- published: 15 Jul 2014
- views: 13238
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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
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: 63827
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: 951175
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: 278064
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: 38367
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: 42371
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
- published: 15 Dec 2014
- views: 13075
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: 29266
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 -
https://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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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 -
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 -
10:40Module 1: Data Analysis in Excel
Module 1: Data Analysis in Excel -
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. -
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. -
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 -
18:32Introduction to Quantitative Data Analysis
Introduction to Quantitative Data AnalysisIntroduction to Quantitative Data Analysis
-
6:36Data Collection & Analysis
Data Collection & AnalysisData Collection & Analysis
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 -
64:15WEBINAR: Basic Data Analysis
WEBINAR: Basic Data AnalysisWEBINAR: 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.
-
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 -
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...
published: 19 Apr 2012 -
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...
published: 19 Oct 2012 -
-
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 -
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 -
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....
published: 24 Jun 2015 -
Introduction to Quantitative Data Analysis
published: 24 Oct 2014 -
Data Collection & Analysis
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
published: 15 Dec 2014 -
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...
published: 25 Jan 2012
Introduction to Data Analysis and Mining: what is it?
- Order: Reorder
- Duration: 9:50
- Updated: 15 Jul 2014
- views: 13238
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: 13238
Data Analysis - 10 Things I Wish Someone Had Told Me about Data Analysis
- Order: Reorder
- Duration: 48:45
- Updated: 19 Apr 2012
- views: 63827
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: 63827
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
- Order: Reorder
- Duration: 55:13
- Updated: 19 Oct 2012
- views: 951175
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: 951175
Module 1: Data Analysis in Excel
- Order: Reorder
- Duration: 10:40
- Updated: 19 Apr 2016
- views: 122537
This video is part of the Analyzing and Visualizing Data with Excel course available on EdX.
To sign up for the course, visit: http://aka.ms/edxexcelbi
This video is part of the Analyzing and Visualizing Data with Excel course available on EdX.
To sign up for the course, visit: http://aka.ms/edxexcelbi
wn.com/Module 1 Data Analysis In Excel
Introduction to Data Science with R - Data Analysis Part 1
- Order: Reorder
- Duration: 81:50
- Updated: 09 Nov 2014
- views: 278064
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: 278064
Introduction to Data Analysis using Machine Learning
- Order: Reorder
- Duration: 68:30
- Updated: 04 Apr 2015
- views: 38367
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: 38367
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
- Order: Reorder
- Duration: 17:12
- Updated: 24 Jun 2015
- views: 42371
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: 42371
Introduction to Quantitative Data Analysis
- Order: Reorder
- Duration: 18:32
- Updated: 24 Oct 2014
- views: 27194
- published: 24 Oct 2014
- views: 27194
Data Collection & Analysis
- Order: Reorder
- Duration: 6:36
- Updated: 15 Dec 2014
- views: 13075
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 ha...
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
wn.com/Data Collection Analysis
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
- published: 15 Dec 2014
- views: 13075
WEBINAR: Basic Data Analysis
- Order: Reorder
- Duration: 64:15
- Updated: 25 Jan 2012
- views: 29266
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: 29266
-
Introduction to Data Science with R - Data Analysis Part 3
Part 3 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. Also check out my blog for additional topics at http://daveondata.com.
published: 17 Jan 2016 -
Data Interpretation Basics by Rohit Agarwal
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...
published: 12 Apr 2014 -
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 ...
published: 19 Dec 2014 -
Data Analysis with MATLAB for Excel Users
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...
published: 05 Sep 2014 -
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...
published: 18 Apr 2012
Introduction to Data Science with R - Data Analysis Part 3
- Order: Reorder
- Duration: 55:33
- Updated: 17 Jan 2016
- views: 17013
Part 3 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 3 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. Also check out my blog for additional topics at http://daveondata.com.
wn.com/Introduction To Data Science With R Data Analysis Part 3
Part 3 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. Also check out my blog for additional topics at http://daveondata.com.
- published: 17 Jan 2016
- views: 17013
Data Interpretation Basics by Rohit Agarwal
- Order: Reorder
- Duration: 115:09
- Updated: 12 Apr 2014
- views: 872787
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 organizat...
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 for solving various exam questions.
The session will also focus on Approaches to solve the questions in this section quickly and correctly.
wn.com/Data Interpretation Basics By Rohit Agarwal
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 for solving various exam questions.
The session will also focus on Approaches to solve the questions in this section quickly and correctly.
- published: 12 Apr 2014
- views: 872787
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
- Order: Reorder
- Duration: 142:43
- Updated: 19 Dec 2014
- views: 247723
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: 247723
Data Analysis with MATLAB for Excel Users
- Order: Reorder
- Duration: 59:52
- Updated: 05 Sep 2014
- views: 140303
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...
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 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
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 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: 140303
WEBINAR: Introduction to Basic Data Analysis
- Order: Reorder
- Duration: 64:12
- Updated: 18 Apr 2012
- views: 8202
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: 8202
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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: 13238
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: 63827
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: 951175
10:40
Module 1: Data Analysis in Excel
This video is part of the Analyzing and Visualizing Data with Excel course available on Ed...
published: 19 Apr 2016
Module 1: Data Analysis in Excel
Module 1: Data Analysis in Excel
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- published: 19 Apr 2016
- views: 122537
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: 278064
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: 38367
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: 42371
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: 27194
6:36
Data Collection & Analysis
Impact evaluations need to go beyond assessing the size of the effects (i.e., the average ...
published: 15 Dec 2014
Data Collection & Analysis
Data Collection & Analysis
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- published: 15 Dec 2014
- views: 13075
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: 29266
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55:33
Introduction to Data Science with R - Data Analysis Part 3
Part 3 in a in-depth hands-on series of videos introducing the viewer to Data Science usin...
published: 17 Jan 2016
Introduction to Data Science with R - Data Analysis Part 3
Introduction to Data Science with R - Data Analysis Part 3
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- published: 17 Jan 2016
- views: 17013
115:09
Data Interpretation Basics by Rohit Agarwal
Data Interpretation, a tool that is required for day to day decisions for us, is also a ke...
published: 12 Apr 2014
Data Interpretation Basics by Rohit Agarwal
Data Interpretation Basics by Rohit Agarwal
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- published: 12 Apr 2014
- views: 872787
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: 247723
59:52
Data Analysis with MATLAB for Excel Users
This webinar highlights how MATLAB can work with Excel.
Get a Free MATLAB Trial: https://g...
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: 140303
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: 8202
Introduction to Data Science with R - Data Analysi...
Data Interpretation Basics by Rohit Agarwal...
Excel 2013 Statistical Analysis #01: Using Excel E...
Data Analysis with MATLAB for Excel Users...
WEBINAR: Introduction to Basic Data Analysis...
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