Education analytics – reporting students growth using sgp modeleSAT Journals
Abstract Every part of the education sector is struggling to produce actionable data favorable for their growth. The primary stakeholders of this sector are unable to take effective and productive decisions as the huge amount of data collected is not being processed properly. A lot of striking data are lost in the process as there are no schemas available for extracting the intelligence from them. Various external factors affecting the student’s growth are not identified and thus the parents and teachers fail to understand the real reason behind the student’s performance. Hence to measure student's growth SGP (Students Growth Percentile) can be used. It is also necessary to keep tab on student's future marks so as to take precautionary measure in case of negative growth. Here, time series analysis and forecasting can be used. Regression is use to calculate impact of any external factors on overall performance. When all these identified external myriad data along with the academic data is captured, processed using analytical models such as R. The stakeholders will be able to understand the core reasoning behind progress rate and thus take decisions accordingly. This is the fundamental idea behind Education Analytics. Keywords - Analytics, Education Analytics, Student’s Growth Percentile, Marks Forecasting, Student’s growth
Students opting engineering as their disciple is increasing rapidly. But due to various factors and inappropriate primary education in India dropout rates are high.With the help of data mining techniques we can predict the performance of students in terms of grades and dropout for a subject can be predicted. In the proposed system, Naïve Bayes algorithm is used. Based on the rules obtained from the developed technique, the system can derive the key factors influencing student performance.
Indiana University 2018 SICE summer camp slidesJoanne Luciano
This is a mini lecture overview of the data science workflow for the students attending the Indiana University School of Informatics, Computing, and Engineering (SICE) Summer Camp.
Education analytics – reporting students growth using sgp modeleSAT Journals
Abstract Every part of the education sector is struggling to produce actionable data favorable for their growth. The primary stakeholders of this sector are unable to take effective and productive decisions as the huge amount of data collected is not being processed properly. A lot of striking data are lost in the process as there are no schemas available for extracting the intelligence from them. Various external factors affecting the student’s growth are not identified and thus the parents and teachers fail to understand the real reason behind the student’s performance. Hence to measure student's growth SGP (Students Growth Percentile) can be used. It is also necessary to keep tab on student's future marks so as to take precautionary measure in case of negative growth. Here, time series analysis and forecasting can be used. Regression is use to calculate impact of any external factors on overall performance. When all these identified external myriad data along with the academic data is captured, processed using analytical models such as R. The stakeholders will be able to understand the core reasoning behind progress rate and thus take decisions accordingly. This is the fundamental idea behind Education Analytics. Keywords - Analytics, Education Analytics, Student’s Growth Percentile, Marks Forecasting, Student’s growth
Students opting engineering as their disciple is increasing rapidly. But due to various factors and inappropriate primary education in India dropout rates are high.With the help of data mining techniques we can predict the performance of students in terms of grades and dropout for a subject can be predicted. In the proposed system, Naïve Bayes algorithm is used. Based on the rules obtained from the developed technique, the system can derive the key factors influencing student performance.
Indiana University 2018 SICE summer camp slidesJoanne Luciano
This is a mini lecture overview of the data science workflow for the students attending the Indiana University School of Informatics, Computing, and Engineering (SICE) Summer Camp.
1. Vishal Mhadeshwar, B. SC, M. SC, M. Phil
193/7004, Kannmwar Nagar 2, Vikhroli (E), Mumbai 400083
+91-8082399219, Email: mhadeshwar16@gmail.com
PROFILE
Experienced and dynamic researcher with strong analytical and management skills.
Competent in qualitative and quantitative epidemiologic and bio-statistical methods.
Proficient in using statistical software (R, STATA, SPSS), business intelligence software
(Tableau)and data management software (CS Pro, ODK,SQL). Thinks out-of-the-box to create
fresh ideas and challenges. Organized and principled with strong leadership qualities.
Excellent communication skills and a great team player.
EDUCATION
Master of Philosophy(M.Phil.) May, 2015
International Institute of Population Science (IIPS), Mumbai, India
Concentration: Population Science
Relevant coursework: Advanced Research Methodology, Survey of Literature, Optional
paper-Public Health and Mortality
Grade Point: 62.70
Master of Science (M.SC) May, 2014
International Institute of Population Science (IIPS), Mumbai, India
Concentration: Population Science
Relevant coursework:
Part I: Mathematical Methods, Statistics, Economics and Geography, Sociology-Psychology &
Anthropology, Introduction to Demography and Sources of Demographic Data, Fertility,
Nuptiality, Mortality-Morbidity & Public Health, Migration & Urbanization, Evaluation &
Adjustment of Demographic Data & Population Projections and Reproductive Health.
Part II: Gender Issue, Population & Development, Population Policy – Programmes &
Evaluation of Health & Family Welfare Programme, Research Methodology, Population
Ageing, Operational Research, Introduction of Demographic and Statistical Software.
Grade Point: 62.10
2. Bachelor of Science (B. SC) May, 2012
University of Mumbai
Concentration: Statistics
Relevant coursework: Major Subject: Statistics, Minor Subject: Computer PRG & SYS. ANAL
Grade Point: 73.25
Certificate of Assistant Professor Dec, 2013
UGC-NET has awarded the certificate of assistant professor in the subject population studies
(UGC reference number: 28429/ (NET-DEC. 2013))
Certificate of Junior Research Fellowship Jun, 2015
UGC-NET has awarded the certificate of junior research fellowship in the subject population
studies (UGC reference number: 11335/ (OBC) (Net-June 2015)
PROFESSIONAL DEVELOPMENT:
Computer Skills: Microsoft Office Suite (Word, Excel, PowerPoint, Access & Outlook), R
Studio, SPSS, STATA, Tableau, ODK, CS Pro, SQL
PROFESSIONAL EXPERENIENCE
Data Management Officer Sep 2015 – till date
India Health Action Trust, UP-TSU, Lucknow, Uttar Pradesh.
Project: Improved Quality of community and low level facility management of childhood
pneumonia and diarrhoea in Uttar Pradesh.
Responsibility handled in the project
Ensuring smooth operation of data management and analysis using statistical
programming software like R.
Managing and implementing system and tools to aid program staff in analysing data
for decision making.
Ensuring timely completion of data entry, validation of data and incorporating
corrective measure when errors are identified in the state database management
system on regular basis.
Ensuring feedback on data quality to field staff and providing analytical report to
field/supervisor.
3. Develop Tableau interactive reports as per the schedule and deadlines that provide
clear visualizations of various KPIs.
Access and transform massive datasets through filtering, grouping, aggregation, and
statistical calculation using Tableau using advanced Tableau features including
calculated fields, parameters, table calculations, joins, data blending, and dashboard
actions.
Design and implementation of research survey to evaluate the programme for
understanding the appropriateness of intervention.
Providing feedback on data inputs in project publications.