👋 Hi! I am Nikolay Maltsev - Data Analyst from Russia, Saint-Petersburg
- I love learning new things, finding new insights in data 💡 and visualise them 🔮
- Data analytics for me is the best way to discover the world 🌍
- Besides DA, my hobby is bicycle and hiking 🏕️
📚 My education in IT:
- Karpov.courses: Data analyst
- Sololearn: Data Programming
- Sololearn: Start Python
- Stepic: Basics of Statistics
🛠️ Languages and tools:
📫 My contacts:
👩🏻💻 My projects:
| Project name | Summary | Stack |
|---|---|---|
| E-commerce project | In this project, online store data is analyzed, various metrics (including Retention) are calculated and RFM-analysis is carried out. | Python (used libraries: pandas, seaborn, requests et al) |
| Mobile games data analysis project | As part of the educational project, the indicators of a mobile game are analyzed and a function is written to calculate retention. A statistical analysis of the data from the conducted A/B test was carried out. Based on the available data, it was determined which option can be considered the best and based on what metrics it is worth making the right decision. Metrics for assessing the results of the last thematic event in the game are described. | Python (used libraries: pandas, seaborn, numpy, statsmodels, pingouin, scipy et al.) |
| Delivery app A/B-test | Analysis of A/B test data conducted in grocery delivery app conducted to test a new recommendation system. To test the effectiveness of the recommendation system, an A/B test was conducted. There was a task: to evaluate whether the new recommendation system could benefit the business and application users. For this purpose, metrics that are responsible for the quality of service were selected and statistically compared in 2 groups. The source data contains logs of user orders. Based on the results of the analysis, it is recommended to roll out the recommendation system for all users. | Python (used libraries pandas, seaborn, statsmodels, scipy et al) |
| Dating app A/B-test | The project analyzed the results of an A/B test which was conducted for the new mechanics of a dating application (the application team developed a new algorithm for finding the most suitable profiles). The mechanics of the application are as follows: users see each other’s profiles in the application and can like or dislike each other. If users like each other, this is called a match, and users have the opportunity to get to know each other. Based on the results of the analysis, it was recommended to roll out the new mechanics for all users | Python. Used libraries pandas, seaborn, statsmodels, scipy stats |
| Video games airflow DAG | Airflow DAG which regularly checks most popular games, platforms, publishers, count of games etc | Python (used libraries: pandas, numpy, datetime, used io module) |
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