Googleフォームによるアンケート調査で、複数の選択肢を選ぶことが可能な質問のデータを処理する方法。Transforming multiple response categories into a series of dummy variables using Google Form + Excel.
If you recognize yourself in this description, please take 5 minutes to read on and answer these 3 simple questions:
"By now you have been working for several years with Excel, gradually getting better at it and making simple or elaborate spreadsheets for private or business use. All in all, you are quite satisfied with your work and the results."
An excel manual for beginners. Excel can be confusing and time taking at some times but with perfect knowledge and practice of basic shortcuts, one can surely increase speed and save time
If you recognize yourself in this description, please take 5 minutes to read on and answer these 3 simple questions:
"By now you have been working for several years with Excel, gradually getting better at it and making simple or elaborate spreadsheets for private or business use. All in all, you are quite satisfied with your work and the results."
An excel manual for beginners. Excel can be confusing and time taking at some times but with perfect knowledge and practice of basic shortcuts, one can surely increase speed and save time
Types Of Join In Sql Server - Join With Example In Sql Serverprogrammings guru
Do you know How many types of Joins in SQL. In this ppt presentation we are discussion about types of joins in sql server eg: INNER JOIN , SELF JOIN ,OUTER JOIN ,Right outer Join,Left outer Join,Full Outer Join,CROSS JOIN .
Splitter Student version Tutorial June 2020 - EnglishAdhi Wikantyoso
Student version : For Google Forms Questionnaire with Closed End questions only
-Closed End : Multiple Choice (question with single answer) and Checkboxes (question with multiple answers allowed - multiple data in a cell in a cell format)
-Analysis : Crosstabulations and Filter
Types Of Join In Sql Server - Join With Example In Sql Serverprogrammings guru
Do you know How many types of Joins in SQL. In this ppt presentation we are discussion about types of joins in sql server eg: INNER JOIN , SELF JOIN ,OUTER JOIN ,Right outer Join,Left outer Join,Full Outer Join,CROSS JOIN .
Splitter Student version Tutorial June 2020 - EnglishAdhi Wikantyoso
Student version : For Google Forms Questionnaire with Closed End questions only
-Closed End : Multiple Choice (question with single answer) and Checkboxes (question with multiple answers allowed - multiple data in a cell in a cell format)
-Analysis : Crosstabulations and Filter
Splitter Pro version Tutorial June 2020 - EnglishAdhi Wikantyoso
Pro version : For Google Forms Questionnaire that has Open End Questions
Open End : Instead of choosing available option, respondent write their own answer whether in Short Answer (question with answer in short text or numeric) or Paragraph (question with answer in long text or essay). Answers for open end question especially Paragraph often contains multiple data in a cell.
Charts charts charts! I loved working with the charts feature. My pasimisterchristen
Charts charts charts! I loved working with the charts feature. My particular data set wasn’t that large, and in retrospect I think I would’ve chosen some thing that had a bit more data to go with it, but I have enough data at minimum to see the effectiveness of the features that we learned this week in Excel. Some stand-outs for me were conditional formatting, sorting, and charts.
I think conditional formatting is really when values in the spreadsheet that meet certain criteria can be manipulated color wise or presented in a way so that they stand out as special. For example, my spreadsheet included conditional formatting which highlighted and colored differently a specific drug reaction among patients that had a higher occurrence rate. Again, the possibilities are endless with regard to this feature, and the vast uses of this type of filter would probably be seen even more with very large data sets. The sorting feature follows along with its own unique utility, in that larger and smaller values, for instance, can be clustered together to see majorities or common occurrences, less common outcomes, etc….
I feel a bit sad that I never had the need in my life to make a basic chart in Excel. Lol. But I feel that if I did, I would be quite satisfied with what the program offered. The bar charts and pie charts are quite crisp and effective in their aesthetic, and the options are endless for the user I. Terms of chart type, color, directional variations, etc. I kept playing with the charts feature and finally had to move on because I was just playing with the styles too much. But this is where move of my data analysis lied. Using this feature allowed me to provide clear visuals in my proposal about drug reactions that were the most common among patients.
References
Wempen, F. (2016). Microsoft office 2016 at work for dummies (For dummies). Hoboken, New
Jersey: For Dummies. (2016).
...
You know that if you ask people to fill out a spreadsheet they will fill it out in a variety of ways. It’s
almost as they had a meeting to discuss all the possibilities and then proceeded to show you exactly
how many different ways the same information can be entered!
ECDL / ICDL Advanced Excel 2010 Tutorial - AM4 is a high level certification programme which presents candidates with the opportunity to bring their spreadsheet skills to an expert level. Individuals with ECDL / ICDL Advanced Spreadsheets certified skills can:
Produce higher quality information
Pinpoint key information quicker and more easily
Provide more sharply-defined analysis
Produce more sophisticated reports
Use advanced editing, data handling, functions and analysis features
Use macros within the spreadsheets application
3-D geospatial data for disaster management and developmentKeiko Ono
Japan is a high income country at an advanced stage of epidemiological transition. One of its remaining public health challenges is response to natural disasters. This presentation explores the potential of 3-D geospatial data in disaster response and management.
A narrative review of NLP applications to political science
人工知能、機械学習の急速な発展とともに、そうした分析で利用できる「データ」の範囲が拡大しつつある。人が発話・作成した言葉を人工知能が読み解いて、翻訳・要約、さらには特徴・パターンを見つけるなど高度な分析をする「自然言語処理」はすでに多くの分野で実用化されている。この発表では政治学における自然言語処理を用いたこれまでの研究をレビューし、今後の可能性について検討する。 Keywords: artificial intelligence, natural language processing (NLP), text mining, political science, data science
US presidential selection: the Electoral College challenged (again)Keiko Ono
Constitutional design for selecting the chief executive
Historical evolution since 1789
The Electoral College
How it works today
Implications and criticism
Alternatives
Reapportionment and post-2020 projections
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
2. In Google Form, a “checkboxes” question allows
respondents to choose multiple response
categories.
The dataset will look like this.
3. To separate items (response categories), go to
DATA menu in Excel and choose “Text to
Columns.”
Click on Delimited.
4. Add Semicolon as Delimiter and voila!
The font color has been changed.
5. To more effectively analyze this data, we want the same fruit
(response category) to be in the same column. In the table below,
one could quickly and easily see that respondents #1 and #4 like
apples, but not respondents #2 and #3, etc.
One could
manually
manipulate the
data (sort, copy,
paste) to do this,
but it’s tedious.
6. There may be a number of solutions to this
problem. One is to use function COUNTIF.
Then look for COUNTIF.
Look in the cells B2, C2, D2, E2 and count
the number of times the word “Apples”
appear.
7. The answer is 1. This respondent likes
apples. 0 if s/he doesn’t like apples.
8. Do the same for the other three fruits.
Use absolute reference (“$”) if necessary. Make sure the range is constant (B, C, D, E)
F, G, H, and I columns contain dummy variables (1 if the
respondent likes the fruit, 0 if not). Then we can count the total
number of 1s. #1 and #2 both like two kinds of fruit. #4 likes all
four.
9. We don’t need the columns B, C, D, E any
more, do we? Let’s delete them.
No, they cannot be deleted because the COUNTIF (and SUM) data
depend on those original columns.
Options
-Hide the unwanted columns
-Copy and paste the values (using Paste Special). Then the COUNTIF
data are no longer dependent on the original data.
