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Data Computer Science

Location:
Fullerton, CA
Posted:
April 16, 2018

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Resume:

Akash Raju

*** ********* *****, ********* ** ****1 949-***-**** ac45dr@r.postjobfree.com

linkedin.com/in/akashraju kaggle.com/zakashx/

EDUCATION QUALIFICATION

California State University, Fullerton December 2017

Master of Science, Computer Science

CMR Institute of Technology, Visvesvaraya Technological University June 2015

Bachelor of Engineering in Computer Science & Engineering

COMPUTER SKILLS

Programming Languages: Python, R programming, OpenGL, SQL, Unix Shell Programming.

Packages / Libraries: Python (Numpy, Pandas, SciPy, Sklearn), R( data.table, caTools, Tidyverse, dplyr,lubridate)

SQL : Create/Update Tables, Queries, Aggregate Functions, Join Operations

MS Excel: Vlookups, Macros, Pivot Tables, Functions, Creating Plots/Graphs

Visualization: Plotly, Tablueau, “ggplot2”, “leaflet”, seaborn, Matplotlib

PREDICTIVE ALGORITHMS:

Classification: kNN, Logistic regression and Multinomial logistic regression, Decision trees, Linear discriminant analysis, Naive Bayes algorithm.

Unsupervised learning: DBSCAN, K-MEANS, Hierarchical clustering, Principal component analysis, Apriori algorithm.

Regression: Linear regression and multiple linear regression.

ACADEMIC PROJECTS

Analysis of Data Mining Algorithms using R-API (Shiny and Leaflet) December 2017

Masters Project that was created to analyze the USA Mass Shootings Dataset.

Pre-Processed data using “data.table” and “tidyverse” library in R

Created reactive graphs using PLOTLY library in R

Created reactive maps using the LEAFLET library which is an open-source JavaScript library for interactive maps.

Creating a front end that can run the project using SHINY library in R.

Noise Detection May 2016

Found Anomalies in Wine dataset from the UCI repository

Used CRAN(DBSCAN). AND CRAN(fpc) to determine noise in the datasets.

Data was split into Training and Testing data using the sample.split function from the CRAN(caTools) package and the results of training and testing data was compared and analyzed.

San Francisco Crime Classification May 2016

Collaboration with a team of Four,

Different Classification algorithms such as KNN, Logistic Regression, Naïve Bayes Algorithm was used to analyze the San Francisco Crime data from Kaggle data-sets.

My role is this project was to identify why Regression and Clustering techniques cannot be applied to this dataset.

Created reports and presentation of this project.

WORK EXPERIENCE:

InoVVorX Technologies Pvt. Ltd. Bangalore, India Data Analyst Intern March 2014 – June 2015

Worked with a team to interpret data using statistical techniques and provide reports

Collected, Updated data to data tables using SQL functions.

Filtered and Cleaned data for visualization using SQL queries, aggregate functions and join operations.

Assisted in creating reports and charts based on the product type, customer type, and sale information.

Updated reports to the manager in a timely manner.



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