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CASE
PRESENTATION
BY TEAM:
PIONEERS
Members
•Sharat Sawhney
•Shubhangi
•Arpit Aggarwal
BACKGROUND
• According to 2013 data statistics, India has about 700 degree granting
Institutionsand 35,500 affiliatedcolleges.
• Procuring authentic informationabout such an enormous list of colleges
across India is a big challengefor students.
• Students require information about variousaspects like no. of departments,
teacher to student ratio, no of publications, course structure, fees,
scholarshipsand loans, infrastructure, hostel and mess facilities,library,
fests, connectivity,reservations( if any), complete placement dataof the
college before admission, which is generallynot providedby the college
itself.
• Most of the students choose alternativepathsfor getting this information
like internet, personalcontacts, personalvisits which are not authentic,
hence less reliable.
OVERVIEW
According to us, it is the sole right of every student to get
correct information about the colleges they want to get
admission in.
To help the case, we have devised a multi-incorporated
algorithm based model suiting the authentic data problem.
We divide our plan in 4 parts:-
Preparing All India Rank for all the colleges based on
admission criteria.
Extracting authentic data from social media.
Honest Reviews from present college students.
Reaching Alumni base of the college.
The Targeting Sectors
Admission Criteria
Social Media(Algorithm-based)
Enrolled College Student
Alumni Base
ADMISSION CRITERIA
Further
Parameters
The
Reference
Simulator
Determining
the code of
comparison
(Engineering
/Medical/La
w)
Engineering
JEE MAINS
Opening &
Closing Ranks
Changing Trends
in Consecutive
Years
UPSEE/KCEE
12th Board
Further Tests
(Yes/No)
• The authenticity of comparison of colleges through opening &
closing rank model can be clearly seen from the cut-offs of colleges of
DU, where on one side cut-off of SRCC is generally 99 or even 100
percent and on the other side ANDC is around 84 percent.
TRACKING SOCIAL MEDIA
Tracking Placement Stats of Collegevia
College Website
Algorithm based statistical learning of data
from reviews of Quora , Shiksha.com,
college-dunia , meet-my-univ , get-my-uni
Analysis of Extracted data through machine
learning and obtaining the average and
standard deviationvalues.
Repeatedexecution fromconsecutive years
and the succeeding years would help us
gather freshdata.
THE ROADMAP EXAMPLE
Strength of Batches
Test Data for ML
Test Data for ML
Test Data for ML
Test Data for ML
Test Data for ML
Test Data for ML
Tracking Enrolled Students
•The concept of floating feedback formsrandomlyamong the students to
get reviews of the present scenario of the college is quite ineffective.
• To sort this, we need to gather campus ambassadors from individual
colleges to work as an intern for Navigus in helping them generate and
collect honest reviews fromother colleges .
•Cambus Ambassadors work by their choice and hence are the most
authentic source of data with referenceto campus reviews and facilities.
•College placement stats-the most important data base , is most easily
collected by students of their own colleges.
•If more than one student is interestedto work as a CA for Navigus , we
can set up a whole hierarchyof CAs to work with us.
Alumni -Tracker
Using Premium version of LinkedIn
to get access to Alumni of Colleges
via Company Employees Section
Getting Current Salary
Stats via LinkedIn helps
to compare colleges on
Alumni Success Index.
Getting Personal
Feedback via e-
mails and
LinkedIn
MARKETING STRATEGY
TEAM
CAs
•CAs would gather data as well as promote.
•The kins of college students as our target.
Affiliate
Marketing
•Partner with sites like quora and discussion forums.
•Partner with sites providing educational material to targetaudience such as Khan
Academy.
Coaching
Centres
•Collaborating with major Coaching Institutes and providing occasionallectures on college
counselling.
•Affiliating yourself in the test series of coaching centres to spread across thenation.
School
Campaigning
•Organizing events and competitions related to college counselling.
•Organizing intra and inter-schoolworkshops.

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Navigus Case Study

  • 2. BACKGROUND • According to 2013 data statistics, India has about 700 degree granting Institutionsand 35,500 affiliatedcolleges. • Procuring authentic informationabout such an enormous list of colleges across India is a big challengefor students. • Students require information about variousaspects like no. of departments, teacher to student ratio, no of publications, course structure, fees, scholarshipsand loans, infrastructure, hostel and mess facilities,library, fests, connectivity,reservations( if any), complete placement dataof the college before admission, which is generallynot providedby the college itself. • Most of the students choose alternativepathsfor getting this information like internet, personalcontacts, personalvisits which are not authentic, hence less reliable.
  • 3. OVERVIEW According to us, it is the sole right of every student to get correct information about the colleges they want to get admission in. To help the case, we have devised a multi-incorporated algorithm based model suiting the authentic data problem. We divide our plan in 4 parts:- Preparing All India Rank for all the colleges based on admission criteria. Extracting authentic data from social media. Honest Reviews from present college students. Reaching Alumni base of the college.
  • 4. The Targeting Sectors Admission Criteria Social Media(Algorithm-based) Enrolled College Student Alumni Base
  • 5. ADMISSION CRITERIA Further Parameters The Reference Simulator Determining the code of comparison (Engineering /Medical/La w) Engineering JEE MAINS Opening & Closing Ranks Changing Trends in Consecutive Years UPSEE/KCEE 12th Board Further Tests (Yes/No) • The authenticity of comparison of colleges through opening & closing rank model can be clearly seen from the cut-offs of colleges of DU, where on one side cut-off of SRCC is generally 99 or even 100 percent and on the other side ANDC is around 84 percent.
  • 6. TRACKING SOCIAL MEDIA Tracking Placement Stats of Collegevia College Website Algorithm based statistical learning of data from reviews of Quora , Shiksha.com, college-dunia , meet-my-univ , get-my-uni Analysis of Extracted data through machine learning and obtaining the average and standard deviationvalues. Repeatedexecution fromconsecutive years and the succeeding years would help us gather freshdata.
  • 15. Tracking Enrolled Students •The concept of floating feedback formsrandomlyamong the students to get reviews of the present scenario of the college is quite ineffective. • To sort this, we need to gather campus ambassadors from individual colleges to work as an intern for Navigus in helping them generate and collect honest reviews fromother colleges . •Cambus Ambassadors work by their choice and hence are the most authentic source of data with referenceto campus reviews and facilities. •College placement stats-the most important data base , is most easily collected by students of their own colleges. •If more than one student is interestedto work as a CA for Navigus , we can set up a whole hierarchyof CAs to work with us.
  • 16. Alumni -Tracker Using Premium version of LinkedIn to get access to Alumni of Colleges via Company Employees Section Getting Current Salary Stats via LinkedIn helps to compare colleges on Alumni Success Index. Getting Personal Feedback via e- mails and LinkedIn
  • 17. MARKETING STRATEGY TEAM CAs •CAs would gather data as well as promote. •The kins of college students as our target. Affiliate Marketing •Partner with sites like quora and discussion forums. •Partner with sites providing educational material to targetaudience such as Khan Academy. Coaching Centres •Collaborating with major Coaching Institutes and providing occasionallectures on college counselling. •Affiliating yourself in the test series of coaching centres to spread across thenation. School Campaigning •Organizing events and competitions related to college counselling. •Organizing intra and inter-schoolworkshops.