Types of Graphs in Data Representation

Different Types of Graphs Used For

• Line graphs display trends over time, showing continuous variable changes. X-axis is time or another continuous variable, Y-axis shows the measured value.
• Bar graphs compare categories or groups. Each bar's height represents a category's value. Used for discrete data.
• Scatter plots show the relationship between two variables. Points represent data points, highlighting correlation or lack thereof.
• Histograms represent the distribution of a single continuous variable. X-axis shows ranges of values, bar height shows frequency within ranges. Useful for identifying distribution shapes (e.g., normal, skewed).
• Pie charts display proportions of categories as slices. Show relative sizes, but less insightful for detailed comparisons.
• Area charts show accumulated values over time, similar to line charts, but the area under the line is filled. Visualize cumulative totals.
• Box plots visualize a variable's distribution across categories. Displays median, quartiles, and potential outliers for central tendency, spread, and skewness.

ICT Applications in Data Science

• Data storage and retrieval use robust ICT systems for large datasets. Databases like SQL and NoSQL efficiently store and retrieve data. Cloud-based storage enhances scalability and accessibility.
• ICT enables fast data processing and analysis, supporting complex techniques like machine learning using computing power and algorithms. Python libraries like Pandas and Scikit-learn are key tools.
• ICT creates sophisticated visualizations such as interactive dashboards and graphs improving data understanding and actionability for stakeholders.
• ICT provides platforms for communication and collaboration amongst data scientists. This allows insightful sharing and work progress.
• ICT handles large dataset transfer across networks, bridging geographical gaps and facilitating collaboration among geographically dispersed teams.

Historical Questions in Data Science

• Early analysis methods explored how statisticians and mathematicians analyzed data without modern computational tools. Understanding early approaches provides context for modern techniques.
• Computational limitations of the past influenced data science scope and approaches. Awareness of historical constraints provides perspective on today's possibilities.
• Historical data collection and storage methods reveal challenges in data analysis. This perspective sheds light on modern collection strategies and accessibility impacts on progress.
• Theoretical frameworks shaped the early development of data science methodologies, contextualizing existing approaches.
• Historical applications of data science, such as census data and epidemiological studies, demonstrate the use of data to solve real-world problems. These case studies offer insightful patterns in data use.
• Algorithms and computational techniques have significantly changed throughout data science history. Evaluating these transformations aids in predicting future trends and development.
• Ethical considerations related to bias, fairness, and privacy regarding data and analysis have been explored historically. Examining these concerns in the past informs modern ethical frameworks.
• Early computational infrastructure for data analysis highlights early limitations and the pathway to modern capabilities.