DATA &

INTRODUCTION
TO DATA

SUBMITTED TO : MS. HINA SATAR

SUBMITTED BY: GROUP 1
 Group
Members:
● JAVERIA KHAN (ROLL NO. 89)
● M RASHID (ROLL NO. 88)
● M USMAN SALEEM (ROLL NO
83)
● NAVEED NAWAB (ROLL NO.61)
 WHAT IS DATA?
In terms of ICT, data is simply any numbers,
letters or symbols that can be entered into a
computer system.
ITEMS AND EXAMPLES OF DATA:A, 20, DOG,
3.1415927, ABC123, +++
Data values don’t have any meaning unless we
put them into context (context means a setting
or circumstance).

For instance, in the above example what does

the value 20 mean?
20 cm? 20 minutes? 20 cats?
Without a context the value 20 is meaningless.
But, if we provide a context for our data, it
becomes something far more useful: information
 TYPES OF DATA
Following are the types of data:
● Alphanumeric or Text:This allows you to type in
text, numbers and symbols.
Example:Forename: James, Surname: Smith ,Address:
73, High Street

● Number:This allows a whole number or a

decimal number.Only numbers can be entered,
no letters or symbols
For example: 1,2,3,4….

● Currency:This automatically formats the data to

have a £ or $ or Euro symbol in front of the data
and also ensures there are two decimal places.

For Example:£5.75, $54.9….

 ● Date/Time:This restricts data entry to 1-31 for
day (28 or 30 in appropriate months) and 1-12
for month.
❏ It checks that a date can actually exist, for
example, it would not allow 31/02/06 to be
entered.
❏ It formats the data into long, medium or short
date/time.
For example: Long Date: 20 February 2006, Medium
Date: 20-Feb-06, Short Date: 20/02/06,Long Time:
18:21:35,Medium Time: 06:21 PM, Short Time: 18:21

● Autonumber:This datatype will automatically

increase by 1 as records are added to the
database

For example: Record 1: 1 Record 2: 2

● Logical, Boolean, Yes/No:This datatype is

often referred to as different things ,
 you may hear it called 'logical', or 'boolean' or
'yes/no'
All it means is that the data is restricted to one of
only two choices.For example:
● Yes/No
● Male/Female
● Hot/Cold
● On/Off
TYPE OF DATA IN ICT
● Continuous data: Data that can be calculated and
has an infinite number of possible values within a
given range.
○ For example, temperature range is
continuous data.
● Boolean: Data that is used to show logic in code. It
usually has two values, true or false, and is used to
clarify conditional statements.
● Array: A linear data structure that stores
elements of the same data type
sequentially. Arrays are used to group items
efficiently and organize data so that related
items can be managed together.
● Analogue data:
● Quantitative data: Data that has a
numerical value and can be
processedContinuous data that is "analogous"
to the actual facts it represents. using
statistical methods.
● Enum: A data type that may be offered by
modern programming languages.
● Numbers: Numbers are stored as integers or
real numbers.
● Text: Text is stored as strings or characters.
● Multimedia: Data that includes photographs,
audio, video, and other specialized formats.
● Primitive type: A data type that has no
structures.
 DATA COLLECTION AND ITS
METHOD/PROCEDURE

Data collection is the process of collecting

and evaluating information or data from
multiple sources to find answers to research
problems, answer questions, evaluate
outcomes, and forecast trends and
probabilities.

IMPORTANCE OF DATA COLLECTION:It is an

essential phase in all types of research,
analysis, and decision-making, including that
done in the social sciences, business, and
healthcare.

During data collection, researchers must

identify the data types, the sources of data,
and the methods being used.
1. Primary Data Collection:The first techniques of data
collection is Primary data collection which involves the
collection of original data directly from the source or
through direct interaction with the respondents. This
method allows researchers to obtain firsthand
information tailored to their research objectives.
FOR EXAMPLE:
● Surveys and Questionnaires
● Interviews
● Observations
● Experiments
● Focus Groups

2. Secondary Data Collection:The next techniques

of data collection is Secondary data collection which
involves using existing data collected by someone else
for a purpose different from the original intent.
Researchers analyze and interpret this data to extract
relevant information.
FOR EXAMPLE:
● Published
● Source Online
● Databases
● Government and Institutional Records
 SOURCES OF DATA
Though the diversity of content, format, and location for data is only increasing
with contributions from technologies such as IoT and the adoption of big data
methodologies, it remains possible to classify most data sources into two broad
categories:-
1. Primary source
2. Secondary source

Primary sources In ICT, primary data is information that is gathered

from an original source for a specific purpose. For example, a loyalty
card scheme collects data about every item a customer has purchased
to calculate their reward points. The purchase data is the primary data

Examples of primary sources:

● Empirical research: Data that has not been collected before, such
as through questionnaires or focus groups.

● Creative works: Such as the original code behind computer

science research.

Secondary source of data:Secondary data refers to second-hand

information. It is not originally collected and rather obtained from
already published or unpublished sources. For example, the address of
a person taken from the telephone directory or the phone number of a
company taken from Just Dial are secondary data.
Examples of secondary source of data :

1.Internet: Data already uploaded on internet is the

basic secondary form of data in ICT.We get a lot of
information from internet based on researches.
(Some time is information is less authentic)

2.Published resources : Published resource are those

sources of data in which data is in printed form.
● E.g: Magzines , journels,research paper published by
government, semi-government and private
organization.
● They are more authentic as they are published after
many peer reviews.The author is related to the field
3.Unpublished resources: This include all the
unpublished research by organizations.
● They include research done by scholars,students
and committes.
● They are prepaered by prepared by private
investigations.
● They are less reliable then published resources
 DATA ANALYSIS

Data analysis is the process of systematically collecting,

cleaning, transforming, describing, modeling, and
interpreting data, generally employing statistical
techniques. Data analysis is an important part of both
scientific research and business, where demand has
grown in recent years for data-driven decision making.

STEPS OF DATA ANALYSIS

1. The extraction step occurs when you identify and copy or

export the desired data from its source, such as by
running a database query to retrieve the desired
records.
2. The transformation step is the process of cleaning the
data so that they fit the analytical need for the data and
the schema of the data warehouse. This may involve
changing formats for certain fields, removing duplicate
records, or renaming fields, among other processes.
3. Finally, the clean data are loaded into the data
warehouse, where they may join vast amounts of
historical data and data from other sources.
 ADVANTAGES OF DATA

1. Informed Decision-Making: Data analysis allows organizations to

make data-driven decisions. By analyzing large volumes of data,
businesses can gain insights into customer behavior, market trends,
and operational performance, enabling them to make informed
decisions and devise effective strategies.
2. Improved Efficiency and Productivity: Data analysis helps
identify areas of inefficiency and process bottlenecks. By analyzing
data, organizations can streamline operations, optimize workflows,
and allocate resources more effectively, leading to increased
efficiency and productivity.
3. Competitive Advantage: Data analysis provides a competitive
edge by uncovering patterns, correlations, and trends that
competitors may overlook. It enables organizations to identify new
market opportunities, improve customer experiences, and develop
innovative products or services based on data-driven insights.
4. Risk Mitigation: Data analysis can identify potential risks and
vulnerabilities within an organization. By examining historical data,
organizations can detect patterns that indicate potential risks, such
as fraud, security breaches, or operational failures. This enables
proactive risk mitigation measures and enhanced security protocols
5. Enhanced Customer Understanding: Analyzing customer data
helps organizations gain a deeper understanding of their target
audience. By analyzing customer behavior, preferences, and
feedback, businesses can personalize marketing efforts, tailor
products to specific needs, and deliver exceptional customer
experiences
 DISADVANTAGES OF DATA

1. Data Quality and Integrity: Data analysis heavily relies on the quality and
integrity of the data being analyzed. Inaccurate or incomplete data can lead
to flawed analysis and incorrect conclusions. Organizations must invest in
data governance and data quality assurance processes to ensure the
accuracy and reliability of their data.
2. Data Privacy and Ethical Concerns: Analyzing large volumes of data
raises concerns about privacy and ethics. Organizations need to handle data
responsibly, ensuring compliance with privacy regulations and protecting
sensitive information. Data analysis must be conducted in an ethical manner,
respecting individual privacy rights and avoiding biases or discriminatory
practices.
3. Complex Data Analysis Techniques: Sophisticated data analysis
techniques, such as machine learning or predictive modeling, often require
specialized skills and expertise. Organizations may face challenges in
acquiring or developing the necessary talent and resources to effectively
implement and interpret complex data analysis methods.
4. Cost and Infrastructure Requirements: Implementing data analysis
initiatives can be costly. Organizations need to invest in data storage,
processing capabilities, analytical tools, and skilled personnel. Small or
resource-constrained organizations may find it challenging to allocate
sufficient resources to establish and maintain robust data analysis
capabilities.
5. Interpretation Challenges: Data analysis provides insights, but the
interpretation and decision-making process are still human-driven. Analyzing
data requires expertise to interpret the results accurately, draw meaningful
conclusions, and translate insights into actionable strategies.
Misinterpretation or misapplication of analysis outcomes can lead to poor
decision-making.