Angelos Konioris

Advanced understanding of statistical, algebraic and other analytical techniques. Highly organized and diligent with significant background in Statistical Analysis and Machine Learning. Quick learner and adaptable to any situation. Ability to work under pressure which helps me deliver complex tasks on time and meet the deadlines. Always motivated and eager to learn and improve my knowledge around Data Science and ML aspects.

Used statistical methods in Excel and SQL to analyze data and generate business reports in Power BI dashboards.

Identified and documented detailed business rules and use cases based on requirements analysis.

Member of the team, responsible for transferring and reconstructing all the business reports from Excel to Power BI.

Familiarized with version control system like Git and Agile methodologies.

Utilized model deployment frameworks and API's such as Flask and Heroku.

Applied data visualization and forecasting along with supervised and unsupervised ML techniques to provide satisfactory recommendations for a bank marketing campaign, using Python.

Thesis: Data driven design of the next Google Play Store app, which includes several techniques such as Logging, EDA, Dimensionality reduction, ML, Optimization techniques, written in Python.

Supervised and unsupervised ML techniques such as Dimensionality reduction, and Clustering for customers churn prediction written in Python and R-markdown.

Statistical techniques such as T-tests, Anova, Chi-squared on Employee attrition data, using R-markdown.

Visualization techniques on basketball teams data, using R and Tableau.

Data analysis on soccer teams data, using R and SQL