Данный репозиторий включает набор проектов в Jupyter Notebook, выполненных мной самостоятельно во время 6-месячных онлайн курсов Яндекс.Практикум по Аналитике Данных в 2022 году.
Целью создания данного репозитория является сбор Портфолио для поиска новых позиций в области Аналитики Больших Данных. Ссылка на данный репозиторий присутствует в моих резюме на сайтах кадровых агенств.
Язык проектов - Русский.
Язык программирования - Python 3.
Используемые внешние библиотеки - NumPy, Pandas, Matplotlib, Seaborn, SciPy, Plotly & Dash, SQLalchemy, GetOpt, Requests, JSON, BeautifulSoup, ScikitLearn(ML)
Краткое описание состава данного репозитория приведено в таблице ниже (после английской версии текста)
Файлы Jupyter Notebook были протестированы в окружении Anaconda - конфигурационный файл Anaconda добавлен в этот проект здесь.
This multiple projects repository keeps Jupyter Notebook (JN) projects fully done by me during 6 months of Data Analyst hands-on courses by Yandex.Practicum in 2022.
Purpose of this repository: to collect the best examples of my skills in Data Analytics - as a portfolio for my job hunting CVs.
Language of the markdown text in the JN-files: Russian.
Language of the code in the JN-files: Python 3.
Python external libraries used in the JN-files: NumPy, Pandas, Matplotlib, Seaborn, SciPy, Plotly & Dash, SQLalchemy, GetOpt, Requests, JSON, BeautifulSoup, ScikitLearn(ML)
the Jupyter Notebook files in this project were run OK in the Anaconda environment - its backup is also provided in this project here.
| # | Project (dir) name | Project description | Technology stack | Project dir link |
|---|---|---|---|---|
| 01 | DAproj02_-_credit-story-light-study_for-bank-loans-clients | EDA for bank credit history dataset to establish relation between family status and in-time credit return with the goal to build creditability scoring for new clients | Python, Pandas | DAproj02 |
| 02 | DAproj03_-_real-estate-market-study_for-appartments-in-SPB | EDA for real-estate for-sales announcements dataset to establish relation between price per square meter and other parameters of apartments with the goal to build calculator of marketing price for apartments in Saint Petersburg | Python, Pandas | DAproj03 |
| 03 | DAproj04_-_EDA-and-hyposesis-check_for_telecom_CSP-fariffs | EDA and Hypothesis check for datasets with new tariff plans testing by CSP to select more profitable offer with the goal of revenue raise | Python, NumPy, Pandas, Matplotlib, SciPy | DAproj04 |
| 04 | DAproj05_-_video-games-market-EDA_for-gameshop_salesplan | EDA and Hypothesis check for videogames sales history dataset to find influence of games parameters onto market success with the goal to predict next year sales figures | Python, NumPy, Pandas, Matplotlib, SciPy, Seaborn | DAproj05 |
| 05 | DAproj07_-_biz-metrics-EDA_for-global-shop-negative-PnL | Business metrics investigation based on PnL datasets (orders, costs, activity) to find reasons of profitability degradation with the goal to suggest improvement plan | Python, NumPy, Pandas, Matplotlib, SciPy, Seaborn | DAproj07 |
| 06 | DAproj08_-_e2e-AB-test_for-revenue-grow-hypothesis_check | A/B test results analyses with the goal to make statistically reliable conclusions about tested hypothesis and to decide if to stop or continue the testing | Python, NumPy, Pandas, Matplotlib, SciPy | DAproj08 |
| 07 | DAproj09_-_GtM-business-consulting_for-new-restaurant | Business presentation for sponsors of a new startup, based on market research and EDA of Moscow restaurants dataset | Python, NumPy, Pandas, Matplotlib, Seaborn, MS Power Point | DAproj09 |
| 08 | DAproj10_-_sales-funnel-AB-test-analysis_for-internet-shop | To check ground for the expectations of marketing for improvements and to clear fears of management about negative influence of new changes on clients of a food internet shop - based on A/B test results datasets | Python, NumPy, Pandas, Matplotlib, Seaborn | DAproj10 |
| 09 | DAproj11_-_automated-dashboard_for-users-visits-to-ISP-site | e2e automation of data collection and pre-processing pipeline for marketing dashboard also developed here. Link to dashboard | Pandas, SQLalchemy, Tableau Public | DAproj11 |
| 10 | DAproj12_-_ML-in-churn-prediction_for-gym-clients | application of ML tools to churn prediction for gym clients, prediction-model training and client clustering for target group detection in churn prevention | Python, Pandas, SciPy, Matplotlib, Seaborn, Sklearn | DAproj12 |
| 11 | DAproj13_-_telecomEDA-internetABtest-SQL_graduation-diploma | all Data Analytics skills got in half-year courses for Data Analytics by Yandex.Practicum and Link to dashboard | Python, Pandas, Matplotlib, Seaborn, SciPy, Tableau Public, SQL | DAproj13 |