Shalini Singh

214-***-**** adivga@r.postjobfree.com linkedin.com/shalinisingh github.com/itsmeshalini EDUCATION

The University of Texas at Dallas – Richardson, TX Dec 2020 M.S., Information Technology and Management GPA 3.8 Maharishi Dayanand University – Delhi, India August 2013 B.Tech, Electronics & Communications GPA 3.7

TECHNICAL SKILL

Certificates Deep Learning Specialization (Andrew Ng), IBM Data Science, Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning (Laurence Moroney), Applied Machine Learning in Python (University of Michigan), Tableau data scientist badge, Analyzing and visualizing Data with Microsoft Power BI

Programming and

Statistical Tools

Python, SQL, R, SAS, Stata, Excel

Visualization Tools Tableau, Power BI, Google Analytics Databases MySQL, Microsoft SQL Server, PostgreSQL, MongoDB Big Data Hadoop, Hive, Impala, Sqoop, Apache Spark, Flume, Pig, PySpark IDE/Cloud Platform Jupyter Notebook, Google Colab, Data Bricks, R Studio Machine Learning Regression, Classification, Clustering, Association, Simple Linear Regression, Multivariate linear Regression, Polynomial Regression, Decision Trees, Random Forest, Logistic Regression, K-NN, K-Means, Kernel SVM, Gradient Descent, Backprop, Feed Forward ANN, CNN, RNN Deep Learning

Framework

TensorFlow, Keras

Version Control Git, SVN

PROFESSIONAL EXPERIENCE

Data analyst, Eureka Service (Volunteering) August 2017 – Nov2018

• Successfully conducted and managed Google Ad grants Online Marketing campaign using Search Engine Marketing (SEM) technique.

• Implemented relevant keywords and ad copies to improve the click through rate (CTR) for the website.

• Monitored important metrices like Impressions, Clicks, Conversion Rates, Click Through Rate to evaluate performance of different ad copies.

Engineer, Vihaan Networks Ltd., (Delhi, India) Sept 2015 – June 2017

• Performed K-Nearest Neighbors(KNN) algorithm based on the similarities of the location categories where the device is installed.

• Implemented logistic regression model using scikit learn to predict the various critical alarms that were responsible for device operational shutdown with precision upto 70% and F-1 Score upto 72% Graduate Engineer Trainee, Vihaan Networks Ltd., (Delhi, India) Sept 2014 – Sept 2015

• Cleaned and performed Exploratory Data Analysis on device performance data using Python libraries NumPy and Pandas

• Integrated database with Java application (JDBC), and performed data visualization and generated reports for the derived insights using Tableau to help senior management track operational performance of device Engineer Intern, Vihaan Networks Ltd., (Delhi, India) August 2013 – Sept 2014

• Automated the unit level testing (python, vbscript) for anomaly detection and outliers from the data

• Authored test plans (Excel) and executed 10 cycles different functional testing to ensure that device functions properly at unit level and integration level

PROJECTS (2019 – present) GitHub

Marketing Analytics for Laundry Detergent brand [SAS Studio, Tableau]

• Predictive and prescriptive analytics for customer purchase behavior between top 6 detergent brands

• Recommended store display promotion based on logit model on SAS, maximizing likelihood customer chooses the brand

• Determined which brand would sell better depending on different promotional methods using logistic regression Image Classifier using Deep Neural Network [Python, NumPy, TensorFlow, Keras, scikit-learn, matplotlib]

• Utilized Convolution Neural Networks(CNN) to implement a machine learning image recognition component, Implemented Backpropagation in generating accurate predictions

• Implemented CNN along with Pooling to compress and filter down to important feature to distinguish image and improve prediction.

• Implemented ImageGenerator from Keras library to auto label the images of different sizes and aspect ratio in dataset Linear & Logistic Regression using Gradient Descent [Python, NumPy, scikit-learn, matplotlib]

• Implemented linear and logistic regression models using gradient descent algorithm with batch update

• estimated the global minimum convergence of cost function and RMSE values, evaluated optimal beta coefficients, and hyperparameters like learning rate, threshold and cut off probability and Performed feature selection to get the best predictors

Implementation of SVM, Decision Trees & Ensemble methods [Python, NumPy, scikit-learn, matplotlib]

• Built a ML model using Support Vector Machines and k-fold cross validation to evaluate Kernel performance of an Operating System.

• Experimented with various kernels (linear, polynomial, RBF), Tree models, Ensembles (Random Forest, AdaBoost, XG Boost, etc) and evaluated the best model.

Implementation of Artificial Neural Networks, Clustering and K-NN [Python, NumPy, TensorFlow, Keras, scikit-learn, matplotlib]

• Implemented Artificial Neural Networks using Tensorflow and Keras on a Kaggle dataset to classify risk factor of a firm.

• Experimented with activation functions (relu, sigmoid, tanh), plotted learning curves for different number of layers, nodes, and neighbors.

• Used K-Nearest Neighbor to classify data points based on distance metrics (Euclidean, Manhattan) LEADERSHIP AND ORGANIZATIONS

Orators at UTD, UT Dallas – Treasurer Training & Placement student Coordinator - MDU

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