The idea of the industry project program is not new. Lots of postgraduate degree programs offer capstone projects or work integrated learning opportunity with an industry partner. So why is this program different?
Well it’s the way that SAS Australia runs the program.
First – SAS works with those universities who as part of the SAS Global Academic Program collaborate with SAS around either an undergraduate or postgraduate teaching program. It means students are already learning about AI, Data and Technology before they arrive at the industry project program.
Second – participant have the opportunity to work with either SAS or our customers. This means the industry project program offers a wide range of business challenge scenarios for the teams to engage with.
Third – we provide access to the latest and greatest AI technology platform where the students can build and teste their hypothesis and models. And the app is based in the cloud, making it quick and easy for teams to focus on the important stuff: solving the business problem.
Fourth – teams are introduced to the analytics lifecycle and guided how to use this methodology to drive insights about the data being used and the hypothesis being tested.
Finally – industry mentors advise how the teams should project manage the project, provide regular feedback on the teams progression and work with the teams to prepare a presentation that is delivered to industry and subject matter experts.
Here’s one we prepared earlier
Recently two University of Canberra students, Milica Gallardo Petkovich and Tzuwei Tsai, took part in the industry project program. Both students were learning about AI, Data and technology as part of their degree program. They project that both students worked on was to develop a data-driven solutions for home loan origination scorecard.
The project main goal was to leverage AI and SAS Viya for Learners to enhance risk management and decision-making in the Australian Mortgage Industry. 20 students participated, forming 4 groups, who each worked collaboratively to address this real-world problem.
5 mentor sessions were run where the students presented their work to date to a team of industry mentors with the valuable feedback and coaching helping the students develop their technology, AI and business problem solving skills.
In fact SAS was so impressed with 2 students – we hired them. You can read their story here.
What Next?
Simple. If you are reading this article and interested to know more, reach out to us at academic@oz.sas.com. If you’re in industry and want to leverage the connections that the SAS Academic Program has in Australia to recruit students to participate in your industry project, let us know. Or if you just want to be introduced to one of the many higher education partners we work with, we here to help. And if you’re a academic and want to know more about the SAS Global Academic Program, we’re here to help.
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Hello, I ran a linear mixed model with repeated measures using PROC MIXED. I used the RCORR statement to obtain the intraclass correlation coefficient (ICC) for the non-independent variable 'score' across readers. Is there a function within PROC MIXED that outputs the 95% CI and p-value for the ICC? PROC MIXED DATA=final COVTEST;
CLASS subject_id reader (REF='1');
MODEL score = reader / S CL;
REPEATED / SUBJECT=subject_id TYPE=cs R RCORR;
RUN; Thank you for your help!
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When using function foptname and filename contains Chinese character, it failed to return value. The prerequisites are:
1. Windows OS;
2. SAS 9.4 M8;
3. UTF-8 encoded SAS session;
And the active code page of my Windows OS is 936, which means simplified Chinese, a friend think this could be related.
Using foptname and finfo to get file attributes, with English file name:
filename _dummy_ "C:\Profiles\Work\temp\hello.sas";
data _null_;
fid=fopen('_dummy_');
if fid then do;
do i=1 to foptnum(fid);
foptname=foptname(fid,i);
finfo=finfo(fid,foptname);
put foptname ":" finfo;
end;
fid=fclose(fid);
end;
run;
Log:
文件名 :C:\Profiles\Work\temp\hello.sas
RECFM :V
LRECL :32767
文件大小(字节) :634
上次修改时间 :2024年05月16日 10时06分17秒
创建时间 :2024年05月16日 10时06分17秒
The output is cool.
With Chinese file name:
filename _dummy_ "C:\Profiles\Work\temp\你好.sas";
data _null_;
fid=fopen('_dummy_');
if fid then do;
do i=1 to foptnum(fid);
foptname=foptname(fid,i);
finfo=finfo(fid,foptname);
put foptname ":" finfo;
end;
fid=fclose(fid);
end;
run;
Log:
文件名 :C:\Profiles\Work\temp\你好.sas
RECFM :V
LRECL :32767
:
:
:
Missing the last 3 output.
With Janpenese file name:
filename _dummy_ "C:\Profiles\Work\temp\こんにちは.sas";
data _null_;
fid=fopen('_dummy_');
if fid then do;
do i=1 to foptnum(fid);
foptname=foptname(fid,i);
finfo=finfo(fid,foptname);
put foptname ":" finfo;
end;
fid=fclose(fid);
end;
run;
Log:
文件名 :C:\Profiles\Work\temp\こんにちは.sas
RECFM :V
LRECL :32767
:
:
:
Missing the last 3 output.
Why I cann't get right output when filename contains MBCS? Is there a robust way to get file attributes regardless of the encoding or OS? Thanks in advance.
PS1: Under euc-cn(whichc means simplified Chinese) encoded SAS session, Windows OS, the problem just disappear.
PS2: Under utf-8 encoded SAS session, Linux OS, the problem just disappear.
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I am attempting to extract topics from a collection of customer comments. I would like to be able to parse common phrases. I read the documentation for the multi term parameters within the parse statement. However I am not getting it to work. How can I pass the phrases"I don't know", "improve training" or "everything is fine"?
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I have long data and wrote code to track changes in 2 variables over time. However, the second entry for each userID is wrong. My data, code, and screenshot of results below. What is wrong with the code?
data fake_data;
input userID $ event_date date9. response $ code $;
format event_date date9.;
datalines;
1693 01Dec2014 Y V
1693 01Jan2015 Y V
1693 01Feb2015 Y V
1693 01Mar2015 M G
1693 01Apr2015 M G
1693 01May2015 M G
1129 01Feb2018 Y V
1129 01Mar2018 Y V
1129 01Apr2018 N R
1129 01May2018 N R
1129 01Jun2018 M R
1129 01Jul2018 M R
1345 01Aug2016 N R
1345 01Sep2016 N R
1345 01Oct2016 N R
1345 01Nov2016 N R
1345 01Dec2016 M R
1345 01Jan2017 M G
1345 01Feb2017 Y G
1345 01Mar2017 Y G
1345 01Apr2017 Y G
1345 01May2017 Y G
;
proc sort data = fake_data out=fake_data_nodupkey nodupkey; by userID event_date; run;
data flag_vars;
set fake_data_nodupkey;
by userID event_date;
if first.userID then do;
prev_response = response;
prev_code = code;
end;
else do;
prev_response = lag(response);
prev_code = lag(code);
end;
response_change = 0;
if response ne prev_response then response_change = 1;
code_change = 0;
if code ne prev_code then code_change = 1;
run;
proc print data=flag_vars;
run;
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