1. primary data and secondary data along with there methods of data collection.
2. importance of statistics in research work
3. questionnaire on biscuit
Everyone is a data scientist today, but that is impossible. How do you spot the real data scientist from the fake? Some people just lie. Don't be fooled this presentation will help find the fools
HCID2014: In interfaces we trust? End user interactions with smart systems. D...City University London
There are many cutting-edge systems that learn from users and do something smart as a result. These systems are often reasonably reliable but they do make mistakes. This talk gives an overview of research that investigates what matters to trust as users interact and how we could design interfaces to support users better.
For a detailed explanation Watch the Youtube video:
https://youtu.be/cZlGTckM1AE
introduction to statistics,origin definition,characteristics of statistics, Data collection- primary data, secondary data, difference, sources of primary and secondary data collection, questionnaire vs schedules, limitations of statistics, scrutiny of data
The document defines research and describes various methods for collecting primary data. It discusses:
1) Research is defined as creating new knowledge through new and creative use of existing information. It includes synthesis and analysis of previous research.
2) Primary data collection methods include quantitative methods like surveys, observations, and experiments as well as qualitative methods like focus groups, in-depth interviews, and projective techniques.
3) Both primary and secondary sources are used to gather data, with primary data collected directly for the research and secondary data having been previously collected.
Data can come from internal or external sources and be collected as either primary or secondary data. Primary data is collected directly from first-hand experiences through methods like interviews, questionnaires, or observation and allows for targeted research but is more costly and time-consuming. Secondary data has already been collected for other purposes and can be quicker to obtain but does not directly address specific research questions and risks being outdated or inaccurate. Both types of data have merits and limitations depending on the research needs.
This document discusses research methodology, specifically focusing on methods for collecting primary and secondary data. It defines primary data as original data collected by the researcher, while secondary data is already existing data collected by others. Some key methods for collecting primary data discussed include direct personal observation, indirect oral interviews, collecting information through agencies, mailed questionnaires, and using enumerators to distribute schedules. Sources of secondary data include government publications, research institutions, commercial/financial institutions, and unpublished records. The document also covers case study methodology, defining it as an intensive examination of a single unit or case and outlining its characteristics and limitations.
This document provides an introduction to statistics and data collection methods. It discusses key concepts such as:
1. The difference between economic and non-economic activities, and definitions of common economic roles like consumers, producers, service holders and service providers.
2. The stages of collecting statistical data, including primary and secondary sources, methods of collecting primary data, and the differences between primary and secondary data.
3. Methods of organizing raw data through classification, frequency distributions, and other statistical techniques. Common approaches to presenting organized data are also outlined, including tables, diagrams and graphs.
4. Sampling methods like census surveys and sample surveys are introduced, along with the differences between them. Key organizations involved in
Everyone is a data scientist today, but that is impossible. How do you spot the real data scientist from the fake? Some people just lie. Don't be fooled this presentation will help find the fools
HCID2014: In interfaces we trust? End user interactions with smart systems. D...City University London
There are many cutting-edge systems that learn from users and do something smart as a result. These systems are often reasonably reliable but they do make mistakes. This talk gives an overview of research that investigates what matters to trust as users interact and how we could design interfaces to support users better.
For a detailed explanation Watch the Youtube video:
https://youtu.be/cZlGTckM1AE
introduction to statistics,origin definition,characteristics of statistics, Data collection- primary data, secondary data, difference, sources of primary and secondary data collection, questionnaire vs schedules, limitations of statistics, scrutiny of data
The document defines research and describes various methods for collecting primary data. It discusses:
1) Research is defined as creating new knowledge through new and creative use of existing information. It includes synthesis and analysis of previous research.
2) Primary data collection methods include quantitative methods like surveys, observations, and experiments as well as qualitative methods like focus groups, in-depth interviews, and projective techniques.
3) Both primary and secondary sources are used to gather data, with primary data collected directly for the research and secondary data having been previously collected.
Data can come from internal or external sources and be collected as either primary or secondary data. Primary data is collected directly from first-hand experiences through methods like interviews, questionnaires, or observation and allows for targeted research but is more costly and time-consuming. Secondary data has already been collected for other purposes and can be quicker to obtain but does not directly address specific research questions and risks being outdated or inaccurate. Both types of data have merits and limitations depending on the research needs.
This document discusses research methodology, specifically focusing on methods for collecting primary and secondary data. It defines primary data as original data collected by the researcher, while secondary data is already existing data collected by others. Some key methods for collecting primary data discussed include direct personal observation, indirect oral interviews, collecting information through agencies, mailed questionnaires, and using enumerators to distribute schedules. Sources of secondary data include government publications, research institutions, commercial/financial institutions, and unpublished records. The document also covers case study methodology, defining it as an intensive examination of a single unit or case and outlining its characteristics and limitations.
This document provides an introduction to statistics and data collection methods. It discusses key concepts such as:
1. The difference between economic and non-economic activities, and definitions of common economic roles like consumers, producers, service holders and service providers.
2. The stages of collecting statistical data, including primary and secondary sources, methods of collecting primary data, and the differences between primary and secondary data.
3. Methods of organizing raw data through classification, frequency distributions, and other statistical techniques. Common approaches to presenting organized data are also outlined, including tables, diagrams and graphs.
4. Sampling methods like census surveys and sample surveys are introduced, along with the differences between them. Key organizations involved in
This document provides an introduction to statistics. It defines statistics as the science of collecting, organizing, analyzing, and interpreting data to make decisions. The origin of statistics can be traced back to Latin and other languages where it referred to the administration of a political state. In modern times, statistics is used across many fields to quantify information. Primary data is collected directly from original sources while secondary data has already been collected by others. Various methods are discussed for collecting primary data including questionnaires, surveys, and interviews.
This document discusses the sources and interpretation of data. It defines data as quantitative or qualitative values that can be used for analysis. There are internal and external sources of data. Internal data comes from within an organization, while external data comes from outside sources and can be primary or secondary. Primary data is collected first-hand through methods like surveys, while secondary data has already been collected for other purposes from sources like publications and records. The key difference between primary and secondary data is that primary data involves direct collection and avoids bias, while secondary data is existing data whose source and potential biases may be unknown.
This document discusses various techniques for collecting data. It defines quantitative and qualitative data and explains how they can be gathered from the same data unit. Primary data is collected directly by the researcher, while secondary data has already been collected. Common methods for collecting primary data include observation, interviews, questionnaires, schedules, and other techniques like content analysis. Factors like the nature, scope and objectives of the study, availability of funds and time, and required precision determine the appropriate data collection method. Precautions must be taken to ensure data is relevant, collected systematically, can be statistically analyzed, has minimal error, and is accurate, reliable, valid, complete and comprehensive.
Data can be collected from both primary and secondary sources. Primary data is originally collected to fulfill the specific needs of a research problem through methods like direct personal interviews, indirect interviews, questionnaires, and schedules sent through enumerators. Secondary data is data that was previously collected and includes published sources like government and private organization reports as well as unpublished sources like internal studies. The choice between primary and secondary data depends on factors like the nature and scope of the research, available resources, desired accuracy, and time and agency available for data collection.
This document provides an overview of managerial statistics and key statistical concepts. It discusses the definition and utility of statistics, as well as characteristics like studying aggregate facts affected by multiple causes. It also outlines how statistics are used in business functions like marketing, production, and finance. Additionally, it defines important statistical terms like population, sample, parameter, and discrete and continuous data. Finally, it discusses primary and secondary data sources and various methods for collecting primary data through interviews, questionnaires, and enumerators.
Research Instrument, Development & Analysis-The Questionnaire ShaharyarShoukatShou
This document discusses research questionnaires. It begins by defining a questionnaire as a list of printed questions completed by respondents. The primary purpose of a questionnaire is to extract data from respondents in a standardized way. There are two main types of questionnaires: unstructured/open-form questionnaires that allow free responses; and structured/closed-form questionnaires that have predetermined answer choices. The document then provides details on the characteristics, advantages, and types of both questionnaire formats.
Data can be quantitative or qualitative values that are collected from various sources. There are two main types of data sources: internal sources which are from within an organization, and external sources which are outside. External data can be primary data collected directly from original research, or secondary data that has already been collected for other purposes. Primary data collection methods include surveys, interviews, and observations, while secondary data comes from published sources like reports and unpublished sources. The main difference between primary and secondary data is that primary data involves direct collection while secondary data has already been collected previously.
Data collection - Statistical data are a numerical statement of aggregates. Data, generally, are obtained through properly organized statistical inquiries conducted by the investigators. Data can either be from primary or secondary sources.
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.
DEFINITION OF STATISTICS,IMPORTANCE & LIMITATIONS OF STATISTICS,STATISTICAL INVESTIGATION,COLLECTION OF DATA,SOURCES OF DATA,PRIMARY DATA,SECONDARY DATA,QUESTIONNAIRE,SCHEDULE,TABULATION OF DATA,COLLECTION OF DATA,STATISTICS
This document discusses primary and secondary data sources for collecting information. Primary data is originally collected by the researcher, such as through direct observation, interviews, questionnaires, or surveys. Secondary data has already been collected by other agencies and researchers, such as published government reports, research papers, or unpublished records. When deciding on a data collection method, researchers must consider factors like the study objectives, resources, time availability, and required accuracy. Both primary and secondary data have advantages and limitations that researchers should be aware of to appropriately evaluate and utilize the data.
Statistics report on Socio-economic background of InvestorsPranab Ghosh
The document provides details about a report on the socio-economic background of general investors in Bangladesh. It includes an introduction outlining the objectives and methodology of the report. It also describes the theoretical background on various statistical tools and techniques used in the analysis. The main body of the report presents statistical analysis on the socio-economic characteristics of investors such as their age, gender, income, and expenditure patterns. Graphs and regression analysis are used to analyze the survey data collected from brokerage houses. The conclusions provide insights about investors gleaned from applying statistical methods.
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
Data are distinct pieces of information collected for analysis to produce research results. There are two types of data: primary and secondary. Primary data is original data collected directly by the researcher through surveys, observation, or experimentation. Secondary data refers to data originally collected by someone else for another purpose that is now being used for a new study. Common methods for collecting primary data include observation, interviews, questionnaires, and schedules. Secondary data can come from government publications, journals, reports, and unpublished sources.
Qualitative vs quantitative user research.pdfWebMaxy
It's a debate as old as time: Qualitative vs Quantitative user research. Which is better for understanding your users?
Do you know the differences between qualitative and quantitative user research?
Here's what you need to know: #QualitativeResearch helps you understand the 'why' behind user behavior, while #QuantitativeResearch helps you measure user behavior.
Check out our latest blog post to learn about the key differences of these two research methods! https://www.webmaxy.co/analyzer/qualitative-vs-quantitative-user-research/
Get started with Webmaxy for free today: https://calendly.com/webmaxy/30min
This Power Point Presentation has been made while referring to the research books written by eminent, renowned and expert authors as mentioned in the references section. The purpose of this Presentation is to help the research students in developing an insight about the Data in Research. I hope the students will find this presentation useful for them.
Data Collection is the segment of any type of research study. At the time when researcher need examine answers to the research problem data collection methods is critical for collection relevant information. this slide also depict of decision to be made by the Researcher towards data collection, methods of data collection with merits and demerits respectively.
This document discusses survey methodology. It begins by defining what a survey is - a means to gather prompt information from a sample of a population. It notes that surveys are used by governments, businesses, and institutions. The document then discusses sample size and methodology, explaining that samples must be scientifically selected so each person has a chance of selection. It outlines some common survey methods like personal interviews, mail surveys, and telephone interviews. The document also discusses issues like confidentiality, sample representativeness, and ensuring unbiased and consistent results. It provides an example of a large survey conducted in India.
Different Methods of Collection of DataP. Veeresha
Data collection is a term used to describe a process of preparing and collecting data.
Data are the basic inputs to any decision making process in any fields like education, business, industries…. etc
The primary data are those which are collected afresh and for the first time, and thus happen to be original in character. It is real time data and which are collected by the researcher himself.
Secondary data means data that are already available i.e., they refer to the data which have already been collected and analyzed by someone else.
Statistical analysis and Statistical process in 2023 .pptxFayaz Ahmad
Fayaz Ahmad (known as Feng fei in China) is a PhD scholar in Biostatistics and Epidemiology at Zhengzhou University in China. He has over 5 years of experience working in universities in Pakistan and has received several awards for his work, including developing a mosquito killing device. He is a member of the American Statistical Association and coordinates statistical training programs in Pakistan.
Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
This document provides an introduction to statistics. It defines statistics as the science of collecting, organizing, analyzing, and interpreting data to make decisions. The origin of statistics can be traced back to Latin and other languages where it referred to the administration of a political state. In modern times, statistics is used across many fields to quantify information. Primary data is collected directly from original sources while secondary data has already been collected by others. Various methods are discussed for collecting primary data including questionnaires, surveys, and interviews.
This document discusses the sources and interpretation of data. It defines data as quantitative or qualitative values that can be used for analysis. There are internal and external sources of data. Internal data comes from within an organization, while external data comes from outside sources and can be primary or secondary. Primary data is collected first-hand through methods like surveys, while secondary data has already been collected for other purposes from sources like publications and records. The key difference between primary and secondary data is that primary data involves direct collection and avoids bias, while secondary data is existing data whose source and potential biases may be unknown.
This document discusses various techniques for collecting data. It defines quantitative and qualitative data and explains how they can be gathered from the same data unit. Primary data is collected directly by the researcher, while secondary data has already been collected. Common methods for collecting primary data include observation, interviews, questionnaires, schedules, and other techniques like content analysis. Factors like the nature, scope and objectives of the study, availability of funds and time, and required precision determine the appropriate data collection method. Precautions must be taken to ensure data is relevant, collected systematically, can be statistically analyzed, has minimal error, and is accurate, reliable, valid, complete and comprehensive.
Data can be collected from both primary and secondary sources. Primary data is originally collected to fulfill the specific needs of a research problem through methods like direct personal interviews, indirect interviews, questionnaires, and schedules sent through enumerators. Secondary data is data that was previously collected and includes published sources like government and private organization reports as well as unpublished sources like internal studies. The choice between primary and secondary data depends on factors like the nature and scope of the research, available resources, desired accuracy, and time and agency available for data collection.
This document provides an overview of managerial statistics and key statistical concepts. It discusses the definition and utility of statistics, as well as characteristics like studying aggregate facts affected by multiple causes. It also outlines how statistics are used in business functions like marketing, production, and finance. Additionally, it defines important statistical terms like population, sample, parameter, and discrete and continuous data. Finally, it discusses primary and secondary data sources and various methods for collecting primary data through interviews, questionnaires, and enumerators.
Research Instrument, Development & Analysis-The Questionnaire ShaharyarShoukatShou
This document discusses research questionnaires. It begins by defining a questionnaire as a list of printed questions completed by respondents. The primary purpose of a questionnaire is to extract data from respondents in a standardized way. There are two main types of questionnaires: unstructured/open-form questionnaires that allow free responses; and structured/closed-form questionnaires that have predetermined answer choices. The document then provides details on the characteristics, advantages, and types of both questionnaire formats.
Data can be quantitative or qualitative values that are collected from various sources. There are two main types of data sources: internal sources which are from within an organization, and external sources which are outside. External data can be primary data collected directly from original research, or secondary data that has already been collected for other purposes. Primary data collection methods include surveys, interviews, and observations, while secondary data comes from published sources like reports and unpublished sources. The main difference between primary and secondary data is that primary data involves direct collection while secondary data has already been collected previously.
Data collection - Statistical data are a numerical statement of aggregates. Data, generally, are obtained through properly organized statistical inquiries conducted by the investigators. Data can either be from primary or secondary sources.
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.
DEFINITION OF STATISTICS,IMPORTANCE & LIMITATIONS OF STATISTICS,STATISTICAL INVESTIGATION,COLLECTION OF DATA,SOURCES OF DATA,PRIMARY DATA,SECONDARY DATA,QUESTIONNAIRE,SCHEDULE,TABULATION OF DATA,COLLECTION OF DATA,STATISTICS
This document discusses primary and secondary data sources for collecting information. Primary data is originally collected by the researcher, such as through direct observation, interviews, questionnaires, or surveys. Secondary data has already been collected by other agencies and researchers, such as published government reports, research papers, or unpublished records. When deciding on a data collection method, researchers must consider factors like the study objectives, resources, time availability, and required accuracy. Both primary and secondary data have advantages and limitations that researchers should be aware of to appropriately evaluate and utilize the data.
Statistics report on Socio-economic background of InvestorsPranab Ghosh
The document provides details about a report on the socio-economic background of general investors in Bangladesh. It includes an introduction outlining the objectives and methodology of the report. It also describes the theoretical background on various statistical tools and techniques used in the analysis. The main body of the report presents statistical analysis on the socio-economic characteristics of investors such as their age, gender, income, and expenditure patterns. Graphs and regression analysis are used to analyze the survey data collected from brokerage houses. The conclusions provide insights about investors gleaned from applying statistical methods.
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
Data are distinct pieces of information collected for analysis to produce research results. There are two types of data: primary and secondary. Primary data is original data collected directly by the researcher through surveys, observation, or experimentation. Secondary data refers to data originally collected by someone else for another purpose that is now being used for a new study. Common methods for collecting primary data include observation, interviews, questionnaires, and schedules. Secondary data can come from government publications, journals, reports, and unpublished sources.
Qualitative vs quantitative user research.pdfWebMaxy
It's a debate as old as time: Qualitative vs Quantitative user research. Which is better for understanding your users?
Do you know the differences between qualitative and quantitative user research?
Here's what you need to know: #QualitativeResearch helps you understand the 'why' behind user behavior, while #QuantitativeResearch helps you measure user behavior.
Check out our latest blog post to learn about the key differences of these two research methods! https://www.webmaxy.co/analyzer/qualitative-vs-quantitative-user-research/
Get started with Webmaxy for free today: https://calendly.com/webmaxy/30min
This Power Point Presentation has been made while referring to the research books written by eminent, renowned and expert authors as mentioned in the references section. The purpose of this Presentation is to help the research students in developing an insight about the Data in Research. I hope the students will find this presentation useful for them.
Data Collection is the segment of any type of research study. At the time when researcher need examine answers to the research problem data collection methods is critical for collection relevant information. this slide also depict of decision to be made by the Researcher towards data collection, methods of data collection with merits and demerits respectively.
This document discusses survey methodology. It begins by defining what a survey is - a means to gather prompt information from a sample of a population. It notes that surveys are used by governments, businesses, and institutions. The document then discusses sample size and methodology, explaining that samples must be scientifically selected so each person has a chance of selection. It outlines some common survey methods like personal interviews, mail surveys, and telephone interviews. The document also discusses issues like confidentiality, sample representativeness, and ensuring unbiased and consistent results. It provides an example of a large survey conducted in India.
Different Methods of Collection of DataP. Veeresha
Data collection is a term used to describe a process of preparing and collecting data.
Data are the basic inputs to any decision making process in any fields like education, business, industries…. etc
The primary data are those which are collected afresh and for the first time, and thus happen to be original in character. It is real time data and which are collected by the researcher himself.
Secondary data means data that are already available i.e., they refer to the data which have already been collected and analyzed by someone else.
Statistical analysis and Statistical process in 2023 .pptxFayaz Ahmad
Fayaz Ahmad (known as Feng fei in China) is a PhD scholar in Biostatistics and Epidemiology at Zhengzhou University in China. He has over 5 years of experience working in universities in Pakistan and has received several awards for his work, including developing a mosquito killing device. He is a member of the American Statistical Association and coordinates statistical training programs in Pakistan.
Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
1. Indore Institute Of Law
(Affiliated to D.A.V.V. & Bar Council of India)
B.B.A.LL.B. (HONS.)
A PROJECT OF BUSINESS STATISTICS
SUBMITTED TO: Prof. Ambrish Bapat
SUBMITTED BY: Aishwarya Gupta
2. Data Collection
We know that, in the plural sense, statistics
means data or quantitative information
capable of some meaningful conclusions.
For the student of economics, the purpose of
data collection is to understand, analyze and
explain a socio-economic problem, for
example, the problem of unemployment or
poverty.
3. Sources of Data Collection
Primary Source
Collection of data from its source of origin. It offers you first-
hand quantitative information relating to your statistical
study.
Secondary Source
Collection of data from some agency or institution which
already happens to have collected the data through statistical
survey.
5. Direct Personal Interviews
The data are personally collected by the investigator
from the informants.
Merits
Originality
Accuracy
Reliability
Demerits
Limited Coverage
Personal Bias
Timely and Costly
6. Indirect Oral Interviews
The information is obtained not from the persons regarding
whom the information is needed. It is collected orally from
other persons who are expected to possess the necessary
information.
Merits
Wide coverage
Less Expensive
Expert Opinion
Demerits
Less Accurate
Biased
Doubtful
Conclusions
7. Information from Local Source or
Correspondents
The investigator appoints local agents or correspondents in
different places to collect information. These correspondents
collect and transmit the information to the central office
where the data are processed.
Merits
Economical
Wide coverage
Continuity
Demerits
Loss of Originality
Personal Bias
Less Accuracy
8. Mailed Questionnaire
A list of questions pertaining to the survey is prepared and
sent to the various informants. The informant notes the
answer against the questions and return the completed
questionnaire to the investigator.
Merits
Economical
Original
Wide Coverage
Demerits
Limited use
Lack of
Flexibility
Less Accuracy
9. Schedules through Enumerators
Under this method, the enumerator himself approaches the
informant with the questionnaire. The questionnaire which
are filled by the enumerators themselves by putting questions
are called schedules.
Merits
Wide Coverage
Accuracy
Completeness
Demerits
Expensive
Training of Enumerators
Not Suitable for Private
Investigation
11. Importance of Secondary Data in
Research
Economical
Time Saving
Basis for Primary Data
Accessibility
May Answer Research Question
12. Importance of Statistics in Research
Work
They permit the most exact kind of description:
They force us to be definite
They help us to summarize the results
They enable us to draw general conclusions
They enable us to make predictions
They enable us to analyze some of the casual factors of
complex and otherwise bewildering events.