Personal Statement Doctor Of Business And Administration

Personal Statement Doctor Of Business And Administration

Write a 300 words short personal statement applying for the DBA program.

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https://www.westcliff.edu/program/college-of-business/dba/

The personal statement requires a minimum length of 300 words and must mention Westcliff University and the program you are applying for. Please discuss why you wish to study at Westcliff University; personal qualifications for your intended program, and adjustments you plan to make in your lifestyle to accommodate your study.

OBJECTIVE

Seeking a data analyst position with significant project experience in business intelligence, big data manipulation and A/B testing. Strong knowledge of data analysis and statistics, and solid programming skills in Python, SQL.

EDUCATION

University of Southern California, Los Angeles, CA Spr 2018-May 2020 Viterbi School of Engineering Master of Science in Financial Engineering Core Courses: Financial Analysis and Valuation, Mathematics and Tools for Financial Engineering, Linear Programming and Extensions

Huazhong University of Science and Technology, Wuchang branch, China Sep 2012-Jun 2016

Bachelor of Engineering in Electrical Engineering Core Courses: Calculus, Linear Algebra, C Language Program Design, Probability Theory and Mathematical Statistics

TECHNICAL SKILLS

Programming Languages: Python (pandas, Numpy)

Applications: Microsoft, Jupyter, SQL, Spark, Tableau

PROJECT EXPERIENCES

Movie Recommendation Engine Development in Apache Spark 1. Built data ETL pipeline based on Spark SQL and conducted online analytical processing for movie rating dataset 2. Leveraged Spark ML to implement distributed recommender system using Alternating Least Square (ALS) 3. Developed user-based approaches to handle cold-start problems and tuned the model hyper- parameters through Spark ML cross-evaluation toolbox which reduced metric root mean square errors by 10%

Banking Customer Churn Prediction and Analysis 1. Developed algorithms for Bank to predict whether a customer is likely to churn 2. Preprocessing data with missing values imputation, one-hot encoding and data standardization, etc 3. Implemented various machine learning models including Logistic regression, K-Nearest Neighbors and Random Forest and evaluated model performance by ROC Curve via k-fold cross- validation method 4. Identified the key factors that influence customer’s churn possibility via analyzing feature importance

 

HONORS

“Dean’s List Recognition”; “Outstanding Student Leaders”; “University-level Advanced Individual in Academic Activities”; “Chairman of Academic Innovation Association, Sch. of Electromechanical”; “Chairman of the Academic Department of the Students’ Union”