A Winter School on Data Analytics is organised jointly by School of Computer Science & IT (SCSIT) and Indo-German Max Planck Center (IMPECS) on November 15-18, 2013 at SCSIT, DAVV, Indore. The school is open to researchers, faculty members, all Post Graduate and final year Under Graduate students fulfilling expected background (listed below).
        Many of today's enterprises and scientific institutions collect data at the most detailed level possible, thereby creating data repositories ranging from terabytes to petabytes in size. The goal of data analytics is to uncover the knowledge buried in these large-scale datasets in order to make most effective use of the available data. Data analytics is closely related to the fields such as data mining and machine learning, data management, natural language processing, and information retrieval.
        The 2013 IMPECS winter school on data analytics focuses on foundational aspects of data analytics and, in particular, on web-based analytics, which is acting as a driving force for many innovations in the field. The school is organized into two modules. In the first module, students learn basic techniques and tools that are useful for many data mining tasks, including clustering, prediction, pattern mining, and matrix decompositions. The second module shifts gears and presents a more system-oriented view of data analytics, including acquisition and organization of data from sources such as the Web as well as fundamental tools required to analyze and process this data to make it useable. Students will learn about large-scale data processing techniques starting from data representation and querying to deeper data analytics tasks.
        Participants will learn concepts from experts in the field and get a chance to try out some of the techniques in hands-on labs. The school consists of lecture sessions in the mornings and practical sessions in the afternoons.

Background Expected

Subject: Apart from data structures and algorithms, participants are expected to have done a basic course on Linear Algebra, Probability and Statistics and Database Management Systems
Programming: Fluent programming in C and/or Java.


Dr. A.K. Ramani Memorial Hall
School of Computer Science & IT
Devi Ahilya Vishwavidyalaya
Takshashila Campus, Khandwa Road
Indore (M.P.)

How To Apply?

The interested faculty members are requested to send their resumes and the students are required to send their marksheets till previous semesters. The resumes/ documents may be sent at wsoda.scsit@gmail.com on or before Oct 15, 2013. The selected participants will be informed by email on Oct 18, 2013.

School Co-ordinators

Dr.(Mrs.) Maya Ingle
Professor & Senior System Analyst
Email: maya_ingle@rediffmail.com

Prof. Maya Ramanath
Assistant Professor
Dept. of computer science & Engineering
IIT Delhi, New Delhi
Email: ramanath@cse.iitd.ac.in


University Guest House
Opposite to Holkar Science College
Near VC House
BRTS Road, Indore (M.P.)

Registration (Reimbursable)

        The school participation fees for selected students is Rs. 2000/- and for selected faculty members is Rs. 2500/-. This fees will be reimbursed after successful completion of the school. Participants will be provided the course material, working lunch and tea/snacks during the session.
        The selected participants from outstation will be provided breakfast, dinner and accommodation free and the travel support also.The registration form will be available on the website and all selected participants will be required to submit it on wsoda.scsit@gmail.com on or before Oct 25, 2013 along with a scanned copy of Demand Draft in favor of “Registrar (Self finance) Computer Science”. (Please send scanned copy of demand draft only after participants are selected for the school).


Day 1 & 2: Understanding and defining several different types of data mining tasks such as prediction, clustering and pattern mining and applications areas such as recommendation systems and topic modeling. Learning how linear algebra and other matrix-based methods serve as a unifying background behind these tools and applications.

Day 3: Introduction to text mining in general, and information extraction in particular. Understanding the various kinds of information extraction tasks, including entity extraction, binary and n-ary relationship extraction, vertical extraction tasks. Studying various tools and techniques to perform high quality extractions on large corpora.

Day 4: Introduction to large scale structured data, graph data in particular including "knowledge-graphs" and social graphs. Understanding the various kinds of analytics tasks that can be performed on these graphs. Techniques to efficiently process large scale graphs.