Data Analyst Python

Saif Adeeb

Business & Data Science

Aspiring Data Scientist with 2+ years of analytical experience and currently working towards completing a part-time online Master's degree in Data Science. My goal is to transition into a role that allows me to apply my skills and knowledge in analytics and machine learning to solve challenging problems.

*************@*****.*** (408) 497 - 8007 San Mateo linkedin.com/in/saif-adeeb WORK EXPERIENCE

04/2018 – Present

Data Analyst

Samba TV

San Francisco, CA

Leveraged big data to derive insights and identify user behavior trends, gaps, and inconsistencies.

Produced reports to measure effectiveness of ad campaigns and communicated results to cross-functional teams.

Worked with Data Scientists to develop methodologies to create synthetic control groups for A/B Testing.

Experienced in writing and deploying production code. Tools: Python, Spark, SQL, Microsoft Excel, Git, PyCharm 05/2017 – 04/2018

Data Analyst

Cave Consulting Group

San Mateo, CA

Improved internal processes by creating scripts to automate data manipulation and other tasks.

Produced databases, tools, queries, and reports to analyze and summarize data to identify providers with the greatest savings potential. Built reports to identify savings for clients. Assisted in presenting savings reports to upper management and operations team to improve strategies and operations.

Experienced with manipulating, cleaning, and analyzing data. Tools: Python, SQL, Microsoft Excel, Qlikview

08/2016 – 05/2017

Item Setup Specialist

Walmart eCommerce

San Bruno, CA

Set up items across many different departments by creating items in eCommerce databases through QMF, PIM, and Retail Link. Ran weekly reports to analyze data's validity across different databases using SQL and Microsoft Excel.

Analyzed and interpreted data to identify costs, common issues, and team's efficiency for each department.

Tools: SQL, Microsoft Excel

01/2016 – 06/2016

Undergraduate Research Assistant

University of California, Davis

Davis, CA

Forecasted weather using solar irradiance data from the Pacific Northwest using time series analysis.

Created functions for weather forecasting. Created directional plots to visualize data using ggplot.

Presented findings at a UC Davis Statistics Conference. Tools: R

PROJECTS

Predicting House Prices in Ames, Iowa (08/2018 – 09/2018) Goal: Build a model to predict the sale prices of residential homes in Ames, Iowa based on features of the homes.

Used correlation plots and visualizations to examine relationships among different features and homes' sales prices.

Trained and tuned Ridge, Lasso, Elastic net, XGBoost, and Ensemble regression models to find the best performing model. Compared the models' prediction accuracy and found that the Ensemble model achieved the best results.

Tools: Python

What's Cooking? - Recipe Cuisine Prediction

(06/2018 – 08/2018)

Goal: Build a classification model to categorize recipes into cuisines based on the ingredients.

Used TF-IDF to extract and generalize features from list of ingredients. Trained and tuned Random Forest, Logistic Regression, and Support Vector Machine models to find the best performing classification model. Compared the models' categorization accuracy and classification reports and found that the Support Vector Machine model yielded the best categorizing results.

Tools: Python

MNIST - Digit Recognizer (05/2018 – 06/2018)

Goal: Build a classification model to classify handwritten digits from the MNIST dataset.

Used convolutional neural networks and artificial neural network to build classification models. Designed an experimental design to examine the effect of classification accuracy by altering nodes and layers for artificial neural networks and max pooling layers and kernel sizes for convolutional neural networks.

The experimental design showed that convolutional neural networks yielded the best results.

Tools: Python, TensorFlow

Decision Rules for Harvesting Abalones

(10/2017 – 12/2017)

Goal: Conduct an alternative approach to a previously unsuccessful study on predicting the age of abalones.

Hypothesized reasonings for the unsuccessful study and used findings from the exploratory data analysis to support conclusions. Compared models using false positive and false negative rates to identify the model that yielded the lowest error rates. Tools: R

EDUCATION

06/2017 – Present

M.S. in Data Science

Northwestern University

Expected Graduation: Spring 2019

09/2014 – 06/2016

B.S. Managerial Economics, Statistics minor

University of California, Davis

SKILLS

Python R SQL Spark Machine Learning

QlikView Tableau Microsoft Office Suite

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