R&D Assistant Professor Resume

OBJECTIVE:

Seeking a position to utilize my skills and abilities in the field of Data Mining/Data Scientist/Data Analysis that gives me scope to explore, extend andapplymy knowledge and also offer professional growth while being resourceful, innovative and flexible.

CAREER SUMMARY:

Masters in computer science and engineering.
Total Three and half year research and development experience in data mining, recommender systems, data analysis, pattern recognition and computer science and lecture experience in computer science, data mining and other IT courses at undergraduate and postgraduate levels.
Three years academic supervision experience of Masters, master by research students and research assistants in data mining and computer science.
Commercialized research outcomes and high quality referred publications.
Proficient in SAS/BASE, SAS/SQL, SAS/Macros, data processing, data collection, quantitative and qualitative data analysis and Data mining technique.
Experience in STATS/REPORT generatingprocedureslike PROC SUMMARY, PROCFREQ, PROCMEANS, PROCUNIVARIATE, PROCTRANSPOSE, PROCFORMAT, PROC REPORT.
Possess good analytical and problem - solving skills, excellent communication and coding skills, excellent teamplayerwith high level of personal initiatives, Ability to handle multiple tasks simultaneously.

TECHNICAL SKILLS:

Programming: SAS/BASE, SAS/SQL, SAS/MACROS, C++, JAVA.

User Interfaces: Microsoft Expression Web, Adobe Photo Shop CS2

Operating System: Windows (7, XP, 2003 and 2000), Linux.

Tools: Rational Rose, Data Mining Tool See5, Tanagra, Orange.

Reporting: MS Excel, MS Access

PROFESSIONAL EXPERIENCE:

Confidential

R&D Assistant Professor

Responsibilities:

Role of partitioning techniques such as Horizontal, Vertical and Hybrid techniques were analyzed in the ground of medical sciences.
Breast cancer dataset from UCI KDD machine learning repository was taken and replicated into three different schemas.
C5, Cross Validation, Boosting algorithms were implemented for generating rules set, classifier with the help of see5 software.
Based on the classified record set, accuracy of different schema of the same dataset is calculated and analyzed.
On comparing the accuracy it was found that the Hybrid Partitioning Technique provides much more accurate results than the other two.
The results generated in each case are in form of classification scores for all records and the decision trees.
On taking the average of accuracies for the individual schemas and comparing them, accuracy of the hybrid approach is highest 95.075%.

Assistant Lecturer

Confidential

Responsibilities:

Administered several classes and graded examinations and Offered optional independent career counseling opportunities to other students.
Provided subject consultations, led and directed the work of faculty instructors and Conducted college - level courses in the field of Computer Science Engineering.
Collaborated with faculty and research centers to enhance external researches.
Performed teaching in areas of expertise and Conducted research for specific areas of the subject.
Executed administrative functions in the faculty and Shared guidance duties, committee and department assignments with other faculty members.
Contributed to the development of engineering development policies.
Developed lesson plans and instructional materials and Taught on assigned schedules for several subjects based on approved curriculum.
Involved in initiatives to maintaining productive forums with students.
Assisted in motivating students in achieving completion of their projects and assignments and Collated reports on student grades and attendance.

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