Hello! I’m a PhD-level Data Analyst with a strong academic background in Statistics and Mathematics, and a broad skill set that spans Statistical Analysis, Machine Learning, Natural Language Processing (NLP), Time Series Modeling, and Mathematical Modelling. My passion lies in solving complex problems by leveraging data to uncover insights that drive business success. With deep expertise in mathematical modeling, machine learning, and deep learning, I can deliver high-impact solutions tailored to your needs.
What I Offer:
• Advanced Statistical & Mathematical Modeling: I combine statistical theory with practical experience to build and implement models that predict, optimize, and solve real-world challenges. My PhD in Statistics and double major in Mathematics enables me to apply the most sophisticated methods to your data, ensuring highly accurate and reliable results.
• Machine Learning & Deep Learning: I’m proficient in building advanced machine learning models using Scikit-learn, TensorFlow, and PyTorch for classification, regression, clustering, and other predictive tasks. My deep learning expertise allows me to create state-of-the-art algorithms for tasks like image classification, object detection, and NLP.
• Time Series Forecasting: Whether you need to predict sales, stock prices, or any time-dependent data, I use the latest statistical techniques to forecast with precision and provide actionable insights.
• Natural Language Processing (NLP): I apply cutting-edge NLP techniques to process and analyze unstructured text data, enabling tasks like sentiment analysis, text classification, and language modeling.
• Image Classification & Computer Vision: Leveraging deep learning algorithms, I help businesses extract valuable insights from visual data, perfect for applications in medical imaging, security, or content categorization.
Why Work With Me:
• PhD Expertise: My deep academic foundation ensures I use the latest, most effective statistical methods to solve complex problems.
• End-to-End Solutions: From data wrangling and preprocessing to model deployment and cloud integration, I offer a comprehensive, end-to-end approach.
• Customized Approach: I understand that every business has unique needs. I tailor my solutions to fit your specific requirements and challenges.
• Clear Communication & Collaboration: I ensure that my results are understandable and actionable, providing you with insights in a clear, concise manner, and I’m committed to collaborating closely with you throughout the project.
If you’re looking for a highly skilled and results-driven Data Analyst with the technical expertise and academic background to solve your data challenges, let's connect! I’m ready to help you turn your data into a strategic advantage.
Feel free to reach out for a consultation, and let's get started!
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Học vấn
Gazi Üniversitesi
2009 - 2012
•
3 năm
PhD
Turkey
2009 - 2012
•
3 năm
Ấn phẩm
A sharp bound for the ergodic distribution of an inventory control model under the assumption that
2014
Journal of Inequalities and Applications
In this study, a stochastic process which represents a single-item inventory control model with -type policy is constructed when the demands of a costumer are dependent on the inter-arrival times between consecutive arrivals. Under the assumption that the demands can be expressed as a monotone convex function of the inter-arrival times, it is proved that this process is ergodic, and closed form of the ergodic distribution is given. Moreover, a sharp lower bound for this distribution is obtained.
2014
ASYMPTOTIC RESULTS FOR AN INVENTORY MODEL OF TYPE s, S WITH A GENERALIZED BETA INTERFERENCE OF CHAN
2011
TWMS Journal of Applied and Engineering Mathematics
In this study, asymptotic expansion for ergodic distribution of an inventory control model of type s, S with generalized beta interference of chance is obtained, when S − s → ∞. Moreover, weak convergence theorem is proved for ergodic distribution. Finally, the accuracy of the asymptotic expansion is examined with Monte Carlo simulation method.
2011
A 0-1 integer programming approach to a university timetabling problem
2008
Hacettepe Journal of Mathematics and Statistics
The aim of this model is to minimize the dissatisfaction of students and lecturers whilst at the same time implementing rules bounded by a set of constraints. The model produced has flexibility in terms of embracing new rules and/or criteria.
2008
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