Writing’s on the button: The grammar of interactivity

Pic Source: https://www.theguardian.com/info/developer-blog/2013/jan/02/interactive-button-text-grammar

A member of Guardian Interactive team propose a practical solution namely “Would you like to? / I would like to” test when it comes to wording buttons in an interactive context.

The words on a button (on an apps or website) must make sense after both the 
  1. interrogative “Would you like to…?” and 
  1. the conditional “I would like to…”
Another perspective is to view interrogative as catering to the website publisher as speaker and the conditional as catering to the web user as speaker
The “Would you like to? / I would like to” test was applied on the world’s popular sites.  According to the author, not a single button on Facebook that didn’t pass.

Resources for Growing Company

Host your own giveaway and sweepstakes to drive leads, promotion for your business with these tools

Amazon Giveaway

Self Publishing
– Amazon
– Blurb
– Createspace
– LuLu
– Git Book

Customer Service Tools
– Gethelpman
– Intercom
– Zendesk

Team Collboration
– Basecamp
– Slack
– Join.me
– Calendly

– Google Analytics
– Heap

– PayPal
– Stripe

– Google Data Studio

– Fontfabric.com
– MyFonts.com
– Losttype.com

Domain Registration
– Namcheap
– BustAName
– Sedo

Open Data
– Google Trend

– Shopify
– SquareSpace
– Wix
– Weebly
– Strikingly
– WordPress.org
– Tumblr

Stock Photography
– iStockPhoto
– Alamy

Socia Media Management
– Buffer
– Buzzsumo
– Scoop.it

– Onepagecrm
– Socedo
– WuFoo
– SurveyMonkey
– SendGrid
– MailChimp
– Leadin

Recommend Reading
– Zero to One
– Lean Start-up
– Simplify : How the Best Businesses in the World Succeed
– Well-Designed: How to Use Empathy to Create Products People Love

– Bullet

Crowd Financing
– Seedrs

50 years of Data Science

Drawing on work by Tukey, Chambers, Breiman and Cleveland, Stanford statistics professor, David Donoho present a vision of data science based on the activities of people who are ‘learning from data’.

  • John Tukey’s The Future of Data Analysis, asserts that Statistics must become concerned with the handling and processing of data, its size, and visualization.
  • John Chambers’s S language, the predecessor of R, is the forerunner of the “notebook” concept, where an academic paper can be made reproducible, scripted, shareable (i.e. Jupyter Notebook)
  • Leo Breiman’s Two Cultures notes that concern strictly with prediction accuracy is different from inference about models, and that the former is under-represented in academia but prevalent in industry, where it has turned into “machine learning.”
  • William S. Cleveland 2001 paper Data Science: An Action Plan for Expanding the Technical Areas of the field of Statistics addressed academic statistics departments and proposed a plan to reorient their work.

His paper reviews the recent spectacle about data science in the popular media, and about how/whether Data Science is really different from Statistics.

He also describe an academic field dedicated to improving that activity in an evidence-based manner. His premises is that this new field is a better academic enlargement of statistics and machine learning than today’s Data Science Initiatives, while being able to accommodate the same short-term goals.

He propose to call the following collection of activities below as a would-be field “Greater Data Science”

1. Data Exploration and Preparation
2. Data Representation and Transformation
3. Computing with Data
4. Data Modeling
5. Data Visualization and Presentation
6. Science about Data Science

He contended that Information technology skills are a premium but scientific understanding and statistical insight should be firmly in the driver’s seat.

Check out a thoughtful essay by Stanford statistics professor David Donoho, titled “50 Years of Data Science

Using Excel for Data Clean-up and management

Microsoft Excel allows data to be combined many ways. Some techniques are IF statements, the TEXT function, PivotTables and Filtering, Grouping, VLOOKUP, HLOOKUP and MATCH.

IF Statements
For example, this allows you to summarize the data by showing how many questions were asked at the help desks and those that were asked in other customer touch point.

The TEXT function
We might want to calculate the weekday from a date. We would like Excel to tell us automatically which day of the week the transaction took place on, based on the date, to show how reference transactions were distributed over days of the week.

PivotTables and filtering
PivotTables allow you to summarise information from large data sets for analysis quickly. It is possible automatically to count, sum, sort, add, find averages, and/or spot trends, patterns and comparisons.

PivotTables allows you to view the data from different angles by dragging and dropping (pivoting) columns and headings and their related data. PivotTables are also pretty accommodating if one needs to update data, add a new record, or make other changes; the table automatically adjusts to the changes.

PivotTables are especially great for large data sets with hundreds or thousands of rows. Using a PivotTable is immensely more efficient than sorting, counting rows, or doing other manual chugging through data.