my-portfolio-in-DA-for-CV-in-RUS

ENGLISH FOLLOWS

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)

Совместимость версий библиотек

Файлы Jupyter Notebook были протестированы в окружении Anaconda - конфигурационный файл Anaconda добавлен в этот проект здесь.

ENGLISH:

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)

Compatibility issues

the Jupyter Notebook files in this project were run OK in the Anaconda environment - its backup is also provided in this project here.

Repository Content / Проектный состав данного репозитория

#	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

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