Data science, Python, R, C, C++
Resume:
Los Angeles, CA ***24
ANUSHA KANNAN
https://www.linkedin.com/in/anusha-kannan
ac1hsd@r.postjobfree.com
EDUCATION
M.S. Electrical Engineering University of California, Los Angeles Sept 2015 – March 2017 Graduate Coursework (CGPA:3.62/4.0)- includes Big data analytics, Graphs & Network flows, Statistical Programming, Integrated Circuits fabrication processes, Nanoscience and Technology
B.E. Electronics & Instrumentation Anna University, Chennai, India June 2010 – April 2014 Undergraduate Coursework (CGPA:8.63/10.0) -includes Transforms and Partial Differential Equations, Fundamentals of Computing and Programming, Data Structures & Algorithms, Object Oriented Programming and Laboratory, Digital Logic Circuits, Signal Processing, Digital Systems Design, Control Systems, Embedded Systems, Data Structures & Algorithm laboratory, Principles of Management Independent Study- Python for Data Science and Machine learning, Machine learning in Python & R for data science, Python bootcamp LANGUAGES, TECHNOLOGIES & SOFTWARE
C, C++, Python, MATLAB, NI-labVIEW(core I and II), TCAD-TSuprem4, TCAD-Medici, OpenCV, SageMathCloud, R WORK EXPERIENCE
Teaching Assistant- Mathematics for Life Scientists Responsibilities include but not limited to teaching Laboratory sections that involve applying the fundamental concepts of differential equations, delays and linear algebra for building mathematical models to understand the dynamics of biological systems. SageMathCloud platform used for problem- solving, plotting and dynamical simulations; January 2017- March 2017
Assistant System Engineer- Trainee- Tata Consultancy Services Acquired training on the basics of core Java and Oracle SQL; Exposed to the basics of SDLC and testing, manual testing; wrote test cases and test scenarios for real time projects using MasterCraft ALM tool, from February to April 2015 TECHNICAL EXPERIENCE
Data Analysis of Stock prices
Analyzed stocks of six different banks between the years 2006 to 2016, from Google Finance; used various data visualization tools including Pandas and Seaborn, on Python to analyze the returns for each bank’s stock, best and worst single day gains during this period, riskiest stocks during this period; used Plotly and Cufflinks library to perform further analysis on the returns for a particular year, of different banks, Close prices, stock moving averages in the year 2008 and correlation between stocks Close prices
Data Analysis of 911 calls
Analyzed dataset of 911 calls from Kaggle; used Pandas library on Python to create new features to perform different analyses and drawing conclusions such as finding the common reason for the calls to be Emergency Medical Services than traffic or fire; performed data cleaning like converting timestamps to DateTime objects and mapping day numbers to string names, ; used Seaborn and matplotlib libraries for data visualization to study any patterns in calls, based on day of the week and month; other analyses included fitting linear model, creating heatmaps and grouping the data in different possible ways to study any call patterns
Popularity prediction in Twitter
Trained a regression model using data collected from Twitter with hashtags related to the 2015 Super Bowl over a time period and used this to make predictions for other hashtags; to make predictions, the test data used had hashtags in a specified time frame and the model built predicted the number of tweets that contained the hashtag posted within the hour following the time frame; Programmed in Python
Analysis of User’s Friendship Network
Performed network analysis on data sets to analyze users’ personal friendship network on Facebook; analyzed their networks’ community structures and their applications; used fast-greedy, edge betweenness and infomap community detection algorithms; Also performed similar analysis on Google+ networks; programmed in R
Analysis of IMDB database
analyzed networks from IMDB dataset- analyzed the characteristics of the network of actors/actresses and network of movies that included running page rank algorithms on the networks, tagging communities with genres, classifying movies into communities, predicting movie ratings and more; programmed in R and C++
Regression & Classification Analysis
Applied various regression models including linear, random forest, neural network regression on different datasets and evaluated the performances by testing the data using techniques like cross validation; for complex models regularization was used with ridge and lasso regression techniques to handle over-fitting; Also trained a classifier to group a collection of documents with predefined class types into two main classes; used linear and soft margin Support Vector Machines, naïve Bayes algorithm and logistic regression classifiers; programmed in Python programmed in Python
Drowsiness detection for drivers using Computer Vision Designed a system to detect drowsiness in drivers while driving and alert them. Algorithm designed on OpenCV platform in Linux; parameters used were face and eye detection, eye closure and gaze; night-vision supported camera captured and live fed the images to the trained algorithm, which detected the parameters; warning system alerted the driver upon detection of abnormalities PAPERS, CONFERENCES & PRESENTATIONS
Authored and presented a paper titled Minutiae-based Feature Extraction and Recognition of Finger Vein Images at the National Conference on Trends in Instrumentation and Automation-2014, organized by Velammal Engineering College, India ADDITIONAL EXPERIENCE
Teaching Assistant- Cells, Tissues & Organs, Fall 2016, UCLA
Grading Assistant- Principles of Semiconductor Device Design, Fall 2015, UCLA
Placement Coordinator- Department of Electronics & Instrumentation, Velammal Engineering College, 2013-14; Organized numerous group discussions, mock tests and mock interviews for candidates
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