Atlanta MS BI Group 5th Anniversary

/0 Comments/in /by

Our next Atlanta MS BI Group meeting will be on August 31st. This happens to be our 5th anniversary! Thanks for everybody who contributed to the group success! We’ve come a long way since our first meeting with a dozen people to the attendance we enjoy today of 60-70+. Come and join the festivities and learn about sentiment analysis with Big Data & Machine Learning.

 

Presentation:

Tone/Sentiment analysis with Big Data & Machine Learning

  

Level: Intermediate

Date:

Monday, August 31th, 2015

Time

6:30 – 8:30 PM ET

Place:

South Terraces Building (Auditorium Room)

115 Perimeter Center Place

Atlanta, GA 30346

Overview:

People’s sentiments and opinions are written in social networks. There are tweets, Facebook posts, book reviews, forum discussions, and more. These attitudes and feelings are communicated using text, with format depending on the social network. Twitter messages are limited to 140 characters and use hash-tags and Facebook messages can be longer.

 

This session reviews the different Natural Language Processing, text mining, and data mining techniques you can use for sentiment and tone analysis. Organizations can use the extracted knowledge for brand reputation, market predictions, and automatic learning. We’ll look at, Hadoop, data mining, Microsoft Big Data Hadoop distribution HDInsight and Azure Machine Learning.

Speaker:

Paco Gonzalez, a SolidQ Mentor, is the PASS Big Data Virtual Chapter Leader, SQL Saturday Barcelona Organizer and assistant at the PASS Atlanta BI User Group. He is finishing his PhD thesis about: “Analyzing Social Data with Machine Learning.” He is an MCT and MCP on BizTalk Server and SQL Server. He is coauthor of Microsoft Training Kit 441. Paco is a frequent speaker at large and small conferences. SQL Saturdays, TechEds, PASS BA, PASS Summit, DevWeek London, and PAW Chicago and London.

Sponsor:

TEKSYSTEMS is a subsidiary of Allegis Group, the largest private talent management firm in the world. Our longstanding history and industry-leading position speak to our success in providing the IT staffing solutions, IT services and talent management insight required for you to actualize ROI and sustain a truly competitive advantage in a fast-changing market.

 

 

Offsetting Week Start Day

/0 Comments/in /by

Scenario: You have a date dimension table and typical date-related columns, such as DayOfWeek, WeekEndDate, etc. In US, weeks start on Sunday and end on Saturday (ISO weeks on the other hand start on Monday and end on Sunday). You have a requirement to overwrite the week start and end days for a Power Pivot, Tabular, or MD model. For example, while the Date table defines the week start day on Sunday, you might need to overwrite it to start on Monday.

Solution: I typically derive as many of the date calculations in a SQL view on top of the Date table instead of saving the results in the table itself. For example, the WeekOfYear calculation might look like this:

DATEPART(WEEK, [Date]) [WeekOfYear]

If you have date calculations like this, instead of changing all week-related calculations to reflect the desired week start day, the easiest way to offset the week day is to use the T-SQL SET DATEFIRST statement preceding the actual SELECT statement. The following example taken from Power Pivot/Tabular overwrites the first day of the week to start on Monday.

Applied Power BI Service Class Available

/0 Comments/in /by

I’m happy to announce the availability of my latest training course – Applied Power BI Service. I believe that the new version of Power BI, known as Power BI Service or Power BI 2.0, is going to be very successful and it should warrant your serious interest. This one to two-day class is designed to help you become proficient with the Power BI Service and acquire the necessary skills to work with online and on-prem data, implement data models on a par with professional models created by BI pros, unlock the power of data by creating interactive reports and dashboards, and share insights with other users. No prior data modeling or reporting knowledge is assumed. Students are welcome to bring their own data to the second day of the class.