Data analysis (Seminar for MR) (1).pptx

DATA ANALYSIS
PREPARED BY:MARY ANN SHEKINA

GLOSSARY
S.NO TOPICS
1 Introduction
2 Types of data based on structures and formats
3 Techniques of data analysis
4 Why data analysis is important ?
5 Data analysis process
6 Importance of data analysis in research
7 Types of data analysis
8 Tools used for data analysis
9 Advantages of data analysis
10 Disadvantages of data analysis
11 Conclusion

INTRODUCTION
◦ A process used by researchers for reducing data to a story and interpreting it to derive insights. The data
analysis process helps in reducing a large chunk of data into smaller fragments, which makes sense.
◦ Data interpretation is a process representing the application of deductive and inductive logic to the research
and data analysis.
◦ We analyze the data during research work because data analysis tells the most unforeseen yet exciting
stories that were not expected at the time of initiating data analysis. Therefore, rely on the data you have at
hand and enjoy the journey of exploratory research.
◦ Sometimes, data analysis can be a messy , ambiguous, and time-consuming but it is a fascinating and clear
piece of data through which various and number of data is being brought to a structure, order and
meaningful.

TYPES OF DATA BASED ON STRUCTURES AND
FORMATS
◦ Big data: A huge data set that continues to grow at an exponential rate over time. The four fundamental
characteristics of big data are volume, variety, velocity, and variability.
◦ Structured/unstructured data: Structured data is a predefined data model such as a traditional row-column
database. Unstructured data comes in a format that does not fit in rows and columns and can include
videos, photos, audio, text, and more.
◦ Metadata: A form of data that describes and provides information about other data. For example, metadata
for an image can include the author, image type, and date created. Metadata enables users to organize
unstructured data into categories, making it easier to work with.
◦ Real-time data: Data that is presented as soon as it is acquired. This type of data is useful when decisions
require up-to-the-minute information. For example, a stockbroker can use a stock market ticker to track the
most active stocks in real time.
◦ Machine data: This data is produced wholly by machines without human instruction.

TECHNIQUES OF DATA ANALYSIS
◦ Quantitative data analysis : Refers to working with numerical variables including statistics, percentages, calculations, measurements, and
other data as the nature of quantitative data is numerical. Quantitative data analysis techniques typically include working with
algorithms, mathematical analysis tools, and software to manipulate data and uncover insights that reveal the business value.
◦ Typical steps involved in quantitative data analysis: Regression analysis & hypothesis analysis.
◦ Regression analysis: A type of statistical analysis method that determines the relationships between independent and dependent
variables.
◦ For example, an independent variable can be the amount an individual invests in the stock market with the dependent variable thetotal
amount of money an individual will have when they retire.
◦ Two primary types of regression analysis : Simple linear and Multiple linear
◦ Simple linear regression analysis: It consists of a dependent variable and an independent variable. The mathematical representation of
the dependent variable is typically Y, while X represents the independent variable. Example:-A market researcher analyzing the
relationship between their company’s products and customer satisfaction.
◦ Multiple linear regression analysis :Contains various independent variables, resulting in a potentially complex formula for performing a
regression analysis

◦ Hypothesis analysis : A data analysis technique that uses sample data to test a hypothesis and a statistical test method to validate an
assumption and determine if it’s plausible or factual.
◦ Two foundational components of hypothesis analysis : Null hypothesis and alternative hypothesis
◦ Qualitative data analysis: Describes information that is typically non numerical and approach involves working with unique
identifiers, such as labels and properties, and categorical variables, such as statistics, percentages, and measurements. A data
analyst may use firsthand or participant observation approaches, conduct interviews, run focus groups, or review documents and
artifacts in qualitative data analysis.
◦ Two main qualitative data approaches: Deductive and Inductive.
◦ Deductive approach: This analysis method is used by researchers and analysts who already have a theory or a predetermined idea of
the likely input from a sample population. The deductive approach aims to collect data that can methodically and accurately support
a theory or hypothesis.
◦ Inductive approach : In this approach, a researcher or analyst with little insight into the outcome of a sample population collects the
appropriate and proper amount of data about a topic of interest. Then, they investigate the data to look for patterns. The aim is to
develop a theory to explain patterns found in the data.

◦ Two main qualitative data analysis techniques : Content analysis and Discourse analysis.
◦ Content analysis: Content analysis can reveal patterns in recorded communication that
indicate the purpose, messages, and effect of the content. An analyst could identify instances
where the word “employment” appears in social media, news stories, and other media and
correlates with other relevant terms, such as “economy,” “business,” and “Main Street.”
◦ Components of content analysis: Identify data sources, Determine data criteria, Develop
coding for the data and Analyze the results.
◦ Discourse analysis : Helps provide an understanding of the social and cultural context of
verbal and written communication throughout conversations. Discourse analysis aims to
investigate the social context of communication and how people use language to achieve
their aims, such as evoking an emotion, sowing doubt, or building trust . Discourse analysis
helps interpret the true meaning and intent of communication and clarifies misunderstanding
◦ Steps in discourse analysis :Define the research question, Select the content types, Collect the
data and Analyze the content.

WHY DATA ANALYSIS IS IMPORTANT ?
◦ 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 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 quantity 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.

DATA ANALYSIS PROCESS
◦ It involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights.
◦ This process contains number of steps which organizes and makes the data in a typical manner. They are:
◦ Data Requirement Gathering: Be sure why you are analyzing the data which you have collected for the research report
and what type of data analysis you are planning to exhibit it out.
◦ Data Collection : This step involves collecting of data from various number of sources like case studies, surveys,
interviews, questionnaires, direct observation, and focus groups. Organizing the data is important.
◦ Data Cleaning : This step is a mandatory one because all the data's which we collect is not useful for representing it out or
for a research report so some duplicate reports, white blank spaces , etc. will be cleaned off. This step is done before the
information is sent for analyzing process.

◦ 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: 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.
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IMPORTANCE OF DATA ANALYSIS IN RESEARCH
A huge part of a researcher’s job is to sift through data. That is literally the definition of
“research.” However, today’s Information Age routinely produces a tidal wave of data,
enough to overwhelm even the most dedicated researcher. Data analysis, therefore,
plays a key role in distilling this information into a more accurate and relevant form,
making it easier for researchers to do to their job. Data analysis also provides
researchers with a vast selection of different tools, such as descriptive statistics,
inferential analysis, and quantitative analysis. So, to sum it up, data analysis offers
researchers better data and better ways to analyze and study said data.

TYPES OF DATA ANALYSIS
◦ Diagnostic Analysis: The process of using data to determine the causes of trends and correlations between variables. It can be viewed
as a logical next step after using descriptive analytics to identify trends.
◦ Predictive Analysis: The use of data, statistical algorithms and machine learning techniques to identify the likelihood of future
outcomes based on historical data.
◦ Prescriptive Analysis: A form of data analytics that tries to answer "What do we need to do to achieve this?“ It uses machine learning
to help businesses decide a course of action based on a computer program’s predictions.
◦ Statistical Analysis: The collection and interpretation of data in order to uncover patterns and trends. It is a component of data
analytics. Statistical analysis can be used in situations like gathering research interpretations, statistical modeling or designing surveys
and studies.
◦ Descriptive analysis : The type of analysis of data that helps describe, show or summarize data points in a constructive way such that
patterns might emerge that fulfill every condition of the data.
◦ Inferential: Inferential analysis is used to generalize the results obtained from a random (probability) sample back to the population
from which the sample was drawn.
◦ Text Analysis: A methodology that involves understanding language, symbols, and/or pictures present in texts to gain information regarding
how people make sense of and communicate life and life experiences.

ADVANTAGES OF DATA ANALYSIS
◦ It detects and correct the errors from data sets with the help of data cleansing.
◦ It removes duplicate information's from data sets and hence saves large amount of memory space. This decreases cost to
the company.
◦ It helps in displaying relevant advertisements on the online shopping websites based on historic data and purchase
behavior of the users.
◦ It reduces banking risks by identifying probable fraudulent customers based on historic data analysis. This helps institutes
in deciding whether to issue loan or credit cards to the applicants or not.

DISADVANTAGES OF DATA ANALYSIS
◦ Lack of alignment within teams
◦ Lack of commitment and patience
◦ Low quality of data
◦ Privacy concerns
◦ Complexity and bias

CONCLUSION
◦ The conclusion is the essential step in completing the data analysis process. The conclusion gives important
inferences derived from the study and bind them together as a final summary of findings.
◦ Cause and effect: The conclusion should be derived based on cause and effect relations. The cause and effect
among the data variables, classes, samples, and groups provide a final conclusion.
◦ Generalizations: Though generalization should be avoided; certain large samples can be generalized to derive
conclusions. The populations with simple structures, small populations that can find certain general
characteristics among themselves can be generalized.
◦ Data Reporting: All the organized data, along with findings, and results in the visualized form, should be
reported on the paper in the form of a document and following a certain format that is called data report or
research paper/thesis. Final reporting of data in the prescribed format, along with research question,
methodology, and literature review, must be put together as a report in the final step of data analysis and
conclusion.

Data analysis (Seminar for MR) (1).pptx

Data analysis (Seminar for MR) (1).pptx

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Data analysis (Seminar for MR) (1).pptx