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Регистрация: 22.06.2023

Artem Tkachev

Специализация: Data Analyst, Data Scientist
My goal is to apply my knowledge and experience in the field of data analysis to create innovative solutions that can improve business performance and meet user needs. I am passionate about analytics and enjoy working with data, exploring them, identifying key trends, and predicting future events. Additionally, there is tremendous potential for growth and self-fulfillment in the field of analytics, which motivates me to develop in this direction. I have experience in data processing, analysis, and visualization using programming languages such as Python and R, as well as tools like SQL, Anaconda, Jupyter Notebook, and Colaboratory. I am also familiar with the theoretical aspects of machine learning and statistical modeling, enabling me to develop and apply machine learning algorithms to solve data analytics tasks. I have experience working with large volumes of data, including structured and unstructured data, and have worked with data in various industries, including marketing and finance. I believe that one of the key personal qualities required for successful work in the analytics field is analytical thinking. I enjoy diving into complex problems, analyzing data, and finding solutions that can enhance business efficiency. Additionally, I have the ability to effectively communicate with other team members, which helps me work well in a team and present the results of my work to other company employees. I am also responsible and disciplined, allowing me to dedicate sufficient time to thorough data analysis and obtain accurate results.
My goal is to apply my knowledge and experience in the field of data analysis to create innovative solutions that can improve business performance and meet user needs. I am passionate about analytics and enjoy working with data, exploring them, identifying key trends, and predicting future events. Additionally, there is tremendous potential for growth and self-fulfillment in the field of analytics, which motivates me to develop in this direction. I have experience in data processing, analysis, and visualization using programming languages such as Python and R, as well as tools like SQL, Anaconda, Jupyter Notebook, and Colaboratory. I am also familiar with the theoretical aspects of machine learning and statistical modeling, enabling me to develop and apply machine learning algorithms to solve data analytics tasks. I have experience working with large volumes of data, including structured and unstructured data, and have worked with data in various industries, including marketing and finance. I believe that one of the key personal qualities required for successful work in the analytics field is analytical thinking. I enjoy diving into complex problems, analyzing data, and finding solutions that can enhance business efficiency. Additionally, I have the ability to effectively communicate with other team members, which helps me work well in a team and present the results of my work to other company employees. I am also responsible and disciplined, allowing me to dedicate sufficient time to thorough data analysis and obtain accurate results.

Портфолио

analytical report for the HR department

Prepared an analytical report for the HR department. Based on the analysis, made recommendations to the HR department on the recruitment strategy, as well as on interaction with existing employees.

Analytical report on educational programs

Based on the available data, prepare an analytical report that will further help producers of educational programs to effectively build a strategy for modernizing and improving courses.

Customer churn from a telecommunications company

In this study, we addressed the problem of classifying customer churn in a telecommunications company. We conducted exploratory data analysis, identified relationships between various attributes and the target variable. Next, we built five machine learning models for classifying customer churn: logistic regression, k-nearest neighbor, random forest, support vector machine, and gradient boosting. For each model, we used GridSearchCV to find the best parameters through cross-validation. As a result, we found the optimal parameters for each model. Finally, we evaluated the performance of each model on a test set using precision, recall, accuracy, and ROC-AUC metrics. We presented graphs for each metric, which indicated that the gradient boosting model achieved the best overall performance. Thus, we obtained a model that can be used to predict customer churn in a telecommunications company.

Скиллы

Python
SQL
R

Опыт работы

Data Analyst
с 11.2022 - По настоящий момент |Skillbox
Python, SQL, R, A/B Testing, Numpy, Pandas, Scikit-learn, Matplotlib, Seaborn, Plotly, PostgreSQL, JSON

Образование

Информатика и вычислительная техника
2015 - 2018
Таганрогский технологический институт Южного федерального университета

Языки

РусскийРоднойАнглийскийСредний