1. The document discusses various topics related to data processing and analysis including defining data and information, the steps of data processing, types of data processing, what data analysis is, important types of data analysis methods, and qualitative study design and data analysis approaches.
2. It provides details on data editing, coding, classification, entry, validation, and tabulation as steps in data processing. Common statistical packages, tools, and software for data analysis are also outlined.
3. Qualitative research methods and coding systems are explained as well as qualitative data analysis software packages that can be used.
Top 30 Data Analyst Interview Questions.pdfShaikSikindar1
Data Analytics has emerged has one of the central aspects of business operations. Consequently, the quest to grab professional positions within the Data Analytics domain has assumed unimaginable proportions. So if you too happen to be someone who is desirous of making through a Data Analyst .
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Top 30 Data Analyst Interview Questions.pdfShaikSikindar1
Data Analytics has emerged has one of the central aspects of business operations. Consequently, the quest to grab professional positions within the Data Analytics domain has assumed unimaginable proportions. So if you too happen to be someone who is desirous of making through a Data Analyst .
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
data science course in Hyderabad data science course in Hyderabadakhilamadupativibhin
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
NCV 3 Business Practice Hands-On Support Slide Show - Module 6Future Managers
This slide show complements the learner guide NCV 3 Business Practice Hands-On Training by Nickey Cilliers, published by Future Managers Pty Ltd. For more information visit our website www.futuremanagers.net
tribhuvan University
M.A population Studies
Research methods for population analysis
Data Processing, editing and coding
if any mistakes, suggest me to improve it.
thank you
hope its useful for all :)
Role of Computers in Research, Data Processing, Data AnalysisRKavithamani
The computers are indispensable throughout the research process. The role of computer becomes more important when the research is on a large sample. Data can be stored in computers for immediate use or can be stored in auxiliary memories like floppy discs, compact discs, universal serial buses (pen drives) or memory cards, so that the same can be retrieved later. The computers assist the researcher throughout different phases of research process.
Pubrica has extensive experience in conducting meta-analysis a quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about that body of research.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
data science course in Hyderabad data science course in Hyderabadakhilamadupativibhin
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
NCV 3 Business Practice Hands-On Support Slide Show - Module 6Future Managers
This slide show complements the learner guide NCV 3 Business Practice Hands-On Training by Nickey Cilliers, published by Future Managers Pty Ltd. For more information visit our website www.futuremanagers.net
tribhuvan University
M.A population Studies
Research methods for population analysis
Data Processing, editing and coding
if any mistakes, suggest me to improve it.
thank you
hope its useful for all :)
Role of Computers in Research, Data Processing, Data AnalysisRKavithamani
The computers are indispensable throughout the research process. The role of computer becomes more important when the research is on a large sample. Data can be stored in computers for immediate use or can be stored in auxiliary memories like floppy discs, compact discs, universal serial buses (pen drives) or memory cards, so that the same can be retrieved later. The computers assist the researcher throughout different phases of research process.
Pubrica has extensive experience in conducting meta-analysis a quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about that body of research.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
2. Unit Objectives
At the end of this unit students are
expected to know:
1. What are the different data processing,
analysis and presentation techniques?
2. Understand common statistical packages used
for data analysis
3. What is data
Data/plural of datum is any collection of facts of figures
or raw material to be processed.
▪ Example: Names of students, marks obtained in the
examination, designation of employees, addresses,
quantity, rate, sales figures or anything that is input to
the computer is data.
▪ Even pictures, photographs, drawings, charts and
maps can be treated as data.
▪ Computer processes the data and produces the output
or result
4.
5.
6. Information:
A collection of data which conveys some
meaningful idea is information.
It may provide answers to questions like:
Who
Which
When
Why
What and
How.
7. Data Processing: any operation or set of
operations performed upon data, whether or not
by automatic means, such as collection,
recording, organization, storage, adaptation or
alteration to convert it into useful information.
Observations and recordings are done to obtain data,
while analysis is done to obtain information
Data Processing is the processing of converting data
into useful information.
Data processing system is the activities, equipment
& personnel involved.
8. Steps of data processing
There are 5 steps included in Data
processing:
1. Editing
2. Coding
3. Classification
4. Data Entry
5. Validation
6. Tabulation
9. Editing: editing of data is a process of
examining the collected raw data to detect
errors and omissions and to correct these
when possible.
With regards to stages:
Field Editing
Central Editing
10. Coding: refers to process of assigning numerals
or other symbols to answers so that responses can
be put into a limited number of categories or
classes.
Classification: data having a common
characteristics are placed in one class
In this way the entire data get divided into a number of
groups or classes.
Types:
1. Classification according to attributes
2. Classification according to class intervals
11. Data Entry: after the data has been properly
arranged and coded, it is entered into the
software that performs the eventual cross
tabulation.
Validation: after the cleaning phase, comes the
validation process.
It refers to the process of thoroughly checking the
collected data to ensure optimal quality levels.
All the accumulated data is double checked in order to
ensure that it contains no inconsistencies and is
relevant.
12. Tabulation: is the process of summarizing raw
data and displaying the same in compact form for
further analysis.
Benefits:
1. It conserves soace and reduces explanatory
statement to a minimum
2. It facilitates the process of comparison
3. It facilitates the summation of items and
detection of errors
4. It provides a basis for various statistical
computations
13. Types Of Data Processing:
1. Manual data Processing
2. Electronic Data Processing
3. Real time Processing
4. Batch Processing
14.
15. What Is Data Analysis?
Data analysis is the process of collecting, modeling,
and analyzing data to extract insights that support
decision-making.
Research data analysis is a process used
by researchers for reducing large chunk of data to a
story and interpreting it to derive insights or makes
sense.
Or Data Analysis is the process of systematically
applying statistical and/or logical techniques to
describe and illustrate, condense and recap, and
evaluate data.
16. There are several methods and techniques to
perform analysis depending on the industry
and the aim of the analysis.
All these various methods for data analysis are
largely based on two core areas:
1. Quantitative methods and
2. Qualitative methods in research.
17. Steps in data analysis
1. Data collection and preparation
2. Exploration of data
3. Data analysis techniques/methods
18. Data preparation
1. Collect data
2. Prepare of codebooks
3. Set up structure of data
4. enter data
5. Screen data for errors
Exploration of data
1. Graphs
2. Descriptive stats
19. Why Is Data Analysis Important?
The main purpose of data analysis is to find
meaning in data so that the derived
knowledge can be used to make informed
decisions
By using data analysis you can understand
which channels your customers use to
communicate with you, their demographics,
interests, habits, purchasing behaviors, and
more.
20. 10 Essential Types of Data Analysis
Methods:
1. Cluster analysis
2. Cohort analysis
3. Regression analysis
4. Factor analysis
5. Neural Networks
6. Data Mining
7. Text analysis
8. Monte Carlo simulation.
9. Time series analysis.
10. Sentiment analysis
21. Types Of Data Analysis Methods
1. Descriptive analysis: What happened.
2. Exploratory analysis: How to explore data
relationships.
3. Diagnostic analysis: Why it happened.
4. Predictive analysis: What will happen.
5. Prescriptive analysis: How will it happen.
22.
23. Steps in Data
Analysis
1. Collaborate your needs
2. Establish your questions
3. Data democratization
4. Clean your data
5. Set your KPIs
6. Omit useless data
7. Build a data management
roadmap
8. Integrate technology
9. Answer your questions
10. Visualize your data
11. Interpretation of data
12. Consider a
autonomous technology
13. Build a narrative
14. Share the load
1. Use Data Analysis tools
24.
25. Data analysis tools
In order to perform high-quality data analysis, it
is fundamental to use tools and software that
will ensure the best results.
As the analysis industry grows, so does the offer
for services and features that you can exploit.
The four fundamental categories of data
analysis tools for your purposes.
26. Statistical analysis tools:
These tools are usually designed for data
scientists, statisticians, market researchers, and
mathematicians, as they allow them to perform
complex statistical analyses with methods like
regression analysis, predictive analysis, and
statistical modeling.
A good tool to perform this type of analysis is:
R-Studio as it offers a powerful data modeling and
hypothesis testing feature that can cover both academic
and general data analysis.
27. This tool is one of the favorite ones in the analysis
industry, due to its capability for data cleaning,
data reduction, and performing advanced analysis
with several statistical methods.
Another relevant tool to mention is SPSS from
IBM.
The software offers advanced statistical analysis for
users of all skill levels.
SPSS also works as a cloud service that enables you to
perform analysis anywhere.
30. Qualitative study design
Case study: single case (shed light on phenomena among
group, individuals, events or institutions).
Content analysis: systematic collection and objective analysis
of contents
Historical analysis (narratives): systematic collection and
analysis.
Participatory action research: individual or group
participation
Ground theory: beyond existing body of knowledge. theory
development
Phenomenological: individual life experience of events,
perceptions, perspectives, or understanding.
31. Qualitative data analysis approaches
1. Grounded theory analysis:
2. Narrative analysis
3. Discourse analysis: language studies
4. Framework analysis: use matrix (rows and
columns
5. Thematic analysis
32. Coding system (based on study design)
1. Inductive: coding during analysis process
Qualitative research methods are also described as
inductive, in the sense that a researcher may construct
theories or hypotheses, explanations, and
conceptualizations from details provided by a
participant.
Embedded in this approach is the perspective that
researchers cannot set aside their experiences,
perceptions, and biases, and thus cannot pretend to be
objective bystanders to the research.
33. 2. Deductive: code listed before analysis start
Replicability and generalizability are not
generally goals of qualitative research