This document discusses various methods for collecting primary and secondary data. It describes primary data collection methods such as observation, interviews, questionnaires, and schedules. It provides details on structured vs unstructured observation, participant vs non-participant observation, and types of interviews. It also discusses constructing questionnaires and using secondary data sources.
This document discusses data collection in quantitative studies. It explains that data are facts that provide information about the phenomenon being studied. There are several steps to collecting data quantitatively: identifying data needs like variables to measure or hypotheses to test; selecting appropriate measurement tools; pretesting instruments; developing data collection forms and procedures; implementing a data collection plan including selecting and training personnel; and addressing issues that may arise during the process like maintaining controls. The goal is to gather information consistently and validly to address the research questions.
This document discusses data collection and measurement. It defines different levels of measurement including nominal, ordinal, interval and ratio. It explains the data collection process and questions to consider like what, how, who, where and when to collect data. Common data collection methods are identified like surveys, interviews and physiological measures. Factors to consider when selecting a data collection instrument are discussed like practicality, reliability and validity. The document provides examples to illustrate key concepts.
This document discusses various methods for collecting data, including definitions, types, categories, and sources of data. It describes primary and secondary data and how each are collected. Common data collection methods like questionnaires, interviews, observation, and document analysis are explained along with their advantages and disadvantages. The key points are that there are various ways to collect both quantitative and qualitative data, and the optimal method depends on factors like the research question and available resources. Primary sources involve collecting original data while secondary sources use previously collected data.
This document discusses various methods for collecting data in research studies. It outlines the differences between quantitative and qualitative research methods. Some key methods discussed include interviews, focus groups, observation, questionnaires, and secondary data collection. Interviews can be structured, unstructured, or semi-structured. Focus groups involve a moderator guiding discussion among similar participants. Observation methods include controlled observation, naturalistic observation, and participant observation. Questionnaires can be self-administered or involve personal interviews. Secondary data is existing unpublished or published information from various sources. The document provides guidance on using these different techniques for collecting both primary and secondary data.
Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. The data collection component of research is common to all fields of study including physical and social sciences, humanities, business, etc. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture quality evidence that then translates to rich data analysis and allows the building of a convincing and credible answer to questions that have been posed.
This document discusses primary and secondary data sources. Secondary data is data gathered by someone else for a different purpose than the current project. It can be inexpensive and convenient to obtain, but may lack accuracy or relevance. Internal secondary data sources include accounting and sales information, while external sources include libraries, the internet, vendors and government records. The document also outlines various methods for collecting primary data, including observation, interviews, questionnaires, case studies and more. It provides details on structured versus unstructured interviews and questionnaires.
The document discusses various methods for collecting data in research. It describes primary and secondary data collection. Some key methods covered include observation, interviews, questionnaires, schedules, and surveys. For each method, it provides details on the process, types, advantages, and disadvantages. The goal of the document is to outline different approaches for gathering information needed to conduct research.
This document discusses data collection in quantitative studies. It explains that data are facts that provide information about the phenomenon being studied. There are several steps to collecting data quantitatively: identifying data needs like variables to measure or hypotheses to test; selecting appropriate measurement tools; pretesting instruments; developing data collection forms and procedures; implementing a data collection plan including selecting and training personnel; and addressing issues that may arise during the process like maintaining controls. The goal is to gather information consistently and validly to address the research questions.
This document discusses data collection and measurement. It defines different levels of measurement including nominal, ordinal, interval and ratio. It explains the data collection process and questions to consider like what, how, who, where and when to collect data. Common data collection methods are identified like surveys, interviews and physiological measures. Factors to consider when selecting a data collection instrument are discussed like practicality, reliability and validity. The document provides examples to illustrate key concepts.
This document discusses various methods for collecting data, including definitions, types, categories, and sources of data. It describes primary and secondary data and how each are collected. Common data collection methods like questionnaires, interviews, observation, and document analysis are explained along with their advantages and disadvantages. The key points are that there are various ways to collect both quantitative and qualitative data, and the optimal method depends on factors like the research question and available resources. Primary sources involve collecting original data while secondary sources use previously collected data.
This document discusses various methods for collecting data in research studies. It outlines the differences between quantitative and qualitative research methods. Some key methods discussed include interviews, focus groups, observation, questionnaires, and secondary data collection. Interviews can be structured, unstructured, or semi-structured. Focus groups involve a moderator guiding discussion among similar participants. Observation methods include controlled observation, naturalistic observation, and participant observation. Questionnaires can be self-administered or involve personal interviews. Secondary data is existing unpublished or published information from various sources. The document provides guidance on using these different techniques for collecting both primary and secondary data.
Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. The data collection component of research is common to all fields of study including physical and social sciences, humanities, business, etc. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture quality evidence that then translates to rich data analysis and allows the building of a convincing and credible answer to questions that have been posed.
This document discusses primary and secondary data sources. Secondary data is data gathered by someone else for a different purpose than the current project. It can be inexpensive and convenient to obtain, but may lack accuracy or relevance. Internal secondary data sources include accounting and sales information, while external sources include libraries, the internet, vendors and government records. The document also outlines various methods for collecting primary data, including observation, interviews, questionnaires, case studies and more. It provides details on structured versus unstructured interviews and questionnaires.
The document discusses various methods for collecting data in research. It describes primary and secondary data collection. Some key methods covered include observation, interviews, questionnaires, schedules, and surveys. For each method, it provides details on the process, types, advantages, and disadvantages. The goal of the document is to outline different approaches for gathering information needed to conduct research.
This document discusses various methods of data collection. It begins by defining key terms like data, types of data, and sources of data. The main methods of data collection discussed are observation, questionnaires, and interviews. Observation can be structured, unstructured, or participatory. Questionnaires contain open-ended, closed-ended, rating, and ranking questions. Interviews are conducted either in-person or over the phone. The document outlines advantages and disadvantages of each method.
data collection is just systematic way approach for gather and measure information form variety source for the aim of get complete and accurate of an area that interested
This document discusses data collection procedures and determining what constitutes data in a study. It covers several key points:
1) The variables of study and their operational definitions determine what data is collected. Data collection methods depend on whether the research is qualitative or quantitative.
2) Qualitative research may use interviews, observations, and open questionnaires to collect in-depth data in a natural context. Quantitative research uses more structured observations and very structured interviews to collect brief, uniform data.
3) Common data collection methods include observations, interviews, questionnaires, tests, and elicitation techniques like judgments, translations, and recalls. The appropriate method depends on the research questions and goals.
This document discusses various tools that can be used for data gathering in qualitative and quantitative research. It begins by stating the objectives of understanding what data gathering is, being able to select appropriate tools, and choosing tools for specific research topics. It then defines data and data gathering. The rest of the document discusses different tools for collecting qualitative data, like interviews and focus groups, and quantitative data, like questionnaires and tests. For each tool, it provides details on what it is, how it is used, and advantages and disadvantages. The goal is to help participants in selecting the right data collection methods for their research needs.
Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
This document discusses various methods of primary and secondary data collection. It describes key advantages and disadvantages of primary data collection methods like surveys and interviews which involve collecting data directly from subjects. Secondary data collection involves using existing data collected for other purposes which is often easier but relies on the accuracy of the original data collection. The document also outlines different types of interviews like unstructured, semi-structured, and structured interviews and the steps involved in conducting structured interviews.
This document outlines principles of data collection and measurement for research. It discusses different levels of measurement including nominal, ordinal, interval and ratio. It also covers selecting appropriate data collection methods and instruments, ensuring validity and reliability of instruments, and criteria for evaluating data collection procedures. Common data collection methods are described such as questionnaires, interviews and observation.
This document discusses data collection methods for research. It states that data is essential for making business decisions and can come from records, observations, measurements or interviews. It then describes surveys as a method to gather information through questionnaires administered to a sample of the population. The document outlines different types of surveys, including census surveys which collect all population data and sample surveys which collect a representative sample. It also describes various personal interview techniques used in surveys, such as door-to-door interviews, mall intercepts, and computer-assisted interviews.
This document provides an overview of research methodologies in architecture. It discusses qualitative and quantitative research, including common approaches like interviews, surveys, observation, and case studies. Qualitative research focuses on collecting and analyzing non-numerical data through methods like ethnography and narrative research, while quantitative research uses statistical analysis and sampling. The document also outlines different types of research like descriptive, exploratory, experimental, and longitudinal research as well as key concepts in research methodology like variables, hypotheses, and validity.
The document discusses various methods of data collection for research purposes. It describes primary and secondary data, as well as qualitative and quantitative data. The main methods of primary data collection discussed are questionnaires, interviews, focus groups, observation, surveys, case studies, and diaries. Some key issues to consider for data collection are promoting aims of research, cooperation among researchers, accountability, and ethical norms around honesty, objectivity, and respecting intellectual property and human subjects.
This document discusses various methods of data collection in research. It describes six main methods: tests, questionnaires, interviews, focus groups, observation, and existing data. It provides details on questionnaires and interviews, including strengths and weaknesses of each. For questionnaires, it outlines 15 principles of construction such as matching items to objectives and using clear, concise language. For interviews it distinguishes between quantitative, standardized interviews and qualitative, open-ended interviews. The document emphasizes the importance of mixing methods to leverage their complementary strengths.
The document discusses methods of collecting data for research. It describes primary data collection methods like observation, interviewing, surveys, and experimentation which involve directly collecting unpublished data from original sources. Secondary data collection methods involve using already published data for research purposes and include sources like census reports, company annual reports, and reports from government departments and international organizations. Both primary and secondary data have advantages and limitations for research. The choice of data collection method depends on the specific needs and conditions of the study.
This document discusses various methods for collecting primary data, including observation, interviews, questionnaires, and schedules. It outlines the key aspects of primary data collection such as structured vs. unstructured approaches, participant vs. non-participant observation, and open-ended vs. closed questions. Primary data collection allows researchers to gather targeted information directly from respondents but requires more time and resources than using secondary data.
This document discusses various tools and techniques used for data collection in research. It defines research tools as instruments used by researchers to measure what they intend to study. Some major tools discussed are questionnaires, checklists, rating scales, attitude scales, observation, interviews, psychological tests, and sociometry. The document provides examples and purposes of each tool while emphasizing the importance of selecting reliable and valid tools that align with the research questions.
This document discusses various methods for collecting data, including interviews, questionnaires, observation, and record analysis. It provides details on each method, such as advantages and disadvantages. For interviews, it explains the different types of interview structures and how to effectively conduct interviews. For questionnaires, it outlines best practices for developing questions, administration, and improving response rates. The document also covers analyzing records, developing tools and techniques for data collection, and selecting appropriate methods based on the nature of the study.
This document discusses various methods and instruments for collecting data in research studies. It begins by defining data and explaining why data collection is important. It then covers primary and secondary sources of data, as well as internal and external sources. The main methods of collecting primary data discussed are direct personal investigation through interviews, indirect oral investigation, case studies, measurements, and observation. Secondary data sources include published and unpublished sources. The document also discusses self-reported data collection methods like surveys, interviews, and questionnaires. Other methods covered include document review, focus groups, and observation. Mixed methods are also briefly discussed.
The document discusses various tools and methods used for data collection in research. It describes primary and secondary sources of data and some common methods for collecting data like interviews, questionnaires, observation, and various scales. Specific tools are discussed for each method - for interviews these include interview schedules and opinionnaires, questionnaires use tools like attitude scales and Likert scales, and observation uses tools like rating scales and checklists. Guidelines for developing questionnaires and uses of different types of scales are also provided.
This document discusses various methods for collecting primary and secondary data. It describes primary data collection methods like observation, interviews (structured and unstructured), questionnaires, and surveys. It also discusses secondary data sources and factors to consider when using secondary data like reliability, suitability, and adequacy. The key methods covered include observation, personal interviews, telephone interviews, questionnaires, and surveys. It provides details on the advantages and disadvantages of each method.
This document discusses various methods of data collection. It begins by defining key terms like data, types of data, and sources of data. The main methods of data collection discussed are observation, questionnaires, and interviews. Observation can be structured, unstructured, or participatory. Questionnaires contain open-ended, closed-ended, rating, and ranking questions. Interviews are conducted either in-person or over the phone. The document outlines advantages and disadvantages of each method.
data collection is just systematic way approach for gather and measure information form variety source for the aim of get complete and accurate of an area that interested
This document discusses data collection procedures and determining what constitutes data in a study. It covers several key points:
1) The variables of study and their operational definitions determine what data is collected. Data collection methods depend on whether the research is qualitative or quantitative.
2) Qualitative research may use interviews, observations, and open questionnaires to collect in-depth data in a natural context. Quantitative research uses more structured observations and very structured interviews to collect brief, uniform data.
3) Common data collection methods include observations, interviews, questionnaires, tests, and elicitation techniques like judgments, translations, and recalls. The appropriate method depends on the research questions and goals.
This document discusses various tools that can be used for data gathering in qualitative and quantitative research. It begins by stating the objectives of understanding what data gathering is, being able to select appropriate tools, and choosing tools for specific research topics. It then defines data and data gathering. The rest of the document discusses different tools for collecting qualitative data, like interviews and focus groups, and quantitative data, like questionnaires and tests. For each tool, it provides details on what it is, how it is used, and advantages and disadvantages. The goal is to help participants in selecting the right data collection methods for their research needs.
Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
This document discusses various methods of primary and secondary data collection. It describes key advantages and disadvantages of primary data collection methods like surveys and interviews which involve collecting data directly from subjects. Secondary data collection involves using existing data collected for other purposes which is often easier but relies on the accuracy of the original data collection. The document also outlines different types of interviews like unstructured, semi-structured, and structured interviews and the steps involved in conducting structured interviews.
This document outlines principles of data collection and measurement for research. It discusses different levels of measurement including nominal, ordinal, interval and ratio. It also covers selecting appropriate data collection methods and instruments, ensuring validity and reliability of instruments, and criteria for evaluating data collection procedures. Common data collection methods are described such as questionnaires, interviews and observation.
This document discusses data collection methods for research. It states that data is essential for making business decisions and can come from records, observations, measurements or interviews. It then describes surveys as a method to gather information through questionnaires administered to a sample of the population. The document outlines different types of surveys, including census surveys which collect all population data and sample surveys which collect a representative sample. It also describes various personal interview techniques used in surveys, such as door-to-door interviews, mall intercepts, and computer-assisted interviews.
This document provides an overview of research methodologies in architecture. It discusses qualitative and quantitative research, including common approaches like interviews, surveys, observation, and case studies. Qualitative research focuses on collecting and analyzing non-numerical data through methods like ethnography and narrative research, while quantitative research uses statistical analysis and sampling. The document also outlines different types of research like descriptive, exploratory, experimental, and longitudinal research as well as key concepts in research methodology like variables, hypotheses, and validity.
The document discusses various methods of data collection for research purposes. It describes primary and secondary data, as well as qualitative and quantitative data. The main methods of primary data collection discussed are questionnaires, interviews, focus groups, observation, surveys, case studies, and diaries. Some key issues to consider for data collection are promoting aims of research, cooperation among researchers, accountability, and ethical norms around honesty, objectivity, and respecting intellectual property and human subjects.
This document discusses various methods of data collection in research. It describes six main methods: tests, questionnaires, interviews, focus groups, observation, and existing data. It provides details on questionnaires and interviews, including strengths and weaknesses of each. For questionnaires, it outlines 15 principles of construction such as matching items to objectives and using clear, concise language. For interviews it distinguishes between quantitative, standardized interviews and qualitative, open-ended interviews. The document emphasizes the importance of mixing methods to leverage their complementary strengths.
The document discusses methods of collecting data for research. It describes primary data collection methods like observation, interviewing, surveys, and experimentation which involve directly collecting unpublished data from original sources. Secondary data collection methods involve using already published data for research purposes and include sources like census reports, company annual reports, and reports from government departments and international organizations. Both primary and secondary data have advantages and limitations for research. The choice of data collection method depends on the specific needs and conditions of the study.
This document discusses various methods for collecting primary data, including observation, interviews, questionnaires, and schedules. It outlines the key aspects of primary data collection such as structured vs. unstructured approaches, participant vs. non-participant observation, and open-ended vs. closed questions. Primary data collection allows researchers to gather targeted information directly from respondents but requires more time and resources than using secondary data.
This document discusses various tools and techniques used for data collection in research. It defines research tools as instruments used by researchers to measure what they intend to study. Some major tools discussed are questionnaires, checklists, rating scales, attitude scales, observation, interviews, psychological tests, and sociometry. The document provides examples and purposes of each tool while emphasizing the importance of selecting reliable and valid tools that align with the research questions.
This document discusses various methods for collecting data, including interviews, questionnaires, observation, and record analysis. It provides details on each method, such as advantages and disadvantages. For interviews, it explains the different types of interview structures and how to effectively conduct interviews. For questionnaires, it outlines best practices for developing questions, administration, and improving response rates. The document also covers analyzing records, developing tools and techniques for data collection, and selecting appropriate methods based on the nature of the study.
This document discusses various methods and instruments for collecting data in research studies. It begins by defining data and explaining why data collection is important. It then covers primary and secondary sources of data, as well as internal and external sources. The main methods of collecting primary data discussed are direct personal investigation through interviews, indirect oral investigation, case studies, measurements, and observation. Secondary data sources include published and unpublished sources. The document also discusses self-reported data collection methods like surveys, interviews, and questionnaires. Other methods covered include document review, focus groups, and observation. Mixed methods are also briefly discussed.
The document discusses various tools and methods used for data collection in research. It describes primary and secondary sources of data and some common methods for collecting data like interviews, questionnaires, observation, and various scales. Specific tools are discussed for each method - for interviews these include interview schedules and opinionnaires, questionnaires use tools like attitude scales and Likert scales, and observation uses tools like rating scales and checklists. Guidelines for developing questionnaires and uses of different types of scales are also provided.
This document discusses various methods for collecting primary and secondary data. It describes primary data collection methods like observation, interviews (structured and unstructured), questionnaires, and surveys. It also discusses secondary data sources and factors to consider when using secondary data like reliability, suitability, and adequacy. The key methods covered include observation, personal interviews, telephone interviews, questionnaires, and surveys. It provides details on the advantages and disadvantages of each method.
This document discusses various methods for collecting data, both primary and secondary. It defines data as units of information that are measured, collected, analyzed and used for data visualizations. There are two main types of data collection methods discussed:
Primary methods involve directly collecting original data and include observation, surveys, interviews and questionnaires. Observation allows collecting large quantities of data in an inexpensive way but requires extensive training. Surveys can be conducted in-person or online and collect standardized information from a sample. Interviews are conducted one-on-one and allow collecting more in-depth information.
Secondary methods involve using existing data collected by others. Common secondary sources include publications, reports, and data available online. While cheaper and faster
This document discusses different methods for collecting data, both primary and secondary. It describes primary data collection methods like observation, surveys, interviews, questionnaires, and schedules. It provides details on how to conduct each method effectively and their advantages and disadvantages. Some key secondary data sources are also outlined such as internal organization records, external publications, reports and internet sources. When using secondary data, factors like reliability, suitability and adequacy must be considered. The selection of the appropriate data collection method depends on the nature, scope, budget and time constraints of the research.
CH-4 Constructing an Instrument for Data Collection.pptxjemalmohamed4
This chapter discusses ethical considerations and methods for collecting data. It covers issues related to participants, researchers, and sponsoring organizations. The two major approaches to gathering information are through primary and secondary sources. Primary data is collected directly for the research purpose while secondary data comes from existing sources. Common primary collection methods include observation, interviews, and questionnaires. Observation can be participant or non-participant. Interviews are structured or unstructured. Questionnaires are administered via mail, in groups, or in public places. Secondary sources include government publications, organizations, earlier research, and media.
Methods of data collection (research methodology)Muhammed Konari
This document discusses different methods for collecting primary data, including observation, interviews, questionnaires, and schedules. It provides details on each method:
Observation methods involve systematically observing participants and recording data. Interviews can be structured or unstructured, and involve an interviewer asking respondents questions. Questionnaires are printed forms sent to respondents to complete on their own, while schedules are similar forms that an enumerator completes by interviewing respondents. Each method has advantages like producing large datasets, but also disadvantages such as being time-consuming or open to bias.
Methods of data collection (research methodology)Muhammed Konari
Included all types of data collection.Includes primary data collection and secondary data collection. Described each and every classification of Data collections which are included in KTU Kerala.
This document discusses different methods for collecting primary data, including observation, interviews, questionnaires, and schedules. It provides details on each method such as the steps involved, types or classifications, advantages, and disadvantages. The key methods covered are observation, where a researcher directly observes participants; interviews, which involve asking participants questions; questionnaires, which are forms mailed to participants to complete; and schedules, where an enumerator asks participants questions and records responses, similar to interviews.
There are various methods for collecting primary and secondary data. Primary data collection methods include observation, interviews, questionnaires, and schedules. Secondary data refers to previously collected data that is analyzed and available for use in other studies. Factors to consider when selecting a data collection method include the nature, scope, and objective of the research, available funds and time, and required precision.
methods of data collection research methodology.pptxYashwanth Rm
The document discusses various methods for collecting primary data in research, including observation, interviews, questionnaires, and schedules. It provides details on how to conduct each method effectively and compares their advantages and disadvantages. The key methods covered are observation, which collects data through direct observation in the field; interviews, which involve oral questioning; questionnaires, which are printed forms sent to respondents; and schedules, which are similar to questionnaires but involve an enumerator administering the questions.
methods of data collection research methodology.pdfYashwanth Rm
The document discusses various methods for collecting primary data in research, including observation, interviews, questionnaires, and schedules. It provides details on how to conduct each method effectively and compares their advantages and disadvantages. The key methods covered are observation, where a researcher directly watches subjects; interviews, conducted in-person or over the phone; questionnaires, which are distributed to respondents; and schedules, which involve an enumerator asking respondents questions from a structured form.
Some common data collection methods include surveys, interviews, observations, focus groups, experiments, and secondary data analysis. The data collected ...
UNIT - III - Data Collection - PPT.pptxJeyasunitha
This document discusses various methods of primary data collection. It describes surveys, observation, experiments, and interviews as common methods. It provides details on constructing questionnaires and validating them through pilot studies. The document also covers sampling techniques, including determining optimal sample size and different sampling methods like probability and non-probability sampling.
The document discusses different methods of collecting primary and secondary data. It describes primary data collection methods such as observation, interviews using questionnaires/schedules, and surveys. It provides details on structured vs unstructured observation, participant vs non-participant observation, and structured vs unstructured interviews. It also discusses advantages and limitations of interviews and questionnaires. Secondary data collection involves obtaining published data from various sources such as government publications, books, reports, and public records. When using secondary data, the researcher must evaluate the reliability, suitability, and adequacy of the data.
This document discusses various methods for collecting data, including primary and secondary data collection. It describes primary data collection methods such as experiments, surveys, observation, and interviews. Specifically, it outlines structured and unstructured observation, as well as participant and non-participant observation. It also discusses personal interviews, questionnaires/schedules, and their advantages and disadvantages. Secondary data collection involves using existing data from government publications, organizations, and other sources. When using secondary data, the researcher must evaluate its reliability, suitability, and adequacy for the research purpose.
The document discusses various methods for collecting primary and secondary data. It describes primary data as being originally collected for the research purpose, while secondary data has already been collected by others. It then provides details on collecting primary data through methods like observation, interviews, questionnaires, and schedules. For secondary data, it notes published and unpublished sources and the need for researchers to carefully evaluate the suitability and reliability of secondary data.
TOOLS AND METHODS OF DATA COLLECTION(Nursing Research & Statistics)virengeeta
This document discusses data collection methods in research. It defines key terms like data, tools, techniques, and methods of data collection. It describes different types of interviews like structured, unstructured, semi-structured, in-depth, and focused group interviews. Factors that influence the selection of data collection methods are described, such as the nature of the phenomenon under study, type of research subjects, purpose of the study, and available resources.
TOOLS AND METHODS OF DATA COLLECTION(Nursing Research & Statistics)virengeeta
This document discusses data collection methods in research. It defines key terms like data, tools, techniques, and methods of data collection. It describes different types of interviews like structured, unstructured, semi-structured, in-depth, and focused group interviews. Factors that influence the selection of data collection methods are described, such as the nature of the phenomenon under study, type of research subjects, purpose of the study, and available resources.
This document discusses various methods for collecting primary data, including observation, interviews, questionnaires, and schedules. It provides details on how to conduct structured and unstructured observation, as well as disguised, undisguised, controlled, and uncontrolled observation. For interviews, it outlines personal and telephone interviews, and structured, semi-structured, and unstructured interview types. It also discusses how to construct questionnaires and the advantages and disadvantages of questionnaires and schedules. Secondary data collection and steps for data analysis like editing, coding, data entry, validation, and tabulation are also covered.
This document discusses various methods of data collection that researchers use in studies, including observation, interviews, questionnaires, and archival data. It provides details on the different types of observation (controlled, participant), interviews (structured, semi-structured, unstructured), and considerations for each method. The document also outlines questionnaires as a method and considerations like response rates. Overall, the document serves as an overview of common data collection methods, their uses, and factors to consider like reliability, validity, and biases.
Similar to L 13 (17-05-21) data collection methodology (20)
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আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
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How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
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How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
2. There are several methods of
collecting primary data:
--- particularly in surveys and
descriptive researches.
In descriptive research, we obtain
primary data either through observation
or through direct communication with
respondents in one form or another or
through personal interviews.
• These are already available i.e. they
refer to the data which have already
been collected and analyzed by
someone else.
• Secondary data may either be published
or unpublished data. Researcher must
be very careful in using secondary data,
because the data available may be
sometimes unsuitable.
COLLECTION OF
SECONDARY
DATA
COLLECTION
OF PRIMARY
DATA
3. Methods of Data Collection
Primary Data
1) OBSERVATION METHOD :
Observation method is a method
under which data from the field is
collected with the help of
observation by the observer or by
personally going to the field.
--- In the words of P.V. Young,
“Observation may be defined as
systematic viewing, coupled with
consideration of seen phenomenon.”
TYPES OF OBSERVATION
Structured and Unstructured
Observation
When observation is done by
characterizing style of recording
the observed information,
standardized conditions of
observation , definition of the units
to be observed , selection of
pertinent data of observation then it
is structured observation
When observation is done without
any thought before observation
then it is unstructured observation
3
4. Methods of Data Collection
Non Participant Observation
When observer is observing people without giving any information to
them then it is non participant observation
4
Participant & Non Participant Observation
When the Observer is member of the group which he is observing then it is
Participant Observation
In participant observation Researcher can record natural behavior of group
Researcher can verify the truth of statements given by informants in the context
of questionnaire
Difficult to collect information obtaining through this method but in this way,
researcher may loose objectivity of research due emotional feelings.
5. Methods of Data Collection
5
Controlled & Uncontrolled Observation
When the observation takes place in natural
condition i.e. uncontrolled observation.
It is done to get spontaneous picture of life and
persons
When observation takes place according to definite
pre arranged plans , with experimental procedure
then it is controlled observation generally done in
laboratory under controlled condition.
6. INTERVIEW METHOD•
This method of collecting data involves presentation or oral-verbal stimuli and
reply in terms of oral-verbal responses.
Interview Method This is Oral Verbal communication .
Where interviewer asks questions( which are aimed to get information
required for study ) to respondent There are different type of interviews as
follows :
PERSONAL INTERVIEWS : The interviewer asks questions generally in a
face to face contact to the other person or persons.
6
Methods of Data Collection
7. 7
Methods of Data Collection
Characteristics of Personal Interview
Structured Interview
Flexibility in asking questions
Predetermined questions
Standardized techniques
No Predetermined questions recording
No Standardized techniques of procedure laid down i.e. asking recording
questions in form & order prescribed
Interviewer has freedom to ask
Time required for such interview omit.
Non structured manner interview
add questions if any
Ask questions without following sequence
Not necessary of skill or specific knowledge, Deep knowledge & skill
Analysis of data becomes easier required Bcoz information is collected
Analysis of data is difficult in prescribed manner
8. TELEPHONIC INTERVIEWS
Contacting samples on telephone
Uncommon method may be used in developed regions
MERITS• Flexible compare to mailing method• Faster than other
methods• Cheaper than personal interview method• Callbacks are
simple and economical also• High response than mailing method.•
when it is not possible to contact the respondent directly, then
interview is conducted through – Telephone.
8
Methods of Data Collection
Replies can be recorded without embarrassment to respondents•
Interviewer can explain requirements more easily• No field staff is
required• Wider distribution of sample is possible
DEMERITS • Little time is given to respondents • Survey is
restricted to respondents who have telephones • Not suitable for
intensive survey where comprehensive answers are required •
Bias information may be more • Very difficult to make
questionnaire because it should short and to the point
9. Structured interviews –
in this case, a set of pre- decided questions are there.
Unstructured interviews –
in this case, we don’t follow a system of pre-determined
questions.
Focused interviews -- attention is focused on the
given experience of the respondent and its possible
effects.
Clinical interviews -- concerned with broad
underlying feelings or motivations or with the course of
individual’s life experience, rather than with the effects of
the specific experience, as in the case of focused
interview.
9
Methods of Data Collection
10. Group interviews --
A group of 6 to 8 individuals is interviewed.
Qualitative and quantitative interviews --
divided on the basis of subject matter i.e. whether
qualitative or quantitative.
Individual interviews -- interviewer meets a single
person and interviews him.
Selection interviews -- done for the selection of
people for certain jobs.
Depth interviews -- it deliberately aims to elicit
unconscious as well as other types of material relating
especially to personality dynamics and motivations.
10
Methods of Data Collection
11. Methods of Data Collection
11
QUESTIONNAIRE METHOD
This method of data collection is quite popular, particularly in case
of big enquiries.
The questionnaire is mailed to respondents who are expected to
read and understand the questions and write down the reply in the
space meant for the purpose in the questionnaire itself.
The respondents have to answer the questions on their own.
Questionnaire is sent to persons with request to answer the
questions and return the questionnaire
Questions are printed in definite order, mailed to samples who are
expected to read that questions understand the questions and
write the answers in provided space.
12. Methods of Data Collection
12
QUESTIONNAIRE METHOD
Merits of Questionnaire – It is Low cost even the geographical area is
large to cover
Answers are in respondents word so free from bias
Adequate time to think for answers
Non approachable respondents may be conveniently contacted
Large samples can be used so results are more reliable
Demerits of Questionnaire -- Low rate of return of duly filled
questionnaire
Can be used when respondent is educated and co operative
It is inflexible Omission of some questions
Difficult to know the expected respondent have filled the form or it is
filled by some one else
Slowest method of data collection
13. Methods of Data Collection
13
Essentials of Good Questionnaire —
It Should Short & simple Questions should arranged in logical
sequence (From Easy to difficult one)
Technical terms should avoided Some control questions which
indicate reliability of the respondent ( To Know consumption first
expenditure and then weight or qty of that material)
Questions affecting the sentiments of the respondents should
avoided
Adequate space for answers should be provided in questionnaire
Provision for uncertainty (do not know, No preference)
Directions regarding the filling of questionnaire should be given
Physical Appearance - - Quality of paper, color
14. Methods of Data Collection
14
HOW TO CONSTRUCT A QUESTIONNAIRE
Researcher should note the following with regard to these three
main aspects of a questionnaire:
• General form
• Question Sequence
• Determine the type the Questions –
• A) Direct Question
• B) Indirect Question
• C) Open Form Questionnaire
• D) Closed Form Questionnaire
• E) Dichotomous Questions
• F) Multiple Choice Questions (MCQ)
15. Methods of Data Collection
15
Secondary Data Sources of data --
Publications of Central, state , local government• Technical and
trade journals• Books, Magazines, Newspaper• Reports &
publications of industry ,bank, stock exchange• Reports by
research scholars, Universities, economist• Public Records.
Factors to be considered before using secondary data --
Reliability of data – Who, when , which methods, at what time etc.
Suitability of data – Object ,scope, and nature of original inquiry
should be studied, as if the study was with different objective then
that data is not suitable for current study
Adequacy of data -- Level of accuracy, • Area differences then
data is not adequate for study
16. Methods of Data Collection
16
Schedules --
Very similar to Questionnaire method
The main difference is that a schedule is filled by
the enumerator who is specially appointed for the
purpose.
Enumerator goes to the respondents, asks them
the questions from the Questionnaire in the order
listed, and records the responses in the space
provided.
Enumerator must be trained in administering the
schedule.
17. Methods of Data Collection
17
Questionnaire Vs. Schedule Questionnaire –
Q generally send to through mail and no further
assistance from sender.
Q is cheaper method.
Non response is high
Incomplete and wrong information is more.
Depends on the quality of questionnaire Schedule
Schedule is filled by the enumerator or research
worker.
Costly requires field workers.
Non response is low
Depends on Honesty of the enumerator.
Relatively more correct and complete
18. Methods of Data Collection
Rating Scale –
Ratting is term applied to express
opinion or judgment regarding
some situation, object or
character.
Opinions are usually expressed on
a scale of values; rating techniques
are devices by which such
judgments may be quantified.
“Rating is an essence and direct
observation.” -- Ruth Strong
“A rating scale ascertains the
degree, intensity and frequency of
a variable.” -- Von Dallen
Rating techniques are more commonly used in
scaling traits and attributes.
A rating method is a method by which one
systematizes, the expression of opinion
concerning a trait.
The rating is done by parents, teachers, a
board of interviewers and judges and even by
the self as well.
The special feature of rating scale is that the
attitudes are evaluated not on the basis of the
opinions of the subjects but on the basis of
the opinions and judgments of the
experimenter himself.
In rating scale data are collected by; Verbal
behavior, facial expression, personal
documents, clinical type interview, projective
techniques and immediate experiences as
emotions, thoughts and perceptions.
18
19. DATA ANALYSIS
Kaul defines data analysis as, ”Studying the
organized material in order to discover inherent
facts. The data are studied from as many angles as
possible to explore the new facts.”
20. DATA ANALYSIS
Data analysis embraces a whole range of activities of both the qualitative and
quantitative type. It is usual tendency in behavioral research that much use of quantative
analysis is made and statistical methods and techniques are employed.
The following are the main purposes of data analysis:
(i) Description: It involves a set of activities that are as essential first step in the development of
most fields. A researcher must be able to identify a topic about which much was not known;
he must be able to convince others about its importance and must be able to collect data.
(ii) Construction of Measurement Scale: The researcher should construct a measurement scale.
All numbers generated by measuring instruments can be placed into one of four categories:
Nominal, Ordinal, Interval, and Ratio Scale.
(iii) Generating empirical relationships: Another purpose of analysis of data is identification of
regularities and relationships among data. The researcher has no clear idea about the
relationship which will be found from the collected data. If the data were available in details it
will be easier to determine the relationship.
(iv) Explanation and prediction: Generally knowledge and research are equated with the
identification of causal relationships and all research activities are directed to it. But in many
fields the research has not been developed to the level where causal explanation is possible
or valid predictions can be made.
20
21. Types of Data Analysis:
Techniques and Methods
There are several types of Data Analysis techniques that exist based on
business and technology. However, the major Data Analysis methods are:
•Text Analysis
•Statistical Analysis
•Diagnostic Analysis
•Predictive Analysis
•Prescriptive Analysis
22. DATA ANALYSIS
• Text Analysis
Text Analysis is also referred to as Data Mining. It is one of the methods of data analysis to discover
data sets using databases or data mining tools. It used to transform raw data into business
• Statistical Analysis
Statistical Analysis shows "What happen?" by using past data in the form of dashboards. Statistical
Analysis includes collection, Analysis, interpretation, presentation, and modeling of data. It
analyses a set of data or a sample of data. Two types- Descriptive and Inferential Analysis
• Diagnostic Analysis
Diagnostic Analysis shows "Why did it happen?" by finding the cause from the insight found in
Statistical Analysis. This Analysis is useful to identify behavior patterns of data. If a new problem
arrives in your business process, then you can look into this Analysis to find similar patterns of
that problem. And it may have chances to use similar prescriptions for the new problems.
• Predictive Analysis
Predictive Analysis shows "what is likely to happen" by using previous data. The simplest data
analysis example is like if last year I bought two dresses based on my savings and if this year my
salary is increasing double then I can buy four dresses. But of course it's not easy like this because
you have to think about other circumstances like chances of prices of clothes is increased this year
or maybe instead of dresses you want to buy a new bike, or you need to buy a house!
• Prescriptive Analysis
Prescriptive Analysis combines the insight from all previous Analysis to determine which action to
take in a current problem or decision. Most data-driven companies are utilizing Prescriptive
Analysis because predictive and descriptive Analysis are not enough to improve data performance.
Based on current situations and problems, they analyze the data and make decisions.
22
23. The Data Analysis Process is nothing but gathering information by using a
proper application or tool which allows you to explore the data and find a
pattern in it. Based on that information and data, you can make decisions, or
you can get ultimate conclusions.
DATA ANALYSIS
PROCESS
24. Data Analysis consists of the following phases –
Data Requirement Gathering
Data Collection
DataCleaning
DataAnalysis
Data Interpretation
DataVisualization
First of all, you have to
think about why do you
want to do this data
analysis? All you need to
find out the purpose or aim
of doing the Analysis of
data. You have to decide
which type of data analysis
you wanted to do! In this
phase, you have to decide
what to analyze and how
to measure it, you have to
understand why you are
investigating and what
measures you have to use
to do this Analysis.
After requirement
gathering, you will get a
clear idea about what things
you have to measure and
what should be your
findings. Now it's time to
collect your data based on
requirements. Once you
collect your data, remember
that the collected data must
be processed or organized
for Analysis. As you
collected data from various
sources, you must have to
keep a log with a collection
date and source of the data.
Now whatever data is
collected may not be
useful or irrelevant to your
aim of Analysis, hence it
should be cleaned. The
data which is collected
may contain duplicate
records, white spaces or
errors. The data should be
cleaned and error free.
This phase must be done
before Analysis because
based on data cleaning,
your output of Analysis will
be closer to your expected
outcome.
24
DATA REQUIREMENT
GATHERING
DATACOLLECTION
Once the data is collected,
cleaned, and processed, it is
ready for Analysis. As you
manipulate data, you may
find you have the exact
information you need, or
you might need to collect
more data. During this
phase, you can use data
analysis tools and software
which will help you to
understand, interpret, and
derive conclusions based on
the requirements.
DATACLEANING DATAANALYSIS
After analyzing your data, it's finally time to interpret
your results. You can choose the way to express or
communicate your data analysis either you can use
simply in words or maybe a table or chart. Then use the
results of your data analysis process to decide your
best course of action.
DATA INTERPRETATION
Data visualization is very
common in your day to day
life; they often appear in the
form of charts and graphs.
In other words, data shown
graphically so that it will be
easier for the human brain
to understand and process
it. Data visualization often
used to discover unknown
facts and trends. By
observing relationships and
comparing datasets, you
can find a way to find out
meaningful information.
DATA VISUALIZATION
25. Data Analysis Techniques
There are different techniques for Data
Analysis depending upon the question at
hand, the type of data, and the amount of
data gathered. Each focuses on strategies
of taking onto the new data, mining insights,
and drilling down into the information to
transform facts and figures into decision
making parameters. Accordingly, the
different techniques of data analysis can be
categorized as follows:
25
26. TECHNIQUES BASED ON
MATHEMATICS AND STATISTICS
•Descriptive Analysis: Descriptive Analysis takes into account the historical data, Key Performance Indicators,
and describes the performance based on a chosen benchmark. It takes into account past trends and how they
might influence future performance.
•Dispersion Analysis: Dispersion in the area onto which a data set is spread. This technique allows data
analysts to determine the variability of the factors under study.
•Regression Analysis: This technique works by modeling the relationship between a dependent variable and
one or more independent variables. A regression model can be linear, multiple, logistic, ridge, non-linear, life
data, and more.
•Factor Analysis: This technique helps to determine if there exists any relationship between a set of variables.
In this process, it reveals other factors or variables that describe the patterns in the relationship among the
original variables. Factor Analysis leaps forward into useful clustering and classification procedures.
•Discriminant Analysis: It is a classification technique in data mining. It identifies the different points on
different groups based on variable measurements. In simple terms, it identifies what makes two groups different
from one another; this helps to identify new items.
•Time Series Analysis: In this kind of analysis, measurements are spanned across time, which gives us a
27. Techniques based on Artificial
Intelligence and Machine Learning
•Artificial Neural Networks: a Neural network is a biologically-inspired programming
paradigm that presents a brain metaphor for processing information. An Artificial Neural
Network is a system that changes its structure based on information that flows through the
network. ANN can accept noisy data and are highly accurate. They can be considered highly
dependable in business classification and forecasting applications.
•Decision Trees: As the name stands, it is a tree-shaped model that represents a
classification or regression models. It divides a data set in smaller subsets simultaneously
developing into a related decision tree.
•Evolutionary Programming: This technique combines the different types of data analysis
using evolutionary algorithms. It is a domain-independent technique, which can explore
ample search space and manages attribute interaction very efficiently.
•Fuzzy Logic: It is a data analysis technique based on probability which helps in handling the
uncertainties in data mining techniques.
28. Techniques based on Visualization and
Graphs
•Column Chart, Bar Chart: Both these charts are used to present numerical differences between
categories. The column chart takes to the height of the columns to reflect the differences. Axes
interchange in the case of the bar chart.
•Line Chart: This chart is used to represent the change of data over a continuous interval of time.
•Area Chart: This concept is based on the line chart. It additionally fills the area between the polyline and
the axis with color, thus representing better trend information.
•Pie Chart: It is used to represent the proportion of different classifications. It is only suitable for only one
series of data. However, it can be made multi-layered to represent the proportion of data in different
categories.
•Funnel Chart: This chart represents the proportion of each stage and reflects the size of each module. It
helps in comparing rankings.
•Word Cloud Chart: It is a visual representation of text data. It requires a large amount of data, and the
degree of discrimination needs to be high for users to perceive the most prominent one. It is not a very
accurate analytical technique.
•Gantt Chart: It shows the actual timing and the progress of activity in comparison to the requirements.
29. Techniques based on Visualization and
Graphs
•Radar Chart: It is used to compare multiple quantized charts. It represents which variables in the
data have higher values and which have lower values. A radar chart is used for comparing
classification and series along with proportional representation.
•Scatter Plot: It shows the distribution of variables in the form of points over a rectangular coordinate
system. The distribution in the data points can reveal the correlation between the variables.
•Bubble Chart: It is a variation of the scatter plot. Here, in addition to the x and y coordinates, the
area of the bubble represents the 3rd value.
•Gauge: It is a kind of materialized chart. Here the scale represents the metric, and the pointer
represents the dimension. It is a suitable technique to represent interval comparisons.
•Frame Diagram: It is a visual representation of a hierarchy in the form of an inverted tree structure.
•Rectangular Tree Diagram: This technique is used to represent hierarchical relationships but at the
same level. It makes efficient use of space and represents the proportion represented by each
rectangular area.
30. Techniques based on Visualization and
Graphs
•Map
• Regional Map: It uses color to represent value distribution over a map partition.
• Point Map: It represents the geographical distribution of data in the form of points on a
geographical background. When the points are the same in size, it becomes meaningless for
single data, but if the points are as a bubble, then it additionally represents the size of the data
in each region.
• Flow Map: It represents the relationship between an inflow area and an outflow area. It
represents a line connecting the geometric centers of gravity of the spatial elements. The use
of dynamic flow lines helps reduce visual clutter.
• Heat Map: This represents the weight of each point in a geographic area. The color here
represents the density.
31. DATA ANALYSIS TOOLS
There are several data analysis tools available in
the market, each with its own set of functions. The
selection of tools should always be based on the type
of analysis performed, and the type of data worked.
Here is a list of a few compelling tools for Data
Analysis.
31
32. 1. Excel - It has a variety of compelling features, and with additional plugins installed, it can handle a massive
amount of data. So, if you have data that does not come near the significant data margin, then Excel can be a
very versatile tool for data analysis.
2. Tableau - It falls under the BI Tool category, made for the sole purpose of data analysis. The essence of Tableau
is the Pivot Table and Pivot Chart and works towards representing data in the most user-friendly way. It additionally
has a data cleaning feature along with brilliant analytical functions. E.g. Udemy's online course Hands-On Tableau
Training for Data Science can be a great asset for you.
3. Power BI - It initially started as a plugin for Excel, but later on, detached from it to develop in one of the most data
analytics tools. It comes in three versions: Free, Pro, and Premium. Its PowerPivot and DAX language can
implement sophisticated advanced analytics similar to writing Excel formulas.
4. Fine Report - Fine Report comes with a straightforward drag and drops operation, which helps to design various
styles of reports and build a data decision analysis system. It can directly connect to all kinds of databases, and its
format is similar to that of Excel. Additionally, it also provides a variety of dashboard templates and several self-
developed visual plug-in libraries.
5. R & Python - These are programming languages which are very powerful and flexible. R is best at statistical
analysis, such as normal distribution, cluster classification algorithms, and regression analysis. It also performs
individual predictive analysis like customer behavior, his spend, items preferred by him based on his browsing
history, and more. It also involves concepts of machine learning and artificial intelligence.
6. SAS - It is a programming language for data analytics and data manipulation, which can easily access data from
any source. SAS has introduced a broad set of customer profiling products for web, social media, and marketing