In this section I will list data analytics projects briefly describing the technology stack used to solve cases.
Skills: data cleaning, data engineering, data analysis, statistics, data visualization, machine learning, git
Technology: Python, Pandas, Numpy, Seaborn, Matplotlib, Scikit-learn.
Results: Found patterns and insights in the data, built a linear regression, carefully documented and built a dashboard based on metrics
- Movie recommendation system: a model that can offer a movie to a client based on similar movies viewed
- Weather predictions: after analyzing the data, I built a visualization, as well as also built an ML model for predicting temperature based on data
- Predicting the number of Olympic medals: the project shows the ability to work with ML models and an understanding of how models work
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Programming: Python, R, SQL
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Data Workflow: Pandas, Numpy, Matplotlib, Plotly, Seaborn, PySparkSQL, Scikit-Learn
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Deployment: Git, Docker
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Extra: Power BI, TableAu, Superset, Excel, A/B testing
I am sure that the best way to show your skills is by doing practical exercises, however courses plays also such an important role. So here is the list of them, that I've finished recently.
- Interactive SQL Trainer (2022) (Stepik)
- SQL for everyone (2022) (Stepik)
- SQL for Data Analysis (2023) (Udacity)
- ML course for beginners (2023) (Stepik)
- A/B testing (2023) (Udacity)
- Data Science Math Skills (2023) (Coursera)
- Advanced SQL (2023) (Kaggle)
- Data Cleaning (2023) (Kaggle)
- Intro to Machine Learning (2023) (Kaggle)
- Intermediate Machine Learning (2023) (Kaggle)
- SQL (Advanced) Certificate (2023) (HackerRank)