predicts outcomes | Finds hidden patterns |
| Methods | Classification & Regression | Clustering & Association |
| Example | Spam detection | Customer segmentation |
14. Advantages of Cloud Computing
- Cost reduction and scalability.
- Easy accessibility and collaboration.
- Automatic software updates.
- Data backup and disaster recovery.
Part C: Advanced Analytics
15. Big Data and Its Characteristics
Big Data refers to extremely large and complex datasets that cannot be processed using traditional systems. It is defined by the 5 V's (Volume, Velocity, Variety, Veracity, Value). Applications include healthcare, banking, e-commerce, and social media.
16. Decision-Making Frameworks
- Certainty: Outcomes are known; no risk.
- Risk: Outcomes are uncertain, but probabilities are known.
- Uncertainty: Both outcomes and probabilities are unknown; relies on judgment.
High-Probability Exam Topics
- Data Science & Data-Driven Decision Making
- Structured vs. Unstructured Data
- Data Cleaning & Data Visualization
- Supervised vs. Unsupervised Learning
- Tableau Basics
- Big Data (5 V's)
- Cloud Computing
- Decision Trees & Payoff Tables
- Sensitivity & Risk Analysis
- Simulation
- Behavioral Decision Making & Heuristics