Projects

Jupyter Notebook Praktikum Projects

Table of contents

• Introduction
• Terms of use
• What's the point?
• List of projects
• Requirements
• Installation
• Support

Introduction

Hi there! This is repository with data analyst educational projects from Yandex.Praktikum.

Data analyst educational program includes following topics: preprocessing data, exploratory data analysis (EDA), statistical data analysis (SDA), data collection and storage (SQL), business analytics (metrics and funnels, cohort analysis, unit economics), A/B-testing, data visualization, automatization (scripts, pipelines, dashboards), machine learning basics.

Terms of use

Repository DOES NOT INCLUDE datasets and complete task descriptions according to paragraph 3.1 Yandex.Praktikum Service Terms of use. Projects are presented strictly for informational purposes.

What's the point?

Projects contain useful code tips, i missed during the education. It concerns especially data processing and visualization.

List of projects

№	Project name/Название проекта	Описание/Description
1	Marketing costs optimization according to Yandex.Afisha data.	Clients behavior and marketing costs analysis according to server log data about Yandex.Afisha visits during june 2017 to the end of may 2019, all orders retrieved for this period and marketing costs statistics.
2	Sales funnel and A/A/B-experiment research of mobile application fonts changing.	Analysis of the sales funnel and users behavior. Research of the AAB-experiment results.
3	Moscow catering market research.	Market research based on the open data about Moscow catering, recommendation preparing and presentation making.
4	Analysis and segmentation of regional bank clients according to consuming products.	Statistical hypotheses formulation and checking of difference between clients consuming two products and clients consuming one. User segmentation according to consuming products. Exploratory data analysis.
5	A/B testing of changes related to the addition of the improved internet store recommendation system.	A/B test estimation correctness and results analysis related to the addition of the improved internet store recommendation system.
6	Retrieving data with the SQL-queries from the book publishers database.	Queries composition about books, publishers, authors and user reviews of books for the value proposition of the new product.
7	Analysis and prediction of customers churn with the machine learning.	Prediction of the fitness center clients churn probability, analysis of the churn signs, forming typical clients characteristics, conclusions and recomendtions.
8	Analysis of the internet store income change according to A/B test result.	Hypotheses prioritization and verification for increasing the internet store income change.
9	Python analysis of the data from the air transportation database.	Queries results research of the flights in september 2018 on each type of the airplane and average number of arriving flights per day for each city during august 2018.
10	Historical data analysis of the games sales for the advertising campaigns planning.	Historical data (before 2016) analysis of the games sales, users and critics sores, genres and platforms for the 2017 campaign planning.
11	Clients behavior сomparative analysis of the federal mobile operator tariff plans.	Preprocessing analysis of the federal mobile operator tariff plans on the small clients sample, definition of the best tariff.
12	St. Petersburg real estate market analysis according to Yandex.Realty service data.	Exploratory data analysis of the adverts archive about selling apartments in St. Petersburg for the real estate market price definition and anomalies tracking.
13	Statistics analysis of the clients creditworthiness in the bank credit department.	Research dependence of the family_status and amount of children to the clients creditworthiness for the credit score modeling.

Requirements

Main development tools:

• programming language Python3 and its libraries:
  • Pandas analytical library;
  • libraries for mathematic and scientific computing NumPy, math, SciPy;
  • data visualization libraries Matplotlib, seaborn, plotly;
  • machine learning library sklearn;
  • additional libraries datetime, display, warnings;
• development environment Jupyter Notebook;
• query language (SQL).

Installation

Projects were made on the Yandex.Praktikum online simulator. Anaconda Installation is necessary for using on a local machine. Each project is presented in three versions: Jupyter Notebook, webpage and pdf-file.

Support

[email protected]

Back to the Table of contents