Data Science Basics - SSS 2 | CMPNOTE

DATA SCIENCE BASICS

Subject: Digital Technologies
Class: SSS 2
Term: Second Term
Week: 6
Topic: Data Science Basics
Duration: 80 Minutes (Double Period)

Learning Objectives

By the end of the lesson, students should be able to:

Explain the meaning and importance of data science.
Identify different methods of collecting data.
Explain how data can be stored and organized.
Describe data cleaning and data preparation.
Perform simple data organization and cleaning activities using a sample dataset.

1. Introduction to Data Science

People and organizations generate large amounts of data every day. Schools record students' scores and attendance, hospitals maintain patient records, shops record sales, and banks keep transaction records. These records can help people make better decisions when they are properly collected, organized and examined.

For example, a school may examine students' examination results to identify subjects in which learners need additional support. A shop may study its sales records to determine which products customers buy most frequently.

Data science provides methods for working with data to discover useful information and solve problems.

2. Meaning and Importance of Data Science

Data science is an interdisciplinary field that uses scientific methods, statistics, computing and other analytical techniques to collect, process, analyze and interpret data in order to obtain useful information and support decision-making.

Data science combines knowledge from different areas, including mathematics, statistics, computer programming and the subject being investigated. It can involve simple tables and charts as well as advanced analytical techniques.

2.1 Data and Information

Although the terms data and information are related, they have different meanings.

Term	Meaning	Example
Data	Raw facts, figures or observations that have not yet been fully interpreted.	Scores of 45, 60, 75 and 80.
Information	Data that has been processed or interpreted to make it meaningful.	The highest score is 80, and the lowest score is 45.

2.2 Importance of Data Science

Data science is important for several reasons.

Better decision-making: It helps individuals and organizations make decisions based on evidence rather than guesswork.
Identifying patterns: It helps reveal repeated behaviours, relationships and trends in data.
Solving problems: It helps investigate problems and identify possible solutions.
Forecasting: Historical data can be used to estimate future outcomes, although predictions are not always accurate.
Improving efficiency: Organizations can identify delays, waste and areas that need improvement.
Supporting research: Researchers use data to investigate questions and evaluate findings.
Understanding customers: Businesses can study sales and feedback to understand customers' needs.

2.3 Applications of Data Science

Area	Application
Education	Analyzing examination results, attendance and learning progress.
Healthcare	Studying health records and supporting medical research.
Agriculture	Analyzing rainfall, soil conditions and crop yields.
Business	Examining sales, customer preferences and product demand.
Transportation	Studying traffic patterns and travel times.
Environment	Monitoring rainfall, pollution and changes in temperature.

Example: If a teacher records the scores of 40 students, the scores can be organized in a spreadsheet. The teacher can calculate the average, identify the highest score and determine how many students scored below 50. These results provide information that can guide teaching.

3. Data Collection Methods

Data collection is the process of gathering facts, measurements, observations or responses for a particular purpose.

Before analyzing data, it is necessary to collect relevant and reliable information. The method selected depends on the purpose of the investigation, the type of data required, available resources and the people or things being studied.

3.1 Common Methods of Data Collection

A. Questionnaires

A questionnaire is a set of written or digital questions used to obtain information from respondents.

Example: A school distributes a questionnaire to find out which subjects students find most difficult.

Advantage: Questionnaires can collect responses from many people.

B. Interviews

An interview involves asking people questions directly and recording their answers.

Example: A researcher interviews teachers about the challenges they face when using computers in class.

Advantage: The interviewer can ask follow-up questions to obtain more details.

C. Observation

Observation involves watching an activity, event or situation and recording what happens.

Example: A researcher observes how students use computers in a school laboratory.

Advantage: It provides information about actual behaviour or events.

D. Experiments and Measurements

Experiments involve carrying out controlled activities to investigate a question. Measurements involve recording quantities such as temperature, distance, time or weight.

Example: Students measure the temperature of water at different times and record their readings.

E. Existing Records and Documents

Data can be obtained from existing records, reports, registers, books, databases and published statistics, provided they are suitable for the intended purpose.

Example: A teacher uses an approved school attendance register to study attendance patterns.

F. Digital Devices and Online Forms

Computers, mobile devices, sensors and online forms can collect or record data electronically.

Example: An electronic form records students' responses to a school survey.

3.2 Primary and Secondary Data

Data can also be classified according to where it comes from.

Type	Meaning	Example
Primary data	Data collected directly by a person or organization for a particular investigation.	Responses collected through a new student questionnaire.
Secondary data	Data that has already been collected by another person or organization or for an earlier purpose.	Using published population statistics for a school project.

3.3 Qualities of Good Data Collection

Good data collection should follow these principles:

Accuracy: Information should correctly represent what is being measured or reported.
Relevance: The data should relate to the question being investigated.
Completeness: Important required fields should not be unnecessarily left blank.
Consistency: Similar information should be recorded using consistent methods and formats.
Reliability: The method should produce dependable results when appropriately repeated.
Privacy: Personal information should be collected and handled responsibly.

Important: Students should not collect sensitive personal information unnecessarily. When conducting a classroom survey, explain its purpose, seek appropriate permission and avoid publishing identifiable responses without authorization.

4. Data Storage and Organization

Data storage is the process of saving data in a location or medium so that it can be retrieved and used later.

Data organization is the arrangement of data in a logical and consistent structure that makes it easier to understand, search, compare and analyze.

After data has been collected, it should be stored safely and arranged properly. Poorly stored or disorganized data can be difficult to find, compare and analyze.

4.1 Methods of Storing Data

Storage Method	Description	Example
Paper records	Information is written or printed on paper.	Attendance registers and printed reports.
Spreadsheets	Data is arranged in rows and columns using spreadsheet software.	Student scores stored in Microsoft Excel.
Databases	Structured collections of data managed by database software.	A school database containing student records.
Computer storage	Files are saved on a computer's storage device.	CSV files, text files and spreadsheet documents.
Cloud storage	Files are stored on remote servers and accessed through an internet connection.	Approved files stored in Google Drive or OneDrive.
External storage	Data is saved on removable storage devices.	USB flash drives and external hard drives.

4.2 Organizing Data in Tables

A table arranges related information into rows and columns. In a spreadsheet or database, each row commonly represents a record, while each column represents a field or attribute.

Consider the following sample table of students' scores:

Student ID	Student Name	Subject	Score
ST001	Ada	Digital Technologies	75
ST002	Emeka	Digital Technologies	62
ST003	Fatima	Digital Technologies	88
ST004	Daniel	Digital Technologies	54

In this table:

Column: Contains one type of information, such as Student Name or Score.
Row: Contains the information for one student.
Record: A collection of related fields describing one item or person.
Field: A particular category of information in a record, such as Student ID.

4.3 Good Practices for Data Organization

Use clear and meaningful column headings.
Keep similar types of data in the same column.
Use a consistent format for dates, names and measurements.
Assign unique identifiers when appropriate to distinguish records.
Arrange records logically, such as alphabetically or by date.
Use descriptive filenames and well-organized folders.
Restrict access to confidential records.
Make secure backups to reduce the risk of losing important data.

4.4 Common Data File Formats

File Format	Common Use
CSV (.csv)	Stores tabular data as comma-separated values, often for use in spreadsheets and data analysis.
XLSX (.xlsx)	Stores spreadsheet data, formulas and workbook features.
TXT (.txt)	Stores plain text.
JSON (.json)	Stores structured data in a format commonly used by applications and web services.

Example: A teacher can enter students' names and scores into a spreadsheet, sort the scores from highest to lowest and calculate the class average. This makes the information easier to interpret than an unorganized list of figures.

5. Introduction to Data Cleaning and Preparation

Data cleaning is the process of identifying and correcting or handling inaccurate, incomplete, duplicated or inconsistent data.

Data preparation is the process of getting collected data into a suitable form for analysis. It can include cleaning, organizing, transforming and checking the data.

Collected data is not always ready for analysis. Some entries may be missing, some records may appear more than once, and different people may record the same type of information in different ways.

Data cleaning and preparation help improve the quality and usefulness of a dataset. However, cleaning must be done carefully so that valid information is not mistakenly removed or changed.

5.1 Common Data Quality Problems

Problem	Meaning	Example
Missing data	A required value has not been recorded.	A student's score field is blank.
Duplicate records	The same record has been entered more than once.	The same student appears twice with identical details.
Inconsistent formatting	The same type of information is recorded in different formats.	Dates are entered as 12/10/2026 and 12 October 2026.
Invalid values	A value falls outside the permitted or expected range.	A test score is entered as 150 when the maximum is 100.
Typing errors	Information is entered incorrectly because of a typing mistake.	A student's name is misspelled.
Inconsistent categories	Different labels are used for the same category.	Some entries use “JSS 2” while others use “Junior Secondary Two”.

5.2 Steps in Data Cleaning and Preparation

Inspect the dataset: Examine the records and identify the fields available.
Check for missing values: Identify blank or incomplete entries and determine how they should be handled.
Identify duplicates: Check whether the same record has been entered more than once.
Correct errors: Compare questionable entries with reliable original records before making corrections.
Standardize formats: Use consistent formats for dates, categories, names and measurements.
Validate values: Check that entries fall within acceptable ranges and follow the required rules.
Organize the data: Arrange records in tables and use clear headings.
Save and review: Save the prepared dataset and check it again before analysis.

Important: A missing value should not automatically be replaced with zero. Zero may be a genuine measurement, while a blank value may mean that the information was not recorded. Check the meaning of the data before making changes.

5.3 Simple Example of Data Cleaning

Suppose a teacher collects the following sample scores from a class:

Student	Original Score	Problem Identified
Ada	75	No obvious problem.
Emeka	62	No obvious problem.
Fatima	150	Exceeds the maximum score of 100.
Daniel	Blank	Missing score.
Ada	75	Possible duplicate record.

The teacher should investigate Fatima's score using the original assessment record, check whether Ada's second entry is a duplicate, and find out why Daniel's score is missing.

The teacher should not simply change 150 to 100 or enter zero for Daniel without verifying the correct information. If the duplicate is confirmed, the extra record can be handled appropriately while preserving the original data when necessary.

5.4 Tools Used for Data Cleaning and Preparation

Microsoft Excel: Can sort, filter, find duplicates and apply data validation.
Google Sheets: Can organize data in tables and help users identify and correct inconsistencies.
Database software: Can help enforce data types, required fields and other rules.
Programming languages: Python and other languages can automate data-cleaning tasks when appropriate tools and libraries are available.

For basic classroom activities, students can use a spreadsheet or a manually prepared table to identify missing values, repeated entries and inconsistent formats.

6. Practical Classroom Activity

Activity: Collect, organize and clean a small sample dataset.

Students should work with fictional data so that private information is not exposed.

Sample Dataset

Student ID	Name	Score	Class
ST001	Ada	75	SSS 2
ST002	Emeka	62	SSS 2
ST003	Fatima	150	SSS 2
ST002	Emeka	62	SSS 2
ST004	Daniel		SSS 2

Instructions

Identify the columns and records in the dataset.
Find the repeated student record.
Identify the score that needs verification.
Identify the missing score.
Explain how you would investigate each problem before correcting the data.
Rewrite the table neatly, showing which entries require checking.

Expected learning outcome: Students should recognize a possible duplicate, an invalid score and a missing value. They should explain that original records must be checked before corrections are made.

7. Summary

Data science uses scientific methods, statistics and computing to obtain useful information from data. It supports decision-making, research, forecasting and problem-solving in areas such as education, healthcare, agriculture and business.

Data can be collected through questionnaires, interviews, observations, experiments, existing records and digital devices. It can be stored in paper records, spreadsheets, databases, computer storage and cloud services.

Data organization arranges information logically, often in rows and columns. Data cleaning identifies and handles problems such as missing values, duplicates, typing errors and inconsistent formats. Data preparation makes the dataset suitable for analysis. Accuracy, privacy, validation and careful handling are important throughout the process.

8. Evaluation Questions

Define data science.
State five reasons why data science is important.
Explain the difference between data and information, giving one example.
List five methods of collecting data.
Differentiate between primary data and secondary data.
Explain the meaning of data storage and data organization.
State four methods of storing data.
What is the difference between a row and a column in a data table?
Define data cleaning and data preparation.
Identify five common data quality problems and explain how each can be handled.

9. Assignment

Answer the following questions in your notebook.

Explain four applications of data science in everyday life.
Describe four methods of data collection and give one example of each.
Draw a table containing five fictional student records with the headings Student ID, Name, Subject and Score.
Explain three problems that could occur in your table and how you would investigate them.
State five good practices for storing and organizing data safely.

10. Interactive Revision Quiz

Choose one answer for each question. Click Mark Quiz to see your score.

1. What is data science?

A. The study of computer hardware only B. The process of deleting all stored information C. The use of scientific methods and computing to obtain useful information from data D. A method of drawing computer parts

2. Which of these is an example of data collection?

A. Asking students to complete a questionnaire B. Deleting every student record C. Formatting a computer screen D. Installing a keyboard

3. Data collected directly for a particular investigation is called:

A. Duplicate data B. Missing data C. Secondary data D. Primary data

4. Which application can be used to organize data in rows and columns?

A. A music player B. Microsoft Excel C. A video game only D. A drawing tool only

5. What does a row commonly represent in a data table?

A. A computer password B. A file extension C. A record containing related fields D. A storage device

6. Which of these is a common data storage method?

A. Guessing B. Random conversation C. Erasing the records D. A database

7. A record that appears more than once in a dataset is a:

A. Duplicate record B. Data type C. Unique identifier D. Column heading

8. What should you do when a student's score is missing?

A. Automatically enter 100 B. Check the original record to determine the correct value C. Delete the entire class list D. Guess the score

9. Which file format is commonly used to store tabular data as comma-separated values?

A. MP3 B. PNG C. CSV D. MP4

10. Why is data cleaning important?

A. It guarantees that every conclusion will be correct B. It removes the need to collect data C. It makes all datasets identical D. It helps identify and handle errors and inconsistencies in data

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