SQL DEVELOPER INTERN Resume

SUMMARY:

I have strong Programming, Analytical and Logical skills and a strong foundation in Math . My core interest is to organize and analyze large amounts of data using various techniques . . I like to play with data by preprocessing, cleaning, analyzing, visualizing, and reporting. I am also interested in collecting the information from various sources and then interpret their patterns and trends. Hands - on experience in analyzing data.

SKILLS:

Programming Languages: Python, R, C, C++, SQL, PL/SQL, Django, Java(Core)

Databases/RDBMS: Oracle SQL, MySQL

Tools: Tableau, Toad, Jupiter Notebook,SAS

Web Servers: Tomcat

Mark-Up Languages: HTML, CSS, XML

Libraries: NumPy, Pandas, Matplotlib, Sci-kit Learn, OpenCV, Keras, TensorFlow

Big-Data platform/tools: Hadoop, MapReduce, Hive,Impala,Sqoop,Flume,Pig,Spark

Operating Systems: Windows 7, Windows 10, Unix, Linux

SDLC Methods: Waterfall, Agile, Scrum

AWS: EC2, Redshift, Lambda, Kinesis, DynamoDB,RDS,VPC,ECS,Docker,Cloudfront,Route53,EBS,Beanstalk.

PROFESSIONAL EXPERIENCE:

SQL DEVELOPER INTERN

Confidential

Responsibilities:

Developed SQL code for Cancellation Ccode Assignment using Analytic functions.
Performed Unit Testing and Integration Testing to assess the reasons behind the claim failure .
Built a flow chart that determines the exact flow of claim when rec ei ve d

Comparison and Analysis on Unsupervised data sets: Grouped the data points into meaningful clusters and then analyzed which classifier works better .
K-means worked very well here than Hierarchical Clustering.
Comparison and Analysis of PCA,LDA on Adult, Wine data sets: Predicted the quality of wine for wine data set and predicted whether person earns more or less than 50k per year .
Projected higher dimensions into lower dimensions using PCA,LDA and preserved the data.
Random Forest and SVM achieved 85% accuracy.
Also used Bagging and Boosting ensemble methods where the accuraices were really better.
Dog Breed Classifier: To build algorithm identifying dog breeds based on images and identify resembling dog breed when given human images .
Analyzed data sets using pre-trained ResNet50 deep learning neural network model and compared 3 Confidential models including transfer learning to optimize performance.
Web Application for University Fitness Center: Insert ed , retrieve d and update d information about the Students, Instructors and classes conducted in the fitness center, which is handled by two admins who have the privilege of performing above mentioned actions .
Used HTML,CSS for front end, Java for backend and Oracle as database.
Recommendation System on Movie-User dataset: Using various techniques like Content-Based, Collaborative,User-User,Item-Item etc., predicted missing values and also recommended several movies for the users.

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