Data Science Fundamentals Practice Exam

Data Science Fundamentals Practice Exam

Data Science Fundamentals is the foundation of learning how to work with data to solve problems and make decisions. It covers the basic concepts of collecting, organizing, and analyzing data using simple tools and methods. This helps people understand how data can uncover patterns, trends, and insights that guide better business strategies, scientific research, or even everyday decisions.
For example, companies use data science fundamentals to study customer preferences, healthcare systems use it to track patient outcomes, and governments apply it to improve public services. It’s the first step toward a career in data science, ensuring learners grasp the essentials before diving into advanced techniques.

Who should take the Exam?

This exam is ideal for:

• Aspiring Data Scientists
• Data Analysts
• Business Analysts
• Students entering the data field
• IT Professionals exploring analytics
• Researchers using data for studies
• Professionals transitioning to AI/ML roles

Skills Required

• Basic math and statistics
• Logical and analytical thinking
• Curiosity to work with data
• Basic computer skills

Knowledge Gained

• Understanding data science concepts
• Data collection and cleaning techniques
• Using visualization to tell stories with data
• Introduction to statistics and probability
• Basic exposure to Python, R, or Excel for analysis

Course Outline

The Data Science Fundamentals Exam covers the following topics - 

1. Introduction to Data Science

• What is data science?
• Applications across industries
• Importance in decision-making

2. Data Collection & Sources

• Structured vs unstructured data
• Databases and file formats
• Data ethics and privacy

3. Data Cleaning & Preparation

• Handling missing values
• Removing duplicates and errors
• Standardization and normalization

4. Basic Statistics for Data Science

• Mean, median, mode
• Probability basics
• Correlation and causation

5. Data Visualization

• Charts, graphs, and dashboards
• Tools like Excel, Tableau, or Matplotlib
• Storytelling with visuals

6. Introduction to Programming for Data Science

• Python basics
• R for beginners
• Simple data manipulation

7. Applications of Data Science Fundamentals

• Case study: Business analytics
• Case study: Healthcare insights
• Case study: Market trends

8. Future Pathways in Data Science

• From fundamentals to machine learning
• Big data and AI applications
• Career growth in analytics