Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
leewayhertz.com-Data analysis workflow using Scikit-learn.pdfKristiLBurns
Data analysis is the process of analyzing, cleaning, transforming, and modeling data to uncover useful information and draw conclusions from it to support decision-making. It involves applying various statistical and analytical techniques to uncover patterns, relationships, and insights from raw data.
FOUR TYPES OF BUSINESS ANALYTICS TO KNOWBUSINESS ANALYTICSby AJeanmarieColbert3
FOUR TYPES OF BUSINESS ANALYTICS TO KNOW
BUSINESS ANALYTICS
by Anushka Mehta October 13, 2017
For different stages of business analytics huge amount of data is processed at various steps. Depending on the stage of the workflow and the requirement of data analysis, there are four main kinds of analytics – descriptive, diagnostic, predictive and prescriptive. These four types together answer everything a company needs to know- from what’s going on in the company to what solutions to be adopted for optimizing the functions.
The four types of analytics are usually implemented in stages and no one type of analytics is said to be better than the other. They are interrelated and each of these offers a different insight. With data being important to so many diverse sectors- from manufacturing to energy grids, most of the companies rely on one or all of these types of analytics. With the right choice of analytical techniques, big data can deliver richer insights for the companies
Before diving deeper into each of these, let’s define the four types of analytics:
1) Descriptive Analytics:Describing or summarizing the existing data using existing business intelligence tools to better understand what is going on or what has happened.
2) Diagnostic Analytics: Focus on past performance to determine what happened and why. The result of the analysis is often an analytic dashboard.
3) Predictive Analytics:Emphasizes on predicting the possible outcome using statistical models and machine learning techniques.
4) Prescriptive Analytics:It is a type of predictive analytics that is used to recommend one or more course of action on analyzing the data.
Let’s understand these in a bit more depth.
1. Descriptive Analytics
This can be termed as the simplest form of analytics. The mighty size of big data is beyond human comprehension and the first stage hence involves crunching the data into understandable chunks. The purpose of this analytics type is just to summarize the findings and understand what is going on.
Among some frequently used terms, what people call as advanced analytics or business intelligence is basically usage of descriptive statistics (arithmetic operations, mean, median, max, percentage, etc.) on existing data. It is said that 80% of business analytics mainly involves descriptions based on aggregations of past performance. It is an important step to make raw data understandable to investors, shareholders and managers. This way it gets easy to identify and address the areas of strengths and weaknesses such that it can help in strategizing.
The two main techniques involved are data aggregation and data mining stating that this method is purely used for understanding the underlying behavior and not to make any estimations. By mining historical data, companies can analyze the consumer behaviors and engagements with their businesses that could be helpful in targeted marketing, service improvement, etc. The tools used in this phase are MS Excel, MATLAB ...
Data Analytics for Digital Marketing Lecture for Advanced Digital & Social Me...Valters Lauzums
Unlock the power of data analytics with our comprehensive slide deck from the Advanced Digital Strategy MGMT X 466.05 course at UCLAx. This presentation reviews the fundamental concepts and practical applications of data analytics in business and marketing.
Key Topics Covered:
Overview & Concepts: Learn how data analytics uses statistics, predictive modeling, and machine learning to enhance business performance.
Types of Data: Understand the differences between structured and unstructured data, and how to leverage quantitative and qualitative data.
Key Techniques: Explore descriptive, diagnostic, predictive, and prescriptive analytics to transform raw data into actionable insights.
Common Tools: Get acquainted with popular tools like Google Analytics, Google Looker, Adobe Analytics, and HubSpot for effective data tracking and analysis.
Data Analysis Process: Follow a step-by-step guide to collecting, cleaning, modeling, and interpreting data to drive informed decision-making.
Optimizing Campaigns: Learn how to use A/B testing and past campaign performance data to enhance future marketing efforts.
Defining Audiences: Discover how to segment target audiences using demographic data, purchase histories, and online behaviors for more precise marketing strategies.
Advanced Methods: Dive into advanced data analysis techniques like cohort analysis, cluster analysis, sentiment analysis, and regression analysis.
Customer Journey Analytics: Visualize the customer journey and identify key engagement moments to optimize the customer experience.
Data Visualization & Storytelling: Master the art of communicating data insights effectively through visualizations and contextual storytelling.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Data Analysis Methods 101 - Turning Raw Data Into Actionable InsightsDataSpace Academy
Data analytics is powerful for organisations. It can help companies improve their overall efficiency and effectiveness. The blog offers a step-by-step narration of the data analysis methods that will help you to comprehend the fundamentals of an analytics project.
leewayhertz.com-Data analysis workflow using Scikit-learn.pdfKristiLBurns
Data analysis is the process of analyzing, cleaning, transforming, and modeling data to uncover useful information and draw conclusions from it to support decision-making. It involves applying various statistical and analytical techniques to uncover patterns, relationships, and insights from raw data.
FOUR TYPES OF BUSINESS ANALYTICS TO KNOWBUSINESS ANALYTICSby AJeanmarieColbert3
FOUR TYPES OF BUSINESS ANALYTICS TO KNOW
BUSINESS ANALYTICS
by Anushka Mehta October 13, 2017
For different stages of business analytics huge amount of data is processed at various steps. Depending on the stage of the workflow and the requirement of data analysis, there are four main kinds of analytics – descriptive, diagnostic, predictive and prescriptive. These four types together answer everything a company needs to know- from what’s going on in the company to what solutions to be adopted for optimizing the functions.
The four types of analytics are usually implemented in stages and no one type of analytics is said to be better than the other. They are interrelated and each of these offers a different insight. With data being important to so many diverse sectors- from manufacturing to energy grids, most of the companies rely on one or all of these types of analytics. With the right choice of analytical techniques, big data can deliver richer insights for the companies
Before diving deeper into each of these, let’s define the four types of analytics:
1) Descriptive Analytics:Describing or summarizing the existing data using existing business intelligence tools to better understand what is going on or what has happened.
2) Diagnostic Analytics: Focus on past performance to determine what happened and why. The result of the analysis is often an analytic dashboard.
3) Predictive Analytics:Emphasizes on predicting the possible outcome using statistical models and machine learning techniques.
4) Prescriptive Analytics:It is a type of predictive analytics that is used to recommend one or more course of action on analyzing the data.
Let’s understand these in a bit more depth.
1. Descriptive Analytics
This can be termed as the simplest form of analytics. The mighty size of big data is beyond human comprehension and the first stage hence involves crunching the data into understandable chunks. The purpose of this analytics type is just to summarize the findings and understand what is going on.
Among some frequently used terms, what people call as advanced analytics or business intelligence is basically usage of descriptive statistics (arithmetic operations, mean, median, max, percentage, etc.) on existing data. It is said that 80% of business analytics mainly involves descriptions based on aggregations of past performance. It is an important step to make raw data understandable to investors, shareholders and managers. This way it gets easy to identify and address the areas of strengths and weaknesses such that it can help in strategizing.
The two main techniques involved are data aggregation and data mining stating that this method is purely used for understanding the underlying behavior and not to make any estimations. By mining historical data, companies can analyze the consumer behaviors and engagements with their businesses that could be helpful in targeted marketing, service improvement, etc. The tools used in this phase are MS Excel, MATLAB ...
Data Analytics for Digital Marketing Lecture for Advanced Digital & Social Me...Valters Lauzums
Unlock the power of data analytics with our comprehensive slide deck from the Advanced Digital Strategy MGMT X 466.05 course at UCLAx. This presentation reviews the fundamental concepts and practical applications of data analytics in business and marketing.
Key Topics Covered:
Overview & Concepts: Learn how data analytics uses statistics, predictive modeling, and machine learning to enhance business performance.
Types of Data: Understand the differences between structured and unstructured data, and how to leverage quantitative and qualitative data.
Key Techniques: Explore descriptive, diagnostic, predictive, and prescriptive analytics to transform raw data into actionable insights.
Common Tools: Get acquainted with popular tools like Google Analytics, Google Looker, Adobe Analytics, and HubSpot for effective data tracking and analysis.
Data Analysis Process: Follow a step-by-step guide to collecting, cleaning, modeling, and interpreting data to drive informed decision-making.
Optimizing Campaigns: Learn how to use A/B testing and past campaign performance data to enhance future marketing efforts.
Defining Audiences: Discover how to segment target audiences using demographic data, purchase histories, and online behaviors for more precise marketing strategies.
Advanced Methods: Dive into advanced data analysis techniques like cohort analysis, cluster analysis, sentiment analysis, and regression analysis.
Customer Journey Analytics: Visualize the customer journey and identify key engagement moments to optimize the customer experience.
Data Visualization & Storytelling: Master the art of communicating data insights effectively through visualizations and contextual storytelling.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Data Analysis Methods 101 - Turning Raw Data Into Actionable InsightsDataSpace Academy
Data analytics is powerful for organisations. It can help companies improve their overall efficiency and effectiveness. The blog offers a step-by-step narration of the data analysis methods that will help you to comprehend the fundamentals of an analytics project.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
This ebook is all about data analysis, what are the steps involved in data analysis and what are the techniques. We will bring out a detailed course very soon. pls register https://excelfinanceacademy.zenler.com/ to save over 80% cost
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docxanhlodge
Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
data analytics is the process of examining large datasets to uncover hidden patterns, correlations, trends and insights that can inform decision-making and drive business strategies.
Uncover Trends and Patterns with Data Science.pdfUncodemy
In today's data-driven world, the vast amount of information generated every second presents both challenges and opportunities for businesses and researchers alike. Harnessing this data effectively can provide valuable insights, unlock hidden trends, and identify patterns that drive innovation and strategic decision-making.
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfKajal Digital
Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
This ebook is all about data analysis, what are the steps involved in data analysis and what are the techniques. We will bring out a detailed course very soon. pls register https://excelfinanceacademy.zenler.com/ to save over 80% cost
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docxanhlodge
Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
data analytics is the process of examining large datasets to uncover hidden patterns, correlations, trends and insights that can inform decision-making and drive business strategies.
Uncover Trends and Patterns with Data Science.pdfUncodemy
In today's data-driven world, the vast amount of information generated every second presents both challenges and opportunities for businesses and researchers alike. Harnessing this data effectively can provide valuable insights, unlock hidden trends, and identify patterns that drive innovation and strategic decision-making.
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfKajal Digital
Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
what is ..how to process types and methods involved in data analysis
1. 1/7
June 16, 2021
What is & how to… Process Types and Methods
involved in Data Analysis
dataanalysis.ie/process-types-and-method-involved-in-data-analysis
Process Types and Methods involved in Data Analysis
Today businesses are looking for every edge and advantage they could gather.
There are barriers such as unexpected conversion markets, financial uncertainty, shifting
political landscapes,
The capricious attitudes of buyers, which make businesses operate today with thin
margins of error.
Without smart choices and proper data analysis, it is impossible to sustain the growth
and development of any business.
Then how does an individual for this matter an organization make those choices?
It is simply by accumulating as a whole a lot of useful, exploitable cases as viable, then
using it to make better-informed decisions.
Common sense is the strategy that can be applied to business as well as personal life.
Important decisions are only made until and unless something is at stake. In order, for
any company to succeed, decisions shouldn’t be made based on ignorance.
Organization requires data, and this requirement of data is the reason why data analysis
comes into the picture. Below is the list of topics that gives you a better understanding of
data analysis.
2. 2/7
List of topics
1. Data Analysis
2. Process of Data Analysis
3. Types of Data Analysis
4. Data Analysis Methods
1. Data Analysis:
Numerous agencies and professionals have different methods of data analytics, but
most of them can be described under a common definition.
Data analysis is the process of cleaning, transforming, and processing raw data in
order to extract useful and actionable information that can assist businesses in making
better decisions.
The program helps in reducing the possibility of risk inherent in decision-making by
providing useful information and statistical data (usually presented in the form of a chart,
tables, graphs, and images).
The term “big data” is often used in discussions of data analytics.
Data analytics plays an important role in processing big data and transforming it into
actionable information. Novice data analysts looking to explore the principles of big data
should return to the basic question “What is data?”
Data are quantifiable units of information collected through Research observation.
2. Data Analysis Process: The data analysis process, or discrete steps in data analysis,
involves collecting and processing all information, examining it, and using it to find
patterns and other information. This process includes:
Data Requirement Gathering: Ask yourself why you are doing this analysis, the type
of data you want to use, and what data you want to analyze.
Data Collection: Start collecting data from the source according to the requirements
you define. Resources include case studies, surveys, interviews, questionnaires, personal
observations, and focus groups. Organize collected data for analysis.
Data Cleaning: Not all data collected is useful, so now is the time to clean it up. This
process removes free disk space, unwanted recordings, and inherent errors. It is
important to clear data before submitting it for analysis.
Data Analysis: Here you can use data analysis software and other tools to interpret,
understand, and estimate your data.
3. 3/7
Data analysis tools include Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase,
Redash, and Microsoft Power BI.
Data Interpretation: Now that we have our results, we need to interpret them and do
our best based on them.
Data Visualization: Data visualization is nothing but a graphical presentation of the
information that people can read and understand. It can be in the form of tables, charts,
bullet points, maps, or any other methods.
Visualizations provide valuable information by comparing data sets and monitoring
relationships.
3. Types of Data Analysis:
There are currently half-dozen types of data analysis that are widely used in technology
and business. They are:
Diagnostic Analysis: “Why is this happening?” the one who has an answer to this
question is diagnostic analysis.
The analyst uses the information obtained from statistical analysis to identify patterns in
the data through diagnostic analysis. Ideally, the analyst looks for similar patterns that
have existed in the past and use solutions to solve current problems.
Predictive Analysis: Predictive analysis can answer “What is most likely to happen?”.
Analysts use historical data from current events and future patterns to predict future
events.
NO prediction is 100% accurate, but analysts are more likely to make predictions if they
have enough specific information and enough discipline to thoroughly investigate.
Prescriptive Analysis: Combine all information from different types of data analysis
which gives you prescriptive analysis.
Sometimes, an issue cannot be solved completely with one analysis type, and instead
requires a lot of information.
Statistical Analysis: “What happened?” can be answered by statistical analysis. It
involves the collection, analysis, interpretation, modeling, and visualization of data
through dashboards. Statistical analysis can be divided into two subsets:
(a) Descriptive: Descriptive analysis works on the whole set or summarized numerical
data. It explains the mean and deviation in continuous data and the percentage and
frequency in categorical data.
(b) Inferential: Inferential analysis works with samples derived from any data. Analysts
can draw different conclusions by choosing different samples from the same bulk of data.
4. 4/7
Text Analysis: Another Name of text analysis is “data mining”, which uses databases
and data mining tools to find patterns in large data sets. Transform raw data into
actionable business transformation. Text analysis is probably the simplest way to analyze
data.
Now, we will try to understand the data analysis method in depth
Cluster Analysis: Grouping a set of data elements according to similarity to each other
than to those in other groups.
This method is often used to find hidden patterns in data because there is no target
variable when clustering. This approach is also used to deliver additional context for a
process or data set.
Looking from a business perspective. In an ideal world, marketers could analyze each
customer individually and provide the best personalized service, when we deal with a
large customer base, it is next to impossible.
But when we group customer base according to demographics, purchasing habits,
monetary value, or other business-related factors.
which might be relevant for our company, we can improve our efforts and the needs of the
customers giving the customers a great experience.
Cohort analysis: This method allows you to use historical data to investigate and
compare specific parts of user behavior and integrate them with other parts with similar
characteristics.
We can use this data analysis method to gain a general understanding of consumer needs
and a broader understanding of a larger audience.
Cohort analysis is very useful for market research as it gives you an idea of the impact of a
campaign on a specific group of customers.
For example, let’s say we send out an email campaign that encourages customers to sign
up for your website.
To do this, create two versions of the campaign, each with different designs, promotional
invitations, and advertising content.
You can then use group analytics to track campaign performance over time to understand
the types of content our customers are asking to subscribe, repurchase or engage with.
Regression analysis: Regression analysis uses historical data to understand how the
dependent variable affects when one (linear regression) or more (multiple regression)
independent variables change or remain constant.
Understanding the relationship between each variable and the past variable will help you
anticipate possible outcomes and make better career decisions for the future.
5. 5/7
Let’s take an example, imagine in our 2019 sales regression analysis, we found that
variables such as product quality, store layout, customer service, marketing campaign,
and sales channels affected the overall results.
Now, we want to use regression analysis to analyze which of these variables have changed
or which new ones have appeared in 2020.
For example, due to COVID lockdowns, the sales figures have gone down in our physical
store. Due to this, there is a sales dropped in general or an increased in online channels.
Likewise, we can understand which of the variable affected the overall performance of
your dependent variable, annual sales.
Neural networks: Neural networks are the foundation of intelligent machine learning
algorithms.
This is a type of data-driven analysis that seeks to understand how the human brain
processes knowledge and predicts value with minimal intervention.
The neural network learns from every data traction. In other words, it evolves over time.
Predictive data analysis is a common application of neural networks. There are business
intelligence reporting tools that implement this feature including Predictive Analytics
tools from datapine.
This tool allows us to quickly and easily make different types of forecasts. Just select the
data to process based on KPI’s and the software will automatically calculate forecasts
based on historical and current data.
It can be managed through an easy-to-use interface for all users in any organization. No
advanced scientist is required.
Factor analysis: Factor analysis, also known as dimensionality reduction, is a type of
data analysis used to account for variations between the observed minimum number of
correlated variables, called factors.
The goal is to find the hidden independent variable. This is an ideal analysis method for
simplifying certain data segments.
A good example of understanding this data analysis method is customer reviews of
products.
The initial evaluation is based on several variables such as color, size, fit, current trends,
material, comfort, where the product is purchased, and how often it is used.
This way we can create endless lists based on what we want to track. In this case, factor
analysis is performed by combining all these variables into homogeneous groups.
Data mining: A method of analysis that is used to describe technical standards and
concepts of values, direction, and context.
6. 6/7
Data mining is performed using exploratory statistical evaluation to identify
dependencies, relationships, data patterns, and trends to gain insights.
Adopting the ideas of data mining when it comes to data analysis is the key to success.
Data pine intelligent data alerts are a perfect example of data mining. It uses artificial
intelligence and machine learning to send automated signals based on specific commands
or events in a data set.
For example, if we are monitoring supply chain KPIs, we can set up a smart alert that fires
when invalid or low-quality data is displayed.
This allows us to better understand the problem and solve it quickly and efficiently
Text analysis: Text analysis, also known in the industry as text mining, is the collection
and organization of large amounts of text data in a manageable way.
Reading all the details of this cleanup process can provide valuable information to help
you extract data that is truly relevant to your business and move forward.
The latest tools and technologies for data analysts accelerate the text analysis process.
The combination of machine learning and intelligent algorithms allows the application of
advanced analytical techniques such as sentiment analysis.
This technique allows you to understand the purpose and sentiment of a text (positive,
negative, neutral, etc.) and rank it according to specific factors and categories related to
your brand.
Sentiment analysis is widely used to track brand and product reputation and to
understand customer success based on experience.
Analyzing data from a variety of text sources, including product reviews, articles, social
media, and survey responses, can provide valuable insights into your audience and their
needs, preferences, and complaints.
Create campaigns, services, and communications that meet the needs of your audience at
an individual level to grow your audience and build customer loyalty. It is one of the most
effective data analysis tools and techniques that anyone can invest in.
7. 7/7
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