This is a repository with all data analysis projects that I am able to share. Some of them I have completed during my education at "Yandex Practicum" as an Data Analyst. Each folder contains one or more jupyter notebooks as well as a README file with description of the project, conclusions, description of data used, as well as a list of python libraries and tools used in that project.
Note: the projects present mostly contain text in RUSSIAN language, and are present to showcase coding and analytical skills.
| Project name | Project Description | Libraries and tools used |
|---|---|---|
| 1.Big cities music | Comparing preferences and music consumption patterns of users from Moscow and Saint-Petersburg | pandas |
| 2.Evaluating potential borrowers reliability | Analysing the effect of various factors (marital status, salary, etc.) on borrowers reliablity and likelyhood to skip a loan payment. | pandas, tableau |
| 3.Real estate market analysis | Analysing the real estate market of Saint-Petersburg | pandas, matplotlib, seaborn, tableau |
| 4.Telecom tariffs analysis | Analysing user behaviour and determening the profitability of various tariffs for the company | pandas, numpy, matplotlib, seaborn, scipy |
| 5.Game marketplace analytics | Determining key patterns in game successes based on historical data on sales | pandas, numpy, matplotlib, seaborn, scipy, tqdm |
| 6.Analyzing 'ProcrastinatePRO+' app financial losses | Determine the reason company is losing money despite large investments into marketing and give recommendations on how to improve financial situation | pandas, numpy, datetime, matplotlib, seaborn |
| 7.Revenue optimization and A/B testing | Prioritizing and testing hypothesis with a goal to increase shop's profitability and margins | pandas, numpy, matplotlib, seaborn, scipy, datetime |
| 8.Moscow catering market analysis | Analyzing Moscow's catering market for investors willing to open a new place in the city | pandas, numpy, matplotlib, seaborn, math, re, plotly, folium, geojson, PowerPoint |
| 9.Analyzing user behaviour in a mobile app | Analyzing the sales funnel and results of A/A/B testing in order to determine profitability of proposed changes | pandas, numpy, math, scipy, matplotlib, seaborn, datetime, plotly |
| 10.Creating a dashboard for a news aggregating service | Creating a dashboard displaying several key metrics of user interaction with topics on Yandex.Dzen platform as well as a presentation. | pandas, matplotlib, seaborn, sqlalchemy, PostgreSQL, PowerPoint, Tableau |
| 11.TED-Talks analytics | Creating a story with 4 dashboard displaying various data on TED-talks. | Tableau |
| 12. Final project | Analyzing user behaviour in an mobile marketplace app, the results of a A/B test and writing SQL queries | pandas, numpy, math, requests, datetime, scipy.stats, matplotlib, seaborn, plotly, warnings, sqlalchemy, PowerPoint, Tableau |