- published: 19 Apr 2012
- views: 54332
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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 drive revenues and profits. This includes both traditional customer and telco data as well as emerging digital data.
Mike has spoken at many Esomar conferences and published several times in the McKinsey Quarterly on marketing issues in developing and Asian markets. He is the former Board Chair of AFS-USA, a leading high school foreign exchange organization and an avid traveler, having visited over 100 countries.
Synopsis:
10 Things I Wish Someone Had Told Me About Data Analysis
Mike will share 10 thoughts about different aspects of data analysis that he has learned during his more than 25 years in this field of customer data analysis.
Mike has extensive experience as an end user of analysis (while at P&G; as a brand manager and now at SingTel in a group role focusing on customer knowledge), as a provider of market research output (while at Synovate [now ipsos]), and as an intermediary (while leading the Asia-Pacific marketing practice at McKinsey). That experience has taught him that too often discussions of analysis focus on the tools of the trade rather than the needs of the end user.
In his talk he will discuss examples of insightful output done with basic tools, instances where more sophisticated approaches do provide high impact and some, often funny, misuses of analysis. He promises that you'll leave the room with practical knowledge you can apply in your studies and work.
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 mark)
14. More than one calculation ( 28:45 min mark)
15. Value Field Settings (32:36 min mark)
16. Grouping Numbers ( 33:24 min mark)
17. Filter in a Pivot Table ( 35:45 min mark)
18. Slicers ( 37:09 min mark)
Charts:
19. Column Charts from Pivot Tables ( 38:37 min mark)
Formulas:
20. SUMIFS ( 42:17 min mark)
21. Data Analysis Formula or PivotTables? ( 45:11 min mark)
22. COUNTIF ( 46:12 min mark)
23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark)
Getting Data Into Excel
24. Import from CSV file ( 51:21 min mark)
25. Import from Access ( 54:00 min mark)
Highline Community College Professional Development Day 2012
- published: 19 Oct 2012
- views: 839440
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
- published: 15 Jul 2014
- views: 7483
Part 1 in a in-depth hands-on series of videos introducing the viewer to Data Science using R. The video series illustrates the complete Data Mining project lifecycle via Kaggle's Titanic 101 competition. All source code from videos are available from GitHub at: https://github.com/EasyD/IntroToDataScience. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo.
- published: 09 Nov 2014
- views: 198048
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.profile
Learn more about the Yale Global Health Leadership Institute
http://ghli.yale.edu
- published: 24 Jun 2015
- views: 29114
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.
- published: 04 Apr 2015
- views: 25165
Pandas is a Python module, and Python is the programming language that we're going to use. The Pandas module is a high performance, highly efficient, and high level data analysis library.
At its core, it is very much like operating a headless version of a spreadsheet, like Excel. Most of the datasets you work with will be what are called dataframes. You may be familiar with this term already, it is used across other languages, but, if not, a dataframe is most often just like a spreadsheet. Columns and rows, that's all there is to it! From here, we can utilize Pandas to perform operations on our data sets at lightning speeds.
Sample code: http://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/
Pip install tutorial: http://pythonprogramming.net/using-pip-install-for-python-modules/
Matplotlib series starts here: http://pythonprogramming.net/matplotlib-intro-tutorial/
- published: 14 Sep 2015
- views: 40487
See http://simplystatistics.org/, https://www.coursera.org/course/dataanalysis or for more information
- published: 10 Jan 2013
- views: 28850
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn about how to automatically creates statistics for Linear Regression using the Data Analysis Regression feature. See how to automatically create statistics such as: Correlation, R Squared, Standard Error, Slope, Intercept, SST, SSR, SSE, F Test, Test Statistic, t Test Statistics, p-values, predicted vales, residuals, Residual plots and more.
Install: File, Options, Add-ins, Data Analysis Toolpak.
Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
- published: 26 Nov 2015
- views: 4931
Data analysis
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.
This page contains text from Wikipedia, the Free Encyclopedia -
http://wn.com/Data_analysis
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
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48:45Data Analysis - 10 Things I Wish Someone Had Told Me about Data Analysis
Data Analysis - 10 Things I Wish Someone Had Told Me about Data AnalysisData Analysis - 10 Things I Wish Someone Had Told Me about Data Analysis
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 drive revenues and profits. This includes both traditional customer and telco data as well as emerging digital data. Mike has spoken at many Esomar conferences and published several times in the McKinsey Quarterly on marketing issues in developing and Asian markets. He is the former Board Chair of AFS-USA, a leading high school foreign exchange organization and an avid traveler, having visited over 100 countries. Synopsis: 10 Things I Wish Someone Had Told Me About Data Analysis Mike will share 10 thoughts about different aspects of data analysis that he has learned during his more than 25 years in this field of customer data analysis. Mike has extensive experience as an end user of analysis (while at P&G; as a brand manager and now at SingTel in a group role focusing on customer knowledge), as a provider of market research output (while at Synovate [now ipsos]), and as an intermediary (while leading the Asia-Pacific marketing practice at McKinsey). That experience has taught him that too often discussions of analysis focus on the tools of the trade rather than the needs of the end user. In his talk he will discuss examples of insightful output done with basic tools, instances where more sophisticated approaches do provide high impact and some, often funny, misuses of analysis. He promises that you'll leave the room with practical knowledge you can apply in your studies and work. -
55:13Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
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 mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 -
9:50Introduction to Data Analysis and Mining: what is it?
Introduction to Data Analysis and Mining: what is it?Introduction to Data Analysis and Mining: what is it?
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 -
81:50Introduction to Data Science with R - Data Analysis Part 1
Introduction to Data Science with R - Data Analysis Part 1Introduction to Data Science with R - Data Analysis Part 1
Part 1 in a in-depth hands-on series of videos introducing the viewer to Data Science using R. The video series illustrates the complete Data Mining project lifecycle via Kaggle's Titanic 101 competition. All source code from videos are available from GitHub at: https://github.com/EasyD/IntroToDataScience. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. -
18:32Introduction to Quantitative Data Analysis
Introduction to Quantitative Data AnalysisIntroduction to Quantitative Data Analysis
-
17:12Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
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.profile Learn more about the Yale Global Health Leadership Institute http://ghli.yale.edu -
68:30Introduction to Data Analysis using Machine Learning
Introduction to Data Analysis using Machine LearningIntroduction to Data Analysis using Machine Learning
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. -
10:26Data Analysis with Python and Pandas Tutorial Introduction
Data Analysis with Python and Pandas Tutorial IntroductionData Analysis with Python and Pandas Tutorial Introduction
Pandas is a Python module, and Python is the programming language that we're going to use. The Pandas module is a high performance, highly efficient, and high level data analysis library. At its core, it is very much like operating a headless version of a spreadsheet, like Excel. Most of the datasets you work with will be what are called dataframes. You may be familiar with this term already, it is used across other languages, but, if not, a dataframe is most often just like a spreadsheet. Columns and rows, that's all there is to it! From here, we can utilize Pandas to perform operations on our data sets at lightning speeds. Sample code: http://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/ Pip install tutorial: http://pythonprogramming.net/using-pip-install-for-python-modules/ Matplotlib series starts here: http://pythonprogramming.net/matplotlib-intro-tutorial/ -
4:03The Landscape of Data Analysis
The Landscape of Data AnalysisThe Landscape of Data Analysis
See http://simplystatistics.org/, https://www.coursera.org/course/dataanalysis or for more information -
6:37Basic Excel Business Analytics #48: Data Analysis Regression feature
Basic Excel Business Analytics #48: Data Analysis Regression featureBasic Excel Business Analytics #48: Data Analysis Regression feature
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn about how to automatically creates statistics for Linear Regression using the Data Analysis Regression feature. See how to automatically create statistics such as: Correlation, R Squared, Standard Error, Slope, Intercept, SST, SSR, SSE, F Test, Test Statistic, t Test Statistics, p-values, predicted vales, residuals, Residual plots and more. Install: File, Options, Add-ins, Data Analysis Toolpak. Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
-
Data Analysis - 10 Things I Wish Someone Had Told Me about Data Analysis
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... -
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
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... -
Introduction to Data Analysis and Mining: what is it?
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 -
Introduction to Data Science with R - Data Analysis Part 1
Part 1 in a in-depth hands-on series of videos introducing the viewer to Data Science using R. The video series illustrates the complete Data Mining project lifecycle via Kaggle's Titanic 101 competition. All source code from videos are available from GitHub at: https://github.com/EasyD/IntroToDataScience. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. -
Introduction to Quantitative Data Analysis
-
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
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.... -
Introduction to Data Analysis using Machine Learning
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. -
Data Analysis with Python and Pandas Tutorial Introduction
Pandas is a Python module, and Python is the programming language that we're going to use. The Pandas module is a high performance, highly efficient, and high level data analysis library. At its core, it is very much like operating a headless version of a spreadsheet, like Excel. Most of the datasets you work with will be what are called dataframes. You may be familiar with this term already, it is used across other languages, but, if not, a dataframe is most often just like a spreadsheet. Columns and rows, that's all there is to it! From here, we can utilize Pandas to perform operations on our data sets at lightning speeds. Sample code: http://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/ Pip install tutorial: http://pythonprogramming.net/using-pip-install-fo... -
The Landscape of Data Analysis
See http://simplystatistics.org/, https://www.coursera.org/course/dataanalysis or for more information -
Basic Excel Business Analytics #48: Data Analysis Regression feature
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn about how to automatically creates statistics for Linear Regression using the Data Analysis Regression feature. See how to automatically create statistics such as: Correlation, R Squared, Standard Error, Slope, Intercept, SST, SSR, SSE, F Test, Test Statistic, t Test Statistics, p-values, predicted vales, residuals, Residual plots and more. Install: File, Options, Add-ins, Data Analysis Toolpak. Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
Data Analysis - 10 Things I Wish Someone Had Told Me about Data Analysis
- Order: Reorder
- Duration: 48:45
- Updated: 19 Apr 2012
- views: 54332
About the Speaker: Mike Sherman (http://www.mikesherman.net/) has an MBA with High Distinction from Harvard Business School and Bachelors degrees, Magna Cum La...
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 drive revenues and profits. This includes both traditional customer and telco data as well as emerging digital data.
Mike has spoken at many Esomar conferences and published several times in the McKinsey Quarterly on marketing issues in developing and Asian markets. He is the former Board Chair of AFS-USA, a leading high school foreign exchange organization and an avid traveler, having visited over 100 countries.
Synopsis:
10 Things I Wish Someone Had Told Me About Data Analysis
Mike will share 10 thoughts about different aspects of data analysis that he has learned during his more than 25 years in this field of customer data analysis.
Mike has extensive experience as an end user of analysis (while at P&G; as a brand manager and now at SingTel in a group role focusing on customer knowledge), as a provider of market research output (while at Synovate [now ipsos]), and as an intermediary (while leading the Asia-Pacific marketing practice at McKinsey). That experience has taught him that too often discussions of analysis focus on the tools of the trade rather than the needs of the end user.
In his talk he will discuss examples of insightful output done with basic tools, instances where more sophisticated approaches do provide high impact and some, often funny, misuses of analysis. He promises that you'll leave the room with practical knowledge you can apply in your studies and work.
wn.com/Data Analysis 10 Things I Wish Someone Had Told Me About Data Analysis
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 drive revenues and profits. This includes both traditional customer and telco data as well as emerging digital data.
Mike has spoken at many Esomar conferences and published several times in the McKinsey Quarterly on marketing issues in developing and Asian markets. He is the former Board Chair of AFS-USA, a leading high school foreign exchange organization and an avid traveler, having visited over 100 countries.
Synopsis:
10 Things I Wish Someone Had Told Me About Data Analysis
Mike will share 10 thoughts about different aspects of data analysis that he has learned during his more than 25 years in this field of customer data analysis.
Mike has extensive experience as an end user of analysis (while at P&G; as a brand manager and now at SingTel in a group role focusing on customer knowledge), as a provider of market research output (while at Synovate [now ipsos]), and as an intermediary (while leading the Asia-Pacific marketing practice at McKinsey). That experience has taught him that too often discussions of analysis focus on the tools of the trade rather than the needs of the end user.
In his talk he will discuss examples of insightful output done with basic tools, instances where more sophisticated approaches do provide high impact and some, often funny, misuses of analysis. He promises that you'll leave the room with practical knowledge you can apply in your studies and work.
- published: 19 Apr 2012
- views: 54332
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
- Order: Reorder
- Duration: 55:13
- Updated: 19 Oct 2012
- views: 839440
Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm
Learn the basics of Data Analysis at Highline Community College Professional Development Da...
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 mark)
14. More than one calculation ( 28:45 min mark)
15. Value Field Settings (32:36 min mark)
16. Grouping Numbers ( 33:24 min mark)
17. Filter in a Pivot Table ( 35:45 min mark)
18. Slicers ( 37:09 min mark)
Charts:
19. Column Charts from Pivot Tables ( 38:37 min mark)
Formulas:
20. SUMIFS ( 42:17 min mark)
21. Data Analysis Formula or PivotTables? ( 45:11 min mark)
22. COUNTIF ( 46:12 min mark)
23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark)
Getting Data Into Excel
24. Import from CSV file ( 51:21 min mark)
25. Import from Access ( 54:00 min mark)
Highline Community College Professional Development Day 2012
wn.com/Excel Data Analysis Sort, Filter, Pivottable, Formulas (25 Examples) Hcc Professional Day 2012
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 mark)
14. More than one calculation ( 28:45 min mark)
15. Value Field Settings (32:36 min mark)
16. Grouping Numbers ( 33:24 min mark)
17. Filter in a Pivot Table ( 35:45 min mark)
18. Slicers ( 37:09 min mark)
Charts:
19. Column Charts from Pivot Tables ( 38:37 min mark)
Formulas:
20. SUMIFS ( 42:17 min mark)
21. Data Analysis Formula or PivotTables? ( 45:11 min mark)
22. COUNTIF ( 46:12 min mark)
23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark)
Getting Data Into Excel
24. Import from CSV file ( 51:21 min mark)
25. Import from Access ( 54:00 min mark)
Highline Community College Professional Development Day 2012
- published: 19 Oct 2012
- views: 839440
Introduction to Data Analysis and Mining: what is it?
- Order: Reorder
- Duration: 9:50
- Updated: 15 Jul 2014
- views: 7483
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/pe...
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
wn.com/Introduction To Data Analysis And Mining What Is It
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
- published: 15 Jul 2014
- views: 7483
Introduction to Data Science with R - Data Analysis Part 1
- Order: Reorder
- Duration: 81:50
- Updated: 09 Nov 2014
- views: 198048
Part 1 in a in-depth hands-on series of videos introducing the viewer to Data Science using R. The video series illustrates the complete Data Mining project lif...
Part 1 in a in-depth hands-on series of videos introducing the viewer to Data Science using R. The video series illustrates the complete Data Mining project lifecycle via Kaggle's Titanic 101 competition. All source code from videos are available from GitHub at: https://github.com/EasyD/IntroToDataScience. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo.
wn.com/Introduction To Data Science With R Data Analysis Part 1
Part 1 in a in-depth hands-on series of videos introducing the viewer to Data Science using R. The video series illustrates the complete Data Mining project lifecycle via Kaggle's Titanic 101 competition. All source code from videos are available from GitHub at: https://github.com/EasyD/IntroToDataScience. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo.
- published: 09 Nov 2014
- views: 198048
Introduction to Quantitative Data Analysis
- Order: Reorder
- Duration: 18:32
- Updated: 24 Oct 2014
- views: 19878
- published: 24 Oct 2014
- views: 19878
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
- Order: Reorder
- Duration: 17:12
- Updated: 24 Jun 2015
- views: 29114
Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Le...
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.profile
Learn more about the Yale Global Health Leadership Institute
http://ghli.yale.edu
wn.com/Fundamentals Of Qualitative Research Methods Data Analysis (Module 5)
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.profile
Learn more about the Yale Global Health Leadership Institute
http://ghli.yale.edu
- published: 24 Jun 2015
- views: 29114
Introduction to Data Analysis using Machine Learning
- Order: Reorder
- Duration: 68:30
- Updated: 04 Apr 2015
- views: 25165
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...
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.
wn.com/Introduction To Data Analysis Using Machine Learning
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.
- published: 04 Apr 2015
- views: 25165
Data Analysis with Python and Pandas Tutorial Introduction
- Order: Reorder
- Duration: 10:26
- Updated: 14 Sep 2015
- views: 40487
Pandas is a Python module, and Python is the programming language that we're going to use. The Pandas module is a high performance, highly efficient, and high l...
Pandas is a Python module, and Python is the programming language that we're going to use. The Pandas module is a high performance, highly efficient, and high level data analysis library.
At its core, it is very much like operating a headless version of a spreadsheet, like Excel. Most of the datasets you work with will be what are called dataframes. You may be familiar with this term already, it is used across other languages, but, if not, a dataframe is most often just like a spreadsheet. Columns and rows, that's all there is to it! From here, we can utilize Pandas to perform operations on our data sets at lightning speeds.
Sample code: http://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/
Pip install tutorial: http://pythonprogramming.net/using-pip-install-for-python-modules/
Matplotlib series starts here: http://pythonprogramming.net/matplotlib-intro-tutorial/
wn.com/Data Analysis With Python And Pandas Tutorial Introduction
Pandas is a Python module, and Python is the programming language that we're going to use. The Pandas module is a high performance, highly efficient, and high level data analysis library.
At its core, it is very much like operating a headless version of a spreadsheet, like Excel. Most of the datasets you work with will be what are called dataframes. You may be familiar with this term already, it is used across other languages, but, if not, a dataframe is most often just like a spreadsheet. Columns and rows, that's all there is to it! From here, we can utilize Pandas to perform operations on our data sets at lightning speeds.
Sample code: http://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/
Pip install tutorial: http://pythonprogramming.net/using-pip-install-for-python-modules/
Matplotlib series starts here: http://pythonprogramming.net/matplotlib-intro-tutorial/
- published: 14 Sep 2015
- views: 40487
The Landscape of Data Analysis
- Order: Reorder
- Duration: 4:03
- Updated: 10 Jan 2013
- views: 28850
See http://simplystatistics.org/, https://www.coursera.org/course/dataanalysis or for more information
See http://simplystatistics.org/, https://www.coursera.org/course/dataanalysis or for more information
wn.com/The Landscape Of Data Analysis
See http://simplystatistics.org/, https://www.coursera.org/course/dataanalysis or for more information
- published: 10 Jan 2013
- views: 28850
Basic Excel Business Analytics #48: Data Analysis Regression feature
- Order: Reorder
- Duration: 6:37
- Updated: 26 Nov 2015
- views: 4931
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn about how to automatically creates statistics for L...
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn about how to automatically creates statistics for Linear Regression using the Data Analysis Regression feature. See how to automatically create statistics such as: Correlation, R Squared, Standard Error, Slope, Intercept, SST, SSR, SSE, F Test, Test Statistic, t Test Statistics, p-values, predicted vales, residuals, Residual plots and more.
Install: File, Options, Add-ins, Data Analysis Toolpak.
Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
wn.com/Basic Excel Business Analytics 48 Data Analysis Regression Feature
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn about how to automatically creates statistics for Linear Regression using the Data Analysis Regression feature. See how to automatically create statistics such as: Correlation, R Squared, Standard Error, Slope, Intercept, SST, SSR, SSE, F Test, Test Statistic, t Test Statistics, p-values, predicted vales, residuals, Residual plots and more.
Install: File, Options, Add-ins, Data Analysis Toolpak.
Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
- published: 26 Nov 2015
- views: 4931
-
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
Download files: http://people.highline.edu/mgirvin/excelisfun.htm. Intro To Excel: Store Raw Data, Data Types, Data Analysis, Formulas, PivotTables, Charts, Keyboards, Number Formatting, Data Analysis & More: (00:08) Introduction to class (00:49) Cells, Worksheets, Sheet Tabs, Naming Sheets, Workbooks, File Names (02:54) Navigating Worksheets & Workbook (03:58) Navigation Keys (04:15) Keyboard Ctrl + PageUp/PageDown moves Active Sheet (05:40) Ribbon Tabs (06:25) Add buttons to Quick Access Tool Bar (07:40) What Excel does: Store Raw Data, Make Calculations, Data Analysis and Charting (08:55) Introduction to Data Analysis (10:37) Data Types in Excel: Text, Numbers, Boolean, Errors, Empty Cells (11:16) Keyboard Enter puts content in cell and move selected cell down (13:00) Data Type DEFAULT ... -
WEBINAR: Basic Data 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, 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... -
Data Analysis with MATLAB for Excel Users
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. Through product demonstrations you will see how to: • Access data from spreadsheets • Plot data and customize figures • Perform statistical analysis and fitting • Automatically generate reports to document your analysis • Freely distribute your MATLAB functions as Excel add-ins This webinar will show new features from the latest versions of MATLAB including new data types to store and manage data commonly found in spreadsheets. Previous knowledge of MATLAB is not required. ... -
-
WEBINAR: Introduction to Basic Data Analysis
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... -
Big Data Analytics with Microsoft Excel 2013
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.
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
- Order: Reorder
- Duration: 142:43
- Updated: 19 Dec 2014
- views: 203319
Download files: http://people.highline.edu/mgirvin/excelisfun.htm.
Intro To Excel: Store Raw Data, Data Types, Data Analysis, Formulas, PivotTables, Charts, Key...
Download files: http://people.highline.edu/mgirvin/excelisfun.htm.
Intro To Excel: Store Raw Data, Data Types, Data Analysis, Formulas, PivotTables, Charts, Keyboards, Number Formatting, Data Analysis & More:
(00:08) Introduction to class
(00:49) Cells, Worksheets, Sheet Tabs, Naming Sheets, Workbooks, File Names
(02:54) Navigating Worksheets & Workbook
(03:58) Navigation Keys
(04:15) Keyboard Ctrl + PageUp/PageDown moves Active Sheet
(05:40) Ribbon Tabs
(06:25) Add buttons to Quick Access Tool Bar
(07:40) What Excel does: Store Raw Data, Make Calculations, Data Analysis and Charting
(08:55) Introduction to Data Analysis
(10:37) Data Types in Excel: Text, Numbers, Boolean, Errors, Empty Cells
(11:16) Keyboard Enter puts content in cell and move selected cell down
(13:00) Data Type DEFAULT Alignments
(13:11) First Formula. Entering Cell References in formulas
(13:35) Keyboard Ctrl + Enter puts content in cell and keep cell selected
(14:45) Why we don’t override DEFAULT Alignments
(15:05) Keyboard Ctrl + Z is Undo
(17:05) Proper Data Sets & Raw Data
(24:21) How To Enter Data & Data Labels into an Excel Spreadsheet
(24:21) Stylistic Formatting
(26:35) AVERAGE Function
(27:31) Format Formulas Differently than Raw Data
(28:30) Keyboard Ctrl + C is Copy. Keyboard Ctrl + V is Paste
(29:59) Use Eraser remove Formatting Only
(29:19) Keyboard Ctrl + B adds Bold
(29:57) Excel’s Golden Rule
(31:43) Keyboard F2 puts cell in Edit Mode
(32:01) Violating Excel’s Golden Rule
(34:12) Use Arrow Keys to put cell references in formulas
(35:40) Full Discussion about Formulas & Formulas Elements
(37:22) SUM function Keyboard is Alt + =
(38:22) Aggregate functions
(38:50) Why we use ranges in functions
(40:56) COUNT & COUNTA functions
(42:47) Edit Formula & change cell references
(44:18) Absolute & Relative Cell References
(45:52) Use Delete Key, Not Right-click Delete
(46:40) Use Fill Handle & Angry Rabbit to copy formula
(47:41) Keyboard F4 Locks Cell Reference (make Absolute)
(49:45) Keyboard Tab puts content in Cell and move selected Cell to right
(50:55) Order of Operation error
(52:17) Range Finder to find formula errors
(52:34) Lock Cell Reference after you put cell in Edit Mode
(53:58) Quickly copy an edited formula down a column
(53:07) F2 key in last cell to find formula errors
(54:15) Fix incorrect range in function
(54:55) SQRT function & Fractional Exponents
(57:20) STDEV.P function
(58:10) Navigate Large Data Sets
(58:48) Keyboard Ctrl + Arrow jumps to bottom of data set (Current Range)
(59:42) Keyboard Ctrl + Shift + Arrow selects to bottom of data set (Current Range)
(01:01:41) Keyboard Shift + Enter puts content in Cell and move selected Cell up
(01:02:55) Counting with conditions or criteria: COUNTIFS function
(01:03:43) Keyboard Ctrl + Backspace jumps back to Active Cell
(01:05:31) Counting between an upper & lower limit with COUNTIFS
(01:07:36) COUNTIFS copied down column
(01:10:08) Joining Comparative Operator with Cell Reference in formula
(01:12:50) Data Analysis features in Excel
(01:13:44) Sorting in Excel
(01:16:59) Filtering in Excel
(01:20:39) Introduction to PivotTables
(01:23:39) Create PivotTable dialog box
(01:24:33) Dragging & dropping Fields to create PivotTable
(01:25:31) Dragging Field to Row area creates a Unique List
(01:26:17) Outline/Tabular Layout
(01:27:00) Value Field Settings dialog to change: Number Formatting, Function, Name
(01:28:12) 2nd & 3rd PivotTable examples
(01:31:23) What is a Cross Tabulated Report?
(01:33:04) Create Cross Tabulated Report w PivotTable
(01:35:05) Show PivotTable Field List
(01:36:48) How to Pivot the Report
(01:37:50) Summarize Survey Data with PivotTable.
(01:38:34) Keyboard Alt, N, V opens PivotTable dialog box
(01:41:38) PivotTable with 3 calculations: COUNT, MAX & MIN
(01:43:25) Count & Count Number calculations in a PivotTable
(01:45:30) Excel 2013 Charts to Visually Articulate Quantitative Data
(01:47:00) #1 Rule for Charts: No Chart Junk!
(01:47:30) Explain chart types: Column, Bar, Pie, Line and X-Y Scatter Chart
(01:51:34) Create Column Chart using Recommended Chart feature
(01:53:00) Remove Field Buttons from Pivot Chart
(01:54:10) Chart Formatting Task Pane
(01:54:45) Vary Fill Color by point
(01:55:15) Format Axis with Numbers by Formatting Source Data in PivotTable
(01:56:02) Add Data Labels to Chart
(01:57:28) Copy Chart & Create Bar Chart
(01:57:48) Change Chart Type
(01:58:15) Change Gap Width.
(01:59:17) Create Pie Chart
(01:59:23) Do NOT use 3-D Pie
(01:59:42) Add % Data Labels to Pie Chart
(02:00:25) Create Line Chart From PivotTable
(02:01:20) Link Chart Tile to Cell
(02:02:20) Move a Chart
(02:02:33) Create an X-Y Scatter Chart
(02:03:35) Add Axis Labels
(02:05:27) Number Formatting to help save time
(02:07:24) Number Formatting is a Façade
(02:10:27) General Number Format
(02:10:52) Percentage Number Formatting
(02:14:03) Don’t Multiply Relative Frequency by 100
(02:17:27) Formula for % Change & End Amount
wn.com/Excel 2013 Statistical Analysis 01 Using Excel Efficiently For Statistical Analysis (100 Examples)
Download files: http://people.highline.edu/mgirvin/excelisfun.htm.
Intro To Excel: Store Raw Data, Data Types, Data Analysis, Formulas, PivotTables, Charts, Keyboards, Number Formatting, Data Analysis & More:
(00:08) Introduction to class
(00:49) Cells, Worksheets, Sheet Tabs, Naming Sheets, Workbooks, File Names
(02:54) Navigating Worksheets & Workbook
(03:58) Navigation Keys
(04:15) Keyboard Ctrl + PageUp/PageDown moves Active Sheet
(05:40) Ribbon Tabs
(06:25) Add buttons to Quick Access Tool Bar
(07:40) What Excel does: Store Raw Data, Make Calculations, Data Analysis and Charting
(08:55) Introduction to Data Analysis
(10:37) Data Types in Excel: Text, Numbers, Boolean, Errors, Empty Cells
(11:16) Keyboard Enter puts content in cell and move selected cell down
(13:00) Data Type DEFAULT Alignments
(13:11) First Formula. Entering Cell References in formulas
(13:35) Keyboard Ctrl + Enter puts content in cell and keep cell selected
(14:45) Why we don’t override DEFAULT Alignments
(15:05) Keyboard Ctrl + Z is Undo
(17:05) Proper Data Sets & Raw Data
(24:21) How To Enter Data & Data Labels into an Excel Spreadsheet
(24:21) Stylistic Formatting
(26:35) AVERAGE Function
(27:31) Format Formulas Differently than Raw Data
(28:30) Keyboard Ctrl + C is Copy. Keyboard Ctrl + V is Paste
(29:59) Use Eraser remove Formatting Only
(29:19) Keyboard Ctrl + B adds Bold
(29:57) Excel’s Golden Rule
(31:43) Keyboard F2 puts cell in Edit Mode
(32:01) Violating Excel’s Golden Rule
(34:12) Use Arrow Keys to put cell references in formulas
(35:40) Full Discussion about Formulas & Formulas Elements
(37:22) SUM function Keyboard is Alt + =
(38:22) Aggregate functions
(38:50) Why we use ranges in functions
(40:56) COUNT & COUNTA functions
(42:47) Edit Formula & change cell references
(44:18) Absolute & Relative Cell References
(45:52) Use Delete Key, Not Right-click Delete
(46:40) Use Fill Handle & Angry Rabbit to copy formula
(47:41) Keyboard F4 Locks Cell Reference (make Absolute)
(49:45) Keyboard Tab puts content in Cell and move selected Cell to right
(50:55) Order of Operation error
(52:17) Range Finder to find formula errors
(52:34) Lock Cell Reference after you put cell in Edit Mode
(53:58) Quickly copy an edited formula down a column
(53:07) F2 key in last cell to find formula errors
(54:15) Fix incorrect range in function
(54:55) SQRT function & Fractional Exponents
(57:20) STDEV.P function
(58:10) Navigate Large Data Sets
(58:48) Keyboard Ctrl + Arrow jumps to bottom of data set (Current Range)
(59:42) Keyboard Ctrl + Shift + Arrow selects to bottom of data set (Current Range)
(01:01:41) Keyboard Shift + Enter puts content in Cell and move selected Cell up
(01:02:55) Counting with conditions or criteria: COUNTIFS function
(01:03:43) Keyboard Ctrl + Backspace jumps back to Active Cell
(01:05:31) Counting between an upper & lower limit with COUNTIFS
(01:07:36) COUNTIFS copied down column
(01:10:08) Joining Comparative Operator with Cell Reference in formula
(01:12:50) Data Analysis features in Excel
(01:13:44) Sorting in Excel
(01:16:59) Filtering in Excel
(01:20:39) Introduction to PivotTables
(01:23:39) Create PivotTable dialog box
(01:24:33) Dragging & dropping Fields to create PivotTable
(01:25:31) Dragging Field to Row area creates a Unique List
(01:26:17) Outline/Tabular Layout
(01:27:00) Value Field Settings dialog to change: Number Formatting, Function, Name
(01:28:12) 2nd & 3rd PivotTable examples
(01:31:23) What is a Cross Tabulated Report?
(01:33:04) Create Cross Tabulated Report w PivotTable
(01:35:05) Show PivotTable Field List
(01:36:48) How to Pivot the Report
(01:37:50) Summarize Survey Data with PivotTable.
(01:38:34) Keyboard Alt, N, V opens PivotTable dialog box
(01:41:38) PivotTable with 3 calculations: COUNT, MAX & MIN
(01:43:25) Count & Count Number calculations in a PivotTable
(01:45:30) Excel 2013 Charts to Visually Articulate Quantitative Data
(01:47:00) #1 Rule for Charts: No Chart Junk!
(01:47:30) Explain chart types: Column, Bar, Pie, Line and X-Y Scatter Chart
(01:51:34) Create Column Chart using Recommended Chart feature
(01:53:00) Remove Field Buttons from Pivot Chart
(01:54:10) Chart Formatting Task Pane
(01:54:45) Vary Fill Color by point
(01:55:15) Format Axis with Numbers by Formatting Source Data in PivotTable
(01:56:02) Add Data Labels to Chart
(01:57:28) Copy Chart & Create Bar Chart
(01:57:48) Change Chart Type
(01:58:15) Change Gap Width.
(01:59:17) Create Pie Chart
(01:59:23) Do NOT use 3-D Pie
(01:59:42) Add % Data Labels to Pie Chart
(02:00:25) Create Line Chart From PivotTable
(02:01:20) Link Chart Tile to Cell
(02:02:20) Move a Chart
(02:02:33) Create an X-Y Scatter Chart
(02:03:35) Add Axis Labels
(02:05:27) Number Formatting to help save time
(02:07:24) Number Formatting is a Façade
(02:10:27) General Number Format
(02:10:52) Percentage Number Formatting
(02:14:03) Don’t Multiply Relative Frequency by 100
(02:17:27) Formula for % Change & End Amount
- published: 19 Dec 2014
- views: 203319
WEBINAR: Basic Data Analysis
- Order: Reorder
- Duration: 64:15
- Updated: 25 Jan 2012
- views: 27393
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...
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, & sub-group comparisons)
* Feel more confident about interpreting data
* Identify steps to improve their current data analysis
--
Capacity for Health (C4H) at APIAHF is a Capacity Building Assistance Program funded by the Centers of Disease Control and Prevention. C4H provides FREE capacity building assistance to community based organizations across the United States and its affiliated territories.
wn.com/Webinar Basic Data 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, 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, & sub-group comparisons)
* Feel more confident about interpreting data
* Identify steps to improve their current data analysis
--
Capacity for Health (C4H) at APIAHF is a Capacity Building Assistance Program funded by the Centers of Disease Control and Prevention. C4H provides FREE capacity building assistance to community based organizations across the United States and its affiliated territories.
- published: 25 Jan 2012
- views: 27393
Data Analysis with MATLAB for Excel Users
- Order: Reorder
- Duration: 59:52
- Updated: 05 Sep 2014
- views: 116043
Many technical professionals find that they run into limitations using Excel for their data analysis applications. This webinar highlights how MATLAB can supple...
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.
Through product demonstrations you will see how to:
• Access data from spreadsheets
• Plot data and customize figures
• Perform statistical analysis and fitting
• Automatically generate reports to document your analysis
• Freely distribute your MATLAB functions as Excel add-ins
This webinar will show new features from the latest versions of MATLAB including new data types to store and manage data commonly found in spreadsheets.
Previous knowledge of MATLAB is not required.
About the Presenter: Adam Filion holds a BS and MS in Aerospace Engineering from Virginia Tech. His research involved nonlinear controls of spacecraft and periodic orbits in the three-body problem. After graduating he joined the MathWorks Engineering Development Group in 2010 and moved to Applications Engineering in 2012.
wn.com/Data Analysis With Matlab For Excel Users
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.
Through product demonstrations you will see how to:
• Access data from spreadsheets
• Plot data and customize figures
• Perform statistical analysis and fitting
• Automatically generate reports to document your analysis
• Freely distribute your MATLAB functions as Excel add-ins
This webinar will show new features from the latest versions of MATLAB including new data types to store and manage data commonly found in spreadsheets.
Previous knowledge of MATLAB is not required.
About the Presenter: Adam Filion holds a BS and MS in Aerospace Engineering from Virginia Tech. His research involved nonlinear controls of spacecraft and periodic orbits in the three-body problem. After graduating he joined the MathWorks Engineering Development Group in 2010 and moved to Applications Engineering in 2012.
- published: 05 Sep 2014
- views: 116043
Basic Data Analysis in RStudio
- Order: Reorder
- Duration: 25:56
- Updated: 04 Aug 2015
- views: 6178
This clip explains how to produce some basic descrptive statistics in R(Studio). Details on http://eclr.humanities.manchester.ac.uk/index.php/R_Analysis
This clip explains how to produce some basic descrptive statistics in R(Studio). Details on http://eclr.humanities.manchester.ac.uk/index.php/R_Analysis
wn.com/Basic Data Analysis In Rstudio
WEBINAR: Introduction to Basic Data Analysis
- Order: Reorder
- Duration: 64:12
- Updated: 18 Apr 2012
- views: 6996
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 peopl...
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 interpret data including attention to the context of the data. This will include some information on how the common software, Microsoft Excel, can be used for these tasks. The webinar will be accompanied by a written Information Sheet.
Online Resource Library: http://library.capacity4health.org
wn.com/Webinar Introduction To Basic Data Analysis
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 interpret data including attention to the context of the data. This will include some information on how the common software, Microsoft Excel, can be used for these tasks. The webinar will be accompanied by a written Information Sheet.
Online Resource Library: http://library.capacity4health.org
- published: 18 Apr 2012
- views: 6996
Big Data Analytics with Microsoft Excel 2013
- Order: Reorder
- Duration: 73:17
- Updated: 18 Sep 2014
- views: 49287
This session describes and demonstrates how to create a big data analytics solution with structured data by using Microsoft HDInsight, Excel 2013 and Microsoft ...
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.
wn.com/Big Data Analytics With Microsoft Excel 2013
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.
- published: 18 Sep 2014
- views: 49287
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48:45
Data Analysis - 10 Things I Wish Someone Had Told Me about Data Analysis
About the Speaker: Mike Sherman (http://www.mikesherman.net/) has an MBA with High Distin...
published: 19 Apr 2012
Data Analysis - 10 Things I Wish Someone Had Told Me about Data Analysis
Data Analysis - 10 Things I Wish Someone Had Told Me about Data Analysis
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- published: 19 Apr 2012
- views: 54332
55:13
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm
Learn the basics of D...
published: 19 Oct 2012
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
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- published: 19 Oct 2012
- views: 839440
9:50
Introduction to Data Analysis and Mining: what is it?
Here I give an introduction to the course of data exploration (data analysis) and data min...
published: 15 Jul 2014
Introduction to Data Analysis and Mining: what is it?
Introduction to Data Analysis and Mining: what is it?
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- published: 15 Jul 2014
- views: 7483
81:50
Introduction to Data Science with R - Data Analysis Part 1
Part 1 in a in-depth hands-on series of videos introducing the viewer to Data Science usin...
published: 09 Nov 2014
Introduction to Data Science with R - Data Analysis Part 1
Introduction to Data Science with R - Data Analysis Part 1
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- published: 09 Nov 2014
- views: 198048
18:32
Introduction to Quantitative Data Analysis
published: 24 Oct 2014
Introduction to Quantitative Data Analysis
Introduction to Quantitative Data Analysis
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- published: 24 Oct 2014
- views: 19878
17:12
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
Qualitative research is a strategy for systematic collection, organization, and interpreta...
published: 24 Jun 2015
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
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- published: 24 Jun 2015
- views: 29114
68:30
Introduction to Data Analysis using Machine Learning
A screencast of a seminar I gave at McGill University in March 2015. Materials including a...
published: 04 Apr 2015
Introduction to Data Analysis using Machine Learning
Introduction to Data Analysis using Machine Learning
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- published: 04 Apr 2015
- views: 25165
10:26
Data Analysis with Python and Pandas Tutorial Introduction
Pandas is a Python module, and Python is the programming language that we're going to use....
published: 14 Sep 2015
Data Analysis with Python and Pandas Tutorial Introduction
Data Analysis with Python and Pandas Tutorial Introduction
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- published: 14 Sep 2015
- views: 40487
4:03
The Landscape of Data Analysis
See http://simplystatistics.org/, https://www.coursera.org/course/dataanalysis or for mo...
published: 10 Jan 2013
The Landscape of Data Analysis
The Landscape of Data Analysis
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- published: 10 Jan 2013
- views: 28850
6:37
Basic Excel Business Analytics #48: Data Analysis Regression feature
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/ex...
published: 26 Nov 2015
Basic Excel Business Analytics #48: Data Analysis Regression feature
Basic Excel Business Analytics #48: Data Analysis Regression feature
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- published: 26 Nov 2015
- views: 4931
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142:43
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
Download files: http://people.highline.edu/mgirvin/excelisfun.htm.
Intro To Excel: Store R...
published: 19 Dec 2014
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
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- published: 19 Dec 2014
- views: 203319
64:15
WEBINAR: Basic Data Analysis
This one-hour webinar will teach the basic skills and tools needed to manage and analyze p...
published: 25 Jan 2012
WEBINAR: Basic Data Analysis
WEBINAR: Basic Data Analysis
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- published: 25 Jan 2012
- views: 27393
59:52
Data Analysis with MATLAB for Excel Users
Many technical professionals find that they run into limitations using Excel for their dat...
published: 05 Sep 2014
Data Analysis with MATLAB for Excel Users
Data Analysis with MATLAB for Excel Users
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- published: 05 Sep 2014
- views: 116043
25:56
Basic Data Analysis in RStudio
This clip explains how to produce some basic descrptive statistics in R(Studio). Details o...
published: 04 Aug 2015
Basic Data Analysis in RStudio
Basic Data Analysis in RStudio
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- published: 04 Aug 2015
- views: 6178
64:12
WEBINAR: Introduction to Basic Data Analysis
This popular webinar, hosted by Capacity for Health on April 18, 2012, is an introduction ...
published: 18 Apr 2012
WEBINAR: Introduction to Basic Data Analysis
WEBINAR: Introduction to Basic Data Analysis
- Report rights infringement
- published: 18 Apr 2012
- views: 6996
73:17
Big Data Analytics with Microsoft Excel 2013
This session describes and demonstrates how to create a big data analytics solution with s...
published: 18 Sep 2014
Big Data Analytics with Microsoft Excel 2013
Big Data Analytics with Microsoft Excel 2013
- Report rights infringement
- published: 18 Sep 2014
- views: 49287
Excel 2013 Statistical Analysis #01: Using Excel E...
WEBINAR: Basic Data Analysis...
Data Analysis with MATLAB for Excel Users...
Basic Data Analysis in RStudio...
WEBINAR: Introduction to Basic Data Analysis...
Big Data Analytics with Microsoft Excel 2013...
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![[Video]: Syrian rebels ‘mistakenly’ beheaded Palestinian boy](http://web.archive.org./web/20160722013139im_/http://cdn4.wn.com/ph/img/12/59/267ab78c5fda7d58737daa1fb634-medium.jpg "[Video]: Syrian rebels ‘mistakenly’ beheaded Palestinian boy")
Syrian rebels ‘mistakenly’ beheaded Palestinian boy
Edit WorldNews.com 21 Jul 2016
Humanity has reached an all-time low as the video of Syrian rebels ‘mistakenly’ beheading a 12-year-old boy identified as ‘Abdullah Issa’ has surfaced on the internet. . The shocking video, which is currently viral on social media, shows a harassed and frightened boy taunted and later beheaded in a truck by five grown men ... The rebels are identified to be a troop from the Nour al-Din al-Zinki Movement ... WN.com, Kalyani. ....
photo: Public Domain / NASA
[Survey]: Poll Reveals A Majority Believe 1969 Moon Landing Was Faked
Edit WorldNews.com 21 Jul 2016
A published survey found a majority of those questioned believe the Apollo 11 Moon landing was in fact faked, according to a Mirror report Thursday.The poll discovered the 25 to 34 age group as those most likely to believe the moon landing never happened, with 73 percent of those falling in that group considering the entire thing to be a highly elaborate hoax ... – WN.com, Jack Durschlag. ....
photo: AP / Joshua Paul
MH370 search team: We’ve been looking in the wrong place
Edit Indian Express 21 Jul 2016photo: ABr / Charles Rex Arbogast
Officer shoots caretaker of autistic man playing outside
Edit The Miami Herald 21 Jul 2016
When a 23-year-old autistic man carrying a toy truck wandered from a mental health center out into the street Monday, a worker there named Charles Kinsey went to retrieve him. A few minutes later the autistic man was still sitting cross-legged blocking the roadway while playing with the small, rectangular white toy ... “He’s on his back with his hands in the air trying to convince the other guy to lie down ... “It was so surprising ... ....
photo: AP / Carolyn Kaster, File
[VIDEO]:Billionaire Mark Cuban Has Some Fun At Donald Trump's Expense On Colbert
Edit WorldNews.com 21 Jul 2016
Cuban has harshly slammed the GOP’s nominee in the past, claiming he’s not as wealthy as he claims and he’s not all that good a businessman, the report said ... “Not just the air.””Your companies fail so often, you must’ve gone to business school at Trump University,” Cuban burned.“Oh, Donald ― he just dropped you like one of your first two wives,” Colbert chimed in ... ....
« back to news headlines
An accelerated pipeline to open materials research
Edit Science Daily 22 Jul 2016
Designed to streamline data analysis and workflow processes from experiments originating at DOE Office of Science User Facilities at ORNL, such as the Center for Nanophase Materials Sciences and Spallation Neutron Source, BEAM gives materials scientists a direct pipeline to scalable computing, software support, and high-performance cloud storage services....
Oil Up on Bullish Inventory Draw, Gasoline Build a Worry
Edit Yahoo Daily News 22 Jul 2016
Analysis of the EIA Data ... The analysts surveyed by S&;P Global Platts – the leading independent commodities and energy data provider – had expected crude stocks to go down some 1.25 million barrels ... The data from EIA generally acts as a catalyst for crude prices and affect producers, such as Exxon Mobil Corp ... Free Stock Analysis Report ... Free Stock Analysis Report ... Free Stock Analysis Report ... Free Stock Analysis Report....
Cabinet okayed 183 foreign trips in eight months
Edit The Himalayan 22 Jul 2016
According to an analysis of the Cabinet decisions made public on the official website of the Office of the Prime Minister, the government took as many as 813 decisions till the first week of June after PM KP Sharma Oli assumed office on October 11 ... The data also shows that the government took 75 decisions to transfer or promote government officials....
SAP's cloud business flying high in China
Edit China Daily 22 Jul 2016
As of June, more than 300 Chinese corporate customers are using S/4 HANA, the company's in-memory computing platform that can offer real-time data analysis ... Data from the research firm Gartner Inc show, the global cloud computing industry will grow nearly 17 percent to $204 billion this year and it is likely to hit $312 billion in 2019....
So you think you chose to read this article?
Edit BBC News 22 Jul 2016
So publishers desperately trying to get us to read and watch their stuff in the face of competition from viral videos and pictures of cats that look like Hitler are enlisting the help of data analytics and artificial intelligence (AI) ... "The data we use is both historical and real-time ... But does using data analytics to learn about reader and viewer behaviour, then make publishing decisions based on that analysis, really count as AI?....
Global Immunochemistry Products Market Research Report 2016
Edit Community news 22 Jul 2016
In the end, the report introduced new project SWOT analysis, investment feasibility analysis, and investment return analysis ... 1.2 Immunochemistry Products Classification Analysis. 1.2.1 Immunochemistry Products Main Classification Analysis. 1.2.2 Immunochemistry Products Main Classification Share Analysis. 1.3 Immunochemistry Products Application Analysis. 1.3.1 Immunochemistry Products Main Application Analysis....
NZ Police intelligence wins three awards (New Zealand Police)
Edit Public Technologies 22 Jul 2016
(Source. New Zealand Police) ... The awards won by Police included. The Intelligence Training Award ... This recognises an agency, team or person responsible for noteworthy innovation in one or more of the fields of collection, analysis or report ... 'It recognises how we're using police communications data and turning it into actionable intelligence that can be used by all districts to help make our roads safer,' Mr Rowland says ... Mr McLennan says....
What's in Store for Mondelez (MDLZ) in Q2 Earnings?
Edit Yahoo Daily News 22 Jul 2016
Mondelez International, Inc. MDLZ is slated to report its second-quarter 2016 results on Jul 27, before the opening bell. Last quarter, the company posted a positive earnings surprise of 20.0% ... MONDELEZ INTL Price and EPS Surprise . MONDELEZ INTL Quote. Let’s see how things are shaping up prior to this announcement ... Zacks Rank ... Free Stock Analysis Report ... Free Stock Analysis Report ... Free Stock Analysis Report ... Free Stock Analysis Report....
What's in Store for Shutterfly (SFLY) This Earnings Season?
Edit Yahoo Daily News 22 Jul 2016
Shutterfly, Inc.SFLY is set to report second-quarter 2016 results on Jul 27, after the market closes. Last quarter, the online photo-printing service provider posted a positive earnings surprise of 12.37% ... Let’s see how things are shaping up for this announcement.SHUTTERFLY INC Price, Consensus and EPS Surprise ... Zacks ESP ... Free Stock Analysis Report ... Free Stock Analysis Report ... Free Stock Analysis Report ... Free Stock Analysis Report....
Spotify joins basically every other app maker in selling your data to advertisers
Edit TechRadar 22 Jul 2016
Streaming music service Spotify announced today that it's begun selling user data to advertisers, though it's not exactly becoming an anomaly in the ad-heavy streaming landscape ... Even Twitter sells user data to help advertisers tailor ads to you. Spotify is selling user data for its 70 million users on the Spotify Free tier ... As a result, these companies are collecting data about you to sell to advertisers....
BCSD Appoints New Administrators (Bakersfield City School District)
Edit Public Technologies 22 Jul 2016
(Source. Bakersfield City School District). July 21, 2016 - The Bakersfield City School District (BCSD) is pleased to announce that it has appointed four district employees to the positions of Area Administrators ... Ms ... Vernon Elementary School ... Ms ... Mr ... While at Cato, he led efforts to build strong Professional Learning Communities which resulted in the improvement of instruction through data analysis and the discussion of best practices ... Mr....
What's in Store for Amkor Technology (AMKR) in Q2 Earnings?
Edit Yahoo Daily News 22 Jul 2016
Amkor Technology, Inc. AMKR is expected to report second-quarter 2016 results on Jul 25. Last quarter, the company posted a positive earnings surprise of 100%. Let us see how things are shaping up for this announcement.AMKOR TECH INC Price and EPS Surprise. AMKOR TECH INC Price and EPS Surprise . AMKOR TECH INC Quote ... Zacks ESP ... Free Stock Analysis Report ... Free Stock Analysis Report ... Free Stock Analysis Report ... Free Stock Analysis Report....
VentureBeat | Workday acquires big data analytics company Platfora (Allegis Capital LLC)
Edit Public Technologies 22 Jul 2016
Publicly traded human capital management software company Workday today announced that it has acquired Platfora, a startup that built software for cleaning up and analyzing big data ... 'Customers want to drill down to transaction-level data and analyze it across multiple dimensions for calculating profitability, ROI, and other operational metrics.' ... Competitors include Clearstory Data and Datameer, among others....
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