- published: 15 Jul 2014
- views: 33328
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.
Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.
Data (/ˈdeɪtə/ DAY-tə, /ˈdætə/ DA-tə, or /ˈdɑːtə/ DAH-tə) is a set of values of qualitative or quantitative variables; restated, pieces of data are individual pieces of information. Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs or images. Data as a general concept refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing.
Raw data, i.e. unprocessed data, is a collection of numbers, characters; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next. Field data is raw data that is collected in an uncontrolled in situ environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording.
The Latin word "data" is the plural of "datum", and still may be used as a plural noun in this sense. Nowadays, though, "data" is most commonly used in the singular, as a mass noun (like "information", "sand" or "rain").
Analysis is the process of breaking a complex topic or substance into smaller parts in order to gain a better understanding of it. The technique has been applied in the study of mathematics and logic since before Aristotle (384–322 B.C.), though analysis as a formal concept is a relatively recent development.
The word comes from the Ancient Greek ἀνάλυσις (analysis, "a breaking up", from ana- "up, throughout" and lysis "a loosening").
As a formal concept, the method has variously been ascribed to Alhazen,René Descartes (Discourse on the Method), and Galileo Galilei. It has also been ascribed to Isaac Newton, in the form of a practical method of physical discovery (which he did not name).
The field of chemistry uses analysis in at least three ways: to identify the components of a particular chemical compound (qualitative analysis), to identify the proportions of components in a mixture (quantitative analysis), and to break down chemical processes and examine chemical reactions between elements of matter. For an example of its use, analysis of the concentration of elements is important in managing a nuclear reactor, so nuclear scientists will analyze neutron activation to develop discrete measurements within vast samples. A matrix can have a considerable effect on the way a chemical analysis is conducted and the quality of its results. Analysis can be done manually or with a device. Chemical analysis is an important element of national security among the major world powers with materials measurement and signature intelligence (MASINT) capabilities.
Here I give an introduction to the course of data exploration (data analysis) and data mining. I also show an example dataset My web page: www.imperial.ac.uk/people/n.sadawi
Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mar...
This talk was given at a local TEDx event, produced independently of the TED Conferences. The amount of information that we are creating is increasing at an incredible speed. But how are we going to manage it? Professor Maria Fasli is based in the School of Computer Science and Electronic Engineering at the University of Essex. She obtained her BSc from the Department of Informatics of T.E.I. Thessaloniki (Greece). She received her PhD from the University of Essex in 2000 having worked under the supervision of Ray Turner in axiomatic systems for intelligent agents. She has previously worked in the area of data mining and machine learning. Her current research interests lie in agents and multi-agent systems and in particular formal theories for reasoning agents, group formation and social ...
Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm;_medium=VM&utm;_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing survey Data • What is business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems ...
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
About the Speaker: Mike Sherman (http://www.mikesherman.net/) has an MBA with High Distinction from Harvard Business School and Bachelors degrees, Magna Cum Laude, from both the Wharton School and College of the University of Pennsylvania. Mike began his career at Procter & Gamble, where he managed both new and established brands. Mike spent 17 years with McKinsey & Company doing marketing consulting, based in New York and Hong Kong. While there he created their Asia-Pacific marketing practice. Mike was also Global Head of Knowledge Management for Synovate (now Ipsos, a global market research firm) , where he lead efforts to improve the value clients obtain from research. He is now leading the SingTel group's efforts to identify how to better leverage its data to serve customers and dri...
The process of doing statistical analysis follows a clearly defined sequence of steps whether the analysis is being done in a formal setting like a medical lab or informally like you would find in a corporate environment. This lecture gives a brief overview of the process.
Lecture Starts at: 8:25 Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation where many of the tools in the analyst's tool belt (e.g., regression) aren't ideal for the task. Despite these challenges, proper analysis of business data represents a fundamental skill required of Business/Data Analysts, Product/Program Managers, and Data Scientists. At this meetup presenter Dave Langer will show how to get started analyzing business data in a robust way using Excel – no programming or statistics required! Dave will cover the following during the presentation: • ...
This week's lecture is an introduction to data analytics. It touches on topics we'll come back to over the coming weeks.
LIMITED TIME - Get The Complete Software Developer's Career Guide for just $0.99 https://simpleprogrammer.com/careerguide-yt SUBSCRIBE TO THIS CHANNEL: vid.io/xokz Inevitable Book: https://simpleprogrammer.com/theinevitable Statistics & Data Analysis: Does It Have A Future? The process of evaluating data using analytical and logical reasoning to examine each component of the data provided is called data analysis or statistics. This form of analysis is just one of the many steps that must be completed when conducting a research experiment. Data from various sources is gathered, reviewed, and then analyzed to form some sort of finding or conclusion. There are a variety of specific data analysis method, some of which include data mining, text analytics, business intelligence, and data vis...
Impact evaluations need to go beyond assessing the size of the effects (i.e., the average impact) to identify for whom and in what ways a programme or policy has been successful. This video provides an overview of the issues involved in choosing and using data collection and analysis methods for impact evaluations
Data Science is the combination of statistics, mathematics, programming, problem solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing, and aligning the data. Complete Video English - https://goo.gl/WJfPeq Complete Video Tamil - https://goo.gl/kaWumR YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English
Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 5. Bradley EH, Curry LA, Devers K. Qualitative data analysis for health services research: Developing taxonomy, themes, and theory. Health Services Research, 2007; 42(4):1758-1772. Learn more about Dr. Leslie Curry http://publichealth.yale.edu/people/leslie_curry....
Data Interpretation, a tool that is required for day to day decisions for us, is also a key area which is tested for selecting fresh talent in various organization, specifically Banks. State Bank of India's Probationary Officers (SBI PO) Test largely focuses on this area. In fact, The quant section of SBI PO is actually labelled as Data Interpretation & Analysis truly reflecting the importance of this skill in their selection process. With the SBI PO exam round the corner, many of the aspirants have requested for covering this topic in our webcast. Hence, this is being taken up on a top priority basis. In this session by Rohit Agarwal, you will learn those concepts of Percentages, Ratio and Proportions and Averages which form the basics of Data Interpretation and will be used extensively...
Use simple data analysis techniques in SPSS to analyze survey questions.
Here I give an introduction to the course of data exploration (data analysis) and data mining. I also show an example dataset My web page: www.imperial.ac.uk/people/n.sadawi
Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mar...
This talk was given at a local TEDx event, produced independently of the TED Conferences. The amount of information that we are creating is increasing at an incredible speed. But how are we going to manage it? Professor Maria Fasli is based in the School of Computer Science and Electronic Engineering at the University of Essex. She obtained her BSc from the Department of Informatics of T.E.I. Thessaloniki (Greece). She received her PhD from the University of Essex in 2000 having worked under the supervision of Ray Turner in axiomatic systems for intelligent agents. She has previously worked in the area of data mining and machine learning. Her current research interests lie in agents and multi-agent systems and in particular formal theories for reasoning agents, group formation and social ...
Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm;_medium=VM&utm;_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing survey Data • What is business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems ...
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
About the Speaker: Mike Sherman (http://www.mikesherman.net/) has an MBA with High Distinction from Harvard Business School and Bachelors degrees, Magna Cum Laude, from both the Wharton School and College of the University of Pennsylvania. Mike began his career at Procter & Gamble, where he managed both new and established brands. Mike spent 17 years with McKinsey & Company doing marketing consulting, based in New York and Hong Kong. While there he created their Asia-Pacific marketing practice. Mike was also Global Head of Knowledge Management for Synovate (now Ipsos, a global market research firm) , where he lead efforts to improve the value clients obtain from research. He is now leading the SingTel group's efforts to identify how to better leverage its data to serve customers and dri...
The process of doing statistical analysis follows a clearly defined sequence of steps whether the analysis is being done in a formal setting like a medical lab or informally like you would find in a corporate environment. This lecture gives a brief overview of the process.
Lecture Starts at: 8:25 Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation where many of the tools in the analyst's tool belt (e.g., regression) aren't ideal for the task. Despite these challenges, proper analysis of business data represents a fundamental skill required of Business/Data Analysts, Product/Program Managers, and Data Scientists. At this meetup presenter Dave Langer will show how to get started analyzing business data in a robust way using Excel – no programming or statistics required! Dave will cover the following during the presentation: • ...
This week's lecture is an introduction to data analytics. It touches on topics we'll come back to over the coming weeks.
LIMITED TIME - Get The Complete Software Developer's Career Guide for just $0.99 https://simpleprogrammer.com/careerguide-yt SUBSCRIBE TO THIS CHANNEL: vid.io/xokz Inevitable Book: https://simpleprogrammer.com/theinevitable Statistics & Data Analysis: Does It Have A Future? The process of evaluating data using analytical and logical reasoning to examine each component of the data provided is called data analysis or statistics. This form of analysis is just one of the many steps that must be completed when conducting a research experiment. Data from various sources is gathered, reviewed, and then analyzed to form some sort of finding or conclusion. There are a variety of specific data analysis method, some of which include data mining, text analytics, business intelligence, and data vis...
Impact evaluations need to go beyond assessing the size of the effects (i.e., the average impact) to identify for whom and in what ways a programme or policy has been successful. This video provides an overview of the issues involved in choosing and using data collection and analysis methods for impact evaluations
Data Science is the combination of statistics, mathematics, programming, problem solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing, and aligning the data. Complete Video English - https://goo.gl/WJfPeq Complete Video Tamil - https://goo.gl/kaWumR YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English
Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 5. Bradley EH, Curry LA, Devers K. Qualitative data analysis for health services research: Developing taxonomy, themes, and theory. Health Services Research, 2007; 42(4):1758-1772. Learn more about Dr. Leslie Curry http://publichealth.yale.edu/people/leslie_curry....
Data Interpretation, a tool that is required for day to day decisions for us, is also a key area which is tested for selecting fresh talent in various organization, specifically Banks. State Bank of India's Probationary Officers (SBI PO) Test largely focuses on this area. In fact, The quant section of SBI PO is actually labelled as Data Interpretation & Analysis truly reflecting the importance of this skill in their selection process. With the SBI PO exam round the corner, many of the aspirants have requested for covering this topic in our webcast. Hence, this is being taken up on a top priority basis. In this session by Rohit Agarwal, you will learn those concepts of Percentages, Ratio and Proportions and Averages which form the basics of Data Interpretation and will be used extensively...
Use simple data analysis techniques in SPSS to analyze survey questions.
Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mar...
Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm;_medium=VM&utm;_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing survey Data • What is business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems ...
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
About the Speaker: Mike Sherman (http://www.mikesherman.net/) has an MBA with High Distinction from Harvard Business School and Bachelors degrees, Magna Cum Laude, from both the Wharton School and College of the University of Pennsylvania. Mike began his career at Procter & Gamble, where he managed both new and established brands. Mike spent 17 years with McKinsey & Company doing marketing consulting, based in New York and Hong Kong. While there he created their Asia-Pacific marketing practice. Mike was also Global Head of Knowledge Management for Synovate (now Ipsos, a global market research firm) , where he lead efforts to improve the value clients obtain from research. He is now leading the SingTel group's efforts to identify how to better leverage its data to serve customers and dri...
Lecture Starts at: 8:25 Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation where many of the tools in the analyst's tool belt (e.g., regression) aren't ideal for the task. Despite these challenges, proper analysis of business data represents a fundamental skill required of Business/Data Analysts, Product/Program Managers, and Data Scientists. At this meetup presenter Dave Langer will show how to get started analyzing business data in a robust way using Excel – no programming or statistics required! Dave will cover the following during the presentation: • ...
This week's lecture is an introduction to data analytics. It touches on topics we'll come back to over the coming weeks.
Data Interpretation, a tool that is required for day to day decisions for us, is also a key area which is tested for selecting fresh talent in various organization, specifically Banks. State Bank of India's Probationary Officers (SBI PO) Test largely focuses on this area. In fact, The quant section of SBI PO is actually labelled as Data Interpretation & Analysis truly reflecting the importance of this skill in their selection process. With the SBI PO exam round the corner, many of the aspirants have requested for covering this topic in our webcast. Hence, this is being taken up on a top priority basis. In this session by Rohit Agarwal, you will learn those concepts of Percentages, Ratio and Proportions and Averages which form the basics of Data Interpretation and will be used extensively...
A screencast of a seminar I gave at McGill University in March 2015. Materials including an expanded self-learning slideshow and code can be found at http://www.dtdata.io/introml.
This popular webinar, hosted by Capacity for Health on April 18, 2012, is an introduction to the remainder of the data management and analysis series. For people newer to the field of evaluation, or newer to the tasks of data management and analysis, this one-hour webinar will teach the basic skills and tools needed to manage and analyze program data, with a special emphasis on analyzing program outcome data. Participants do not need any special skills; no special software or math skills needed! You will learn about: organizing data into a spreadsheet format, how to perform basic quality assurance with your data, simple mathematical functions for data analysis (frequencies, median, mode, mean and basic comparisons), how to create new evaluation questions out of your data, and how to inter...
Supervised and unsupervised learning algorithms
There are audio issues with this video that cannot be fixed. We recommend listening to the tutorial without headphones to minimize the buzzing sound. Tutorial information may be found at https://scipy2017.scipy.org/ehome/220975/493423/ Data Science and Machine learning have been synonymous with languages like Python. Libraries like numpy and Pandas have become the de facto standard when working with data. The DataFrame object provided by Pandas gives us the ability to work with heterogeneous unstructured data that is commonly used in "real world" data. New learners are often drawn to Python and Pandas because of all the different and exciting types of models and insights the language can do and provide, but are awestruck when faced with the initial learning curve. This tutorial ai...
This webinar highlights how MATLAB can work with Excel. Get a Free MATLAB Trial: https://goo.gl/C2Y9A5 Ready to Buy: https://goo.gl/vsIeA5 Learn MATLAB for Free: https://goo.gl/xIiHyG Many technical professionals find that they run into limitations using Excel for their data analysis applications. This webinar highlights how MATLAB can supplement the capabilities of Excel by providing access to thousands of pre-built engineering and advanced analysis functions and versatile visualization tools. Learn more about using MATLAB with Excel: http://goo.gl/3vkFMW Learn more about MATLAB: http://goo.gl/YKadxi Through product demonstrations you will see how to: • Access data from spreadsheets • Plot data and customize figures • Perform statistical analysis and fitting • Automatically gen...
Presented by James Malone, Sports Scientist
Tutorial materials found here: https://scipy2017.scipy.org/ehome/220975/493423/ This tutorial teaches the fundamentals of parallel programming in Python. It focuses on covering a few programming techniques rather than diving into one framework or tool in particular. Student Goals ------- Students will walk away with a high-level understanding of both parallel problems and how to reason about parallel computing frameworks. They will also walk away with hands-on experience using a variety of frameworks easily accessible from Python.
Part 2 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
This one-hour webinar will teach the basic skills and tools needed to manage and analyze program data, with a special emphasis on analyzing program outcome data. Participants do not need any special skills; no special software or math skills needed! You will learn about: organizing data into a spreadsheet format, how to perform basic quality assurance with your data, simple mathematical functions for data analysis, how to create new evaluation questions out of your data, and how to interpret data including attention to the context of the data. By the end of this webinar, participants will: * Understand the importance of checking data before it is analyzed * Understand how to organize data into a spreadsheet * Be able to use three methods to analyze data (frequencies, centers...
This session describes and demonstrates how to create a big data analytics solution with structured data by using Microsoft HDInsight, Excel 2013 and Microsoft Office 365. This session will be of interest to those new to the concept of big data, new to self-service data modelling with PowerPivot, and for those interested to understand what is new for PowerPivot and data analysis in Excel 2013.