Locate family members with the last name MASON-SINGH.

6+ years of experience in professional Software Development. Experience of leading teams. Experience of client communication and gathering requirements. Expertise in analysis, design and development of software applications and general problem solving. Track record of professional and hardworking attitude towards job, focused on delivery. Quick learner who adapts to extraneous assignments and exploring new dimensions while solving development problems. Experienced in design and development in AWS Cloud Computing, Python, Data Lake and Digital Signage. Experienced with Infrastructure as a Code using AWS SAM. Involved in software design, development and management using Agile development and Scrum Model. Technical Skills AWS Cloud: Lambda, Athena, Glue, API Gateway, SAM, Boto3, DynamoDB, RDS, MySQL, S3, SNS, SQS, EC2, IAM, KMS, CloudWatch, CodeCommit Languages: Python, SQL Frameworks: RESTful Web Services, Flask, Django REST framework Databases: MongoDB, DynamoDb, Postgres, MySQL, MSSQL
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AMANDA-MARIE MASON-SINGH is an academic who published a paper in 2016 entitled STU,,ENTS' PERCEPTIONS OF TEACHER INSTRUMENTAL SUPPORT AS A PREDICTOR OF MOTIVATION IN READING AND. AMANDA-MARIE MASON-SINGH published this work as part of a team: Mason-Singh, Amanda Marie. Here is a description of this work: In ,,his study, relations among students' perceptions of instrumental help/support from their teachers and their reading and math ability beliefs, subjective task values, and academic grades, were explored from elementary through high school. These relations were examined in an overall sample of 1, 062 students from the Childhood and Beyond (CAB) study dataset, a cohort-sequential study that followed students from elementary to high school and beyond. Multi-group structural equation model (SEM) analyses were used to explore these relations in adjacent grade pairs (e.G. second grade to third grade) in elementary school and from middle school through high school separately for males and females. In addition, multi-group latent growth curve (LGC) analyses were used to explore the associations among change.