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nktnlx / repository
This is a repository that I have created to showcase skills, share projects and track my progress in Data Analytics / Data Science related topics.
Hi, I'm Aleksandr! I have a technical background and hold a Master of Science (M.S.) degree in Geoscience from Saint Petersburg Mining University, with a major in Seismic Data Processing and Analysis. I have 12 years of experience in the field of geoscience, where I held roles such as Data Processing Engineer, Head of Data Processing Department and CTO in a technological startup. Currently I'm an A/B-testing Platform Lead in a Fintech company. Here is my career path in Data Science:
My article on Habr on matching two open data datasets with help of Machine Learning ---> ENG | RUS
My Data Analytics blog on Medium
My CV in pdf
In this section I will provide links to my github repositories containing code and jupyter notebooks I created while passing online courses or while just having fun with data and code.
Description: Machine Learning Engineer course by karpov.courses X AI Talent Hub ITMO University.
This is a 9 month course designed for data professionals. This course is an exhaustive basis for a career in Machine Learning and Data Science. The course program is built in a way that a graduate of the course can perform any stages of an ML engineer's work: from data collection and the application of classical algorithms, to training neural networks and A/B testing.
Repository: Check the repository having my solutions on Machine Learning Engineer course tasks and challenges ---> go to repo..
Status: Completed in June 2025 (please, check the certificates section below).
Description: Advanced Data Analysis course (Hard DA) by karpov.courses.
This is a 6 month course designed for Middle Data Analysts who wants to advance to Senior positions. The course is focused on product-oriented approach to BI reporting, big data processing and working with DWH team, advanced A/B testing techniques and Machine Learning approaches for solving analytical problems.
Repository: Check the repository having my solutions on Advanced Data Analysis course tasks and challenges ---> go to repo..
Status: Completed in January 2024 (please, check the certificates section below).
Description: Advanced A/B testing course by karpov.courses.
This self-paced course explores such topics as Basics of Statistics, Hypothesis testing, Experimental design, Design testing, Confidence intervals, Improving test sensitivity, Metric selection, Cuped, Stratification, Multiple testing, Traffic splitting, Analysis of ratio metrics (Linearization and Delta Method) and a Complete A/B testing pipeline. All with an extensive coding practice in Python.
Repository: Check the repository having my solutions on Advanced A/B testing course tasks and challenges ---> go to repo..
Status: Completed in June 2023 (please, check the certificates section below).
Description: Algorithms Training 3.0 by Yandex. The course official page.
This 1 month algorithms and data structures coding journey explores such topics as stacks, queues, dynamic programming, graphs, DFS, BFS, etc.
Repository: Check the repository having my notes and solutions on Algorithms Training 3.0 by Yandex based on the course lectures, tasks and materials ---> go to repo..
Status: Completed in April 2023 (please, check the certificates section below).
Description: Data Structures & Algorithms in Python on Udacity by Google. The course official page.
This 1 month course introduces common data structures and algorithms in Python. It overviews frequently-asked technical interview questions and teaches how to structure your responses.
Repository: Check the repository having my notes and useful links on Data Structures & Algorithms in Python based on the course lectures, tasks and materials ---> go to repo..
Status: Completed in March 2023 (please, check the certificates section below).
Description: A/B Testing Course by Google. The course official page.
This 1 month course covers how to choose and characterize metrics to evaluate your experiments, how to design an experiment with enough statistical power, how to analyze the results and draw valid conclusions.
Repository: Check the repository having my notes and useful links on A/B testing based on the course lectures, tasks and materials ---> go to repo..
Status: Completed in February 2023 (please, check the certificates section below).
Description: Machine Learning in Python with scikit-learn by France Université Numérique. The course official page.
This 3 month course is an in-depth introduction to predictive modeling with scikit-learn. Step-by-step and didactic lessons introduce the fundamental methodological and software tools of machine learning, and is as such a stepping stone to more advanced challenges in artificial intelligence, text mining, or data science.
Repository: Check the repository having jupyter notebooks with the course lectures and tasks' solutions ---> go to repo..
Status: Completed in May 2022 (please, check the certificates section below).
Description: 30 days of Machine Learning by Kaggle. The course rapidly covers the most essential skills needed to get hands dirty with data and quickly learn how to build machine learning models.
Repository: Check the repository having jupyter notebooks with the course tasks' solutions ---> go to repo..
Status: Completed in August 2021 (please, check the certificates section below).
Description: This is a 5 month specialization by karpov.courses. The specialization includes Python, API, Git, Airflow, SQL, Statistics, A/B testing, Visualization, Product development and Product Analytics modules.
Repository: Check the repository having 37 data analysis mini-projects ---> go to repo..
Status: Completed in July 2021 (please, check the certificates section below).
Description: This is a 3 month course by Tinkoff Education. The course was created for Moscow State University Faculty of Mechanics and Mathematics students and includes following topics: Introduction to Data Analysis, SQL, Data Visualization in Python, A/B tests, Data Interpretation, Models, Logistic regression, Mobile Analytics, Random Forest, etc..
Repository: Check the repository having my code and solutions for home-tasks and projects of the course ---> go to repo..
Status: Completed in May 2021 (please, check the certificates section below).
Description: SQL queries for tasks from codecademy, sql-ex.ru, stepik, sql module on Yandex Praktikum, etc.
Repository: Check the repository having 400+ SQL queries ---> go to repo..
Status: Some of the courses are still in progress.
Description: 25 projects, 154 hours, 300 topics python developer track from JetBrains Academy
Repository: Check the repository having 11 completed projects including: Hangman, Tic-Tac-Toe, Rock-Paper-Scissors games; Matrix calculator, own-coded Regex engine, To-Do list, etc. ---> go to repo..
Status: Completed 11 projects, studied 116 topics from the track to practice my python skills. Will revert back to the track later.
Description: 20 weeks Computer Science Career Path from Codecademy. The career path includes following topics: command line commands, git, python 3, OOP, linear data structures, complex data structures, asymptotic notation, recursion, sorting algorithms, search algorithms, graph search algorithms.
Repository: Although the career path has been already completed the repository is still under development, having only 9 listed projects including: words statistics calculator, English nouns pluralizer, Eng
Selected from shared topics, language and repository description—not editorial ratings.
Olow304 /
The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run this collections either in Jupyter notebook or python alone.
Komal01 /
Phishing website detection system provides strong security mechanism to detect and prevent phishing domains from reaching user. This project presents a simple and portable approach to detect spoofed webpages and solve security vulnerabilities using Machine Learning. It can be easily operated by anyone since all the major tasks are happening in the backend. The user is required to provide URL as input to the GUI and click on submit button. The output is shown as “YES” for phishing URL and “NO” for not phished URL. PYTHON DEPENDENCIES: • NumPy, Pandas, Scikit-learn: For Data cleaning, Data analysis and Data modelling. • Pickle: For exporting the model to local machine • Tkinter, Pyqt, QtDesigner: For building up the Graphical User Interface (GUI) of the software. To avoid the pain of installing independent packages and libraries of python, install Anaconda from www.anaconda.com. It is a Python data science platform which has all the ML libraries, Data analysis libraries, Jupyter Notebooks, Spyder etc. built in it which makes it easy to use and efficient. Steps to be followed for running the code of the software: • Install anaconda in the system. • gui.py : It contains the code for the GUI and is linked to other modules of the software. • Feature_extractor.py: It contains the code of Data analysis and data modelling. • Rf_model.py: It contains the trained machine learning model. • Only gui.py is to be run to execute the whole software.
My_homebrewed_NOTEBOOKS is a GitHub repository that houses a collection of personal notebooks derived from various sources, including Kaggle and Jupyter Notebooks. This repository serves as a curated collection of notebooks created and customized by the repository owner, providing a valuable resource for learning and exploring different topics.
Course link: https://cognitiveclass.ai/courses/python-for-data-science/ Python for Data Science these are notebooks from the course This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours. Upon its completion, you'll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this free course is for you.
shreeparab1890 /
This is a Jupyter Notebook( iPython Notebook) with Data Analysis (EDA) on FIFA WC Qatar 2022 match data.
aarushiksk /
Welcome! This is a repository for adding your python projects, data analysis/data science projects or jupyter/kaggle notebooks.