Data processing involves converting raw data into useful information through various steps. It includes collecting data through surveys or experiments, cleaning and organizing the data, analyzing it using statistical tools or software, interpreting the results, and presenting findings visually through tables, charts and graphs. The goal is to gain insights and knowledge from the data that can help inform decisions. Common data analysis types are descriptive, inferential, exploratory, diagnostic and predictive analysis. Data analysis is important for businesses as it allows for better customer targeting, more accurate decision making, reduced costs, and improved problem solving.

1. Data Processing
& Explain each
term in details
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2. Data
Processing

3.  Data & Data Set: The information that you collect from
an experiment, survey, or archival source is referred to
as your data. Most generally, data can be defined as a
list of numbers possessing meaningful relations. A data
set is a representation of data, defining a set of
variables" that are measured on a set of cases.“
 Variable: A variable is any characteristic of an object
that can be represented as a number. The values that the
variable takes will vary when measurements are made on
different objects or at different times.
 Case: Recorded information about an object
we observe a case.

5. Data Processing:
Data processing most often refers to
processes that convert data into information
or knowledge.
Information:
Information is defined as either a meaningful
answer to a query or a meaningful stimulus
that can consider into further queries.

6. Steps of Data Processing:
 3. Objective
 4. Questionnaire
 5. Field Survey
 6. Data Entry
 8. Information's in tables and chart formats

7. Elements of Data
Processing
 Data Coding
 Data Editing/Cleaning
 Data Validation
 Data Classification
 8. Attributes
 9. Class-Intervals
 Data Tabulation
 Statistical Analysis
 Computer graphics
 Data Warehousing
 Data Mining

8. Tabulation
 Tables must be clear and easy to read
 Must have a title which describes the data in the table.
 Columns and rows should be clearly headed. Units should
be displayed in column / row headings only.
 Missing values should be displayed as -, and zeros as 0.
They should be no blanks in a table conveying
experimental results.
 Numbers should be listed neatly below each other and
should be to the same number of decimal places.
 Table should be made logical, clear, accurate and Time
simple as possible.

9. SOME PROBLEMS IN PROCESSING:
a) The Problem Concerning "Don't Know" or
DKINA responses.
b) Use of Percentages

10. Softwares
 SAS
 Quantum
 Quanvert
 Stata
 Strata
 Systat
 Euler
 Matlab
 Minitab
 SPSS

11. Data Processing
using Quantum,
SPSS etc

12. Data File Formats
SPSS: *.sav
Excel: *xls
ASCII: *.asc, *.dat
Programming in Quantum:
Exporting data in ASCII format Programming
according to questionnaire (Drafting Set File)
Outputs in *.csv formats

13. Data Analysis
 Defination -;
 Although many groups, organizations, and experts have different ways of approaching data analysis, most of them
can be distilled into a one-size-fits-all definition. Data analysis is the process of cleaning, changing, and processing
raw data and extracting actionable, relevant information that helps businesses make informed decisions. The
procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often
presented in charts, images, tables, and graphs.
 A simple example of data analysis can be seen whenever we make a decision in our daily lives by evaluating what
has happened in the past or what will happen if we make that decision. Basically, this is the process of analyzing the
past or future and making a decision based on that analysis.
 It’s not uncommon to hear the term “big data” brought up in discussions about data analysis. Data analysis plays a
crucial role in processing big data into useful information.

14. What are types of data analysis
 A half-dozen popular types of data analysis are available today, commonly employed in the
worlds of technology and business. They are:
• Descriptive Analysis
 Descriptive analysis involves summarizing and describing the main features of a dataset. It
focuses on organizing and presenting the data in a meaningful way, often using measures such
as mean, median, mode, and standard deviation. It provides an overview of the data and helps
identify patterns or trends.
• Inferential Analysis
 Inferential analysis aims to make inferences or predictions about a larger population based on
sample data. It involves applying statistical techniques such as hypothesis testing, confidence
intervals, and regression analysis. It helps generalize findings from a sample to a larger
population.

15. • Exploratory Data Analysis (EDA)
 EDA focuses on exploring and understanding the data without preconceived hypotheses. It involves visualizations,
summary statistics, and data profiling techniques to uncover patterns, relationships, and interesting features. It helps
generate hypotheses for further analysis.
• Diagnostic Analysis
 Diagnostic analysis aims to understand the cause-and-effect relationships within the data. It investigates the factors or
variables that contribute to specific outcomes or behaviors. Techniques such as regression analysis, ANOVA (Analysis of
Variance), or correlation analysis are commonly used in diagnostic analysis.
• Predictive Analysis
 Predictive analysis involves using historical data to make predictions or forecasts about future outcomes. It utilizes
statistical modeling techniques, machine learning algorithms, and time series analysis to identify patterns and build
predictive models. It is often used for forecasting sales, predicting customer behavior, or estimating risk.
• Prescriptive Analysis
 Prescriptive analysis goes beyond predictive analysis by recommending actions or decisions based on the predictions. It
combines historical data, optimization algorithms, and business rules to provide actionable insights and optimize
outcomes. It helps in decision-making and resource allocation.

16. Data Analysis Important
 Here is a list of reasons why data analysis is crucial to doing business today.
• Better Customer Targeting: You don’t want to waste your business’s precious time, resources, and money putting together
advertising campaigns targeted at demographic groups that have little to no interest in the goods and services you offer. Data
analysis helps you see where you should be focusing your advertising and marketing efforts.
• You Will Know Your Target Customers Better: Data analysis tracks how well your products and campaigns are performing within
your target demographic. Through data analysis, your business can get a better idea of your target audience’s spending habits,
disposable income, and most likely areas of interest. This data helps businesses set prices, determine the length of ad
campaigns, and even help project the number of goods needed.
• Reduce Operational Costs: Data analysis shows you which areas in your business need more resources and money, and which
areas are not producing and thus should be scaled back or eliminated outright.
• Better Problem-Solving Methods: Informed decisions are more likely to be successful decisions. Data provides businesses with
information. You can see where this progression is leading. Data analysis helps businesses make the right choices and avoid
costly pitfalls.
• You Get More Accurate Data: If you want to make informed decisions, you need data, but there’s more to it. The data in question
must be accurate. Data analysis helps businesses acquire relevant, accurate information, suitable for developing future
marketing strategies, business plans, and realigning the company’s vision or mission.ata Analyst Master’s

17. Data Analysis Process
 Answering the question “what is data analysis” is only the first step. Now we will look at how it’s performed. The process of data analysis, or alternately, data analysis
steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights. The process of data analysis consists
of:
• Data Requirement Gathering
 Ask yourself why you’re doing this analysis, what type of data you want to use, and what data you plan to analyze.
• Data Collection
 Guided by your identified requirements, it’s time to collect the data from your sources. Sources include case studies, surveys, interviews, questionnaires, direct
observation, and focus groups. Make sure to organize the collected data for analysis.
 DNot all of the data you collect will be useful, so it’s time to clean it up. This process is where you remove white spaces, duplicate records, and basic errors. Data
cleaning is mandatory before sending the information on for analysis.
• Data Analysis
 Here is where you use data analysis software and other tools to help you interpret and understand the data and arrive at conclusions. Data analysis
tools include Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase, Redash, and Microsoft Power BI.
• Data Interpretation
 Now that you have your results, you need to interpret them and come up with the best courses of action based on your findings.
• Data Visualization
 Data visualization is a fancy way of saying, “graphically show your information in a way that people can read and understand it.” You can use charts, graphs, maps,
bullet points, or a host of other methods. Visualization helps you derive valuable insights by helping you compare datasets and observe relationships.