DSC1630 ASSIGNMENT 1 [ COMPLETE ANSWERS] SEMESTER 2 2024
1. Introduction to Data Science
Question: Define data science and explain its significance in modern business practices.
Detailed Answer: Data science is an interdisciplinary field that uses scientific methods,
processes, algorithms, and systems to extract knowledge and insights from structured and
unstructured data. Its significance in modern business practices includes:
● Decision Making: Data science enables businesses to make informed decisions by
analyzing large datasets to uncover trends and patterns.
● Predictive Analytics: Helps in forecasting future trends based on historical data,
leading to better strategic planning.
● Operational Efficiency: Optimizes business processes and improves operational
efficiency by identifying inefficiencies.
● Customer Insights: Provides insights into customer behavior and preferences, allowing
for personalized marketing and improved customer service.
● Innovation: Drives innovation by discovering new business opportunities and creating
data-driven products and services.
2. Data Collection and Management
Question: Discuss various methods of data collection and the importance of data quality.
Detailed Answer: Methods of data collection include:
● Surveys and Questionnaires: Gather information directly from respondents. Important
for collecting subjective data.
● Experiments: Conduct controlled experiments to gather data on variables of interest.
● Web Scraping: Extract data from websites and online sources.
● Transactional Data: Captured during business transactions (e.g., sales records).
● Sensors and IoT Devices: Collect data from physical devices and sensors.
The importance of data quality includes:
● Accuracy: Ensures the data reflects the true values or states.
● Consistency: Data should be consistent across different sources and systems.
● Completeness: All necessary data should be collected without missing values.
● Timeliness: Data should be up-to-date to remain relevant and useful.
● Reliability: Data should be reliable to ensure that analyses and conclusions drawn are
trustworthy.
3. Data Analysis Techniques
Question: Explain the difference between descriptive and inferential statistics.
Detailed Answer:
1. Introduction to Data Science
Question: Define data science and explain its significance in modern business practices.
Detailed Answer: Data science is an interdisciplinary field that uses scientific methods,
processes, algorithms, and systems to extract knowledge and insights from structured and
unstructured data. Its significance in modern business practices includes:
● Decision Making: Data science enables businesses to make informed decisions by
analyzing large datasets to uncover trends and patterns.
● Predictive Analytics: Helps in forecasting future trends based on historical data,
leading to better strategic planning.
● Operational Efficiency: Optimizes business processes and improves operational
efficiency by identifying inefficiencies.
● Customer Insights: Provides insights into customer behavior and preferences, allowing
for personalized marketing and improved customer service.
● Innovation: Drives innovation by discovering new business opportunities and creating
data-driven products and services.
2. Data Collection and Management
Question: Discuss various methods of data collection and the importance of data quality.
Detailed Answer: Methods of data collection include:
● Surveys and Questionnaires: Gather information directly from respondents. Important
for collecting subjective data.
● Experiments: Conduct controlled experiments to gather data on variables of interest.
● Web Scraping: Extract data from websites and online sources.
● Transactional Data: Captured during business transactions (e.g., sales records).
● Sensors and IoT Devices: Collect data from physical devices and sensors.
The importance of data quality includes:
● Accuracy: Ensures the data reflects the true values or states.
● Consistency: Data should be consistent across different sources and systems.
● Completeness: All necessary data should be collected without missing values.
● Timeliness: Data should be up-to-date to remain relevant and useful.
● Reliability: Data should be reliable to ensure that analyses and conclusions drawn are
trustworthy.
3. Data Analysis Techniques
Question: Explain the difference between descriptive and inferential statistics.
Detailed Answer:
