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Mark Krickovich

I'm Mark Krickovich, from California USA. I am a Data Science and Finance Professional. I am passionate about Data-Prediction Sciences and All-Things Finance. I enjoy learning new Python and Data Science Skills everyday such as Pandas, Numpy, Neural Networks, and many other. I am an experienced professional with a background in Business Valuation, Financial Planning & Analysis (FP&A), Complex Financial Modeling (in Excel and Python) and, beginning in 2018, Data Science. I come to the Data Science field as a Finance Quant of sorts. I am seeking challenging opportunities to work in the Data Science and Finance fields.

Skills and Experience

  • Python for Data Science
  • Supervised & Unsupervised Machine Learning
  • Reinforcement Machine Learning
  • Power BI
  • MS Excel
  • Financial Planning & Analysis

Accredited Data Science Education

I recently completed a rigorous Data-Science Certification program through University of California, San Diego, which was awarded to me in March 2020 (see URL below). This learning expereince provides students with the skills to design, build, verify, and test predictive data models to make data-driven decisions in any industry. https://extension.ucsd.edu/courses-and-programs/data-mining-for-advanced-analytics

Key Program Topics:

- Model training, testing, and evaluation
- Decision tables and trees
- Classification rules
- Association rules
- Bayesian learning
- Numeric prediction
- Clustering
- Ensemble learning
- Artificial neural networks
- Hidden Markov models
- Support vector machines

EXAMPLES OF MY WORK - Please Check out my Data Science Project Portfolio

https://github.com/MarkKrickovich/MarkKrickovich.github.io/blob/main/index.md

Connect with me on LinkedIn

Mark Krickovich's Projects

associationrulemining icon associationrulemining

Machine Learning Projects for Association Rule Mining, a Data-Mining Technigue that Finds Patterns in Data

quick-portfolio icon quick-portfolio

Use this template if you need a quick developer / data science portfolio! Based on a Minimal Jekyll theme for GitHub Pages.

recommendersystem icon recommendersystem

Film Recommender System. A series of notebooks to deep dive into the MovieLens data set using the surpriselib scikit for recommender systems. Content Based Filtering and Collaborative Neighborhood Based Filtering presented. Film Recommender System

recurrent-neural-network icon recurrent-neural-network

Recurrent Neural Network is used with Python, TensorFlow and the Keras API to classify if a written film description was positive or negative.

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