• Exploratory data analysis
• Visualization
• Data preparation (Pandas)
• Feature transformation
• Linear regression
• Curve fitting (Scypy)
• Neural networks (keras, functional API)
• Time series
• Metric selection in line with the industry requirements
• Domain knowledge (shale reserve development)

Google Translate detected! (2022)

"Google Translate detected!" is the battle cry of translators seeing that a translation was done by a computer rather than a human translator (implying that the translation is poor and it is clearly visible).

The purpose of this study project was to train a neural network to tell the difference between human and machine translation.

Skills:

• Data preparation and analysis with Pandas
• Neural networks: model building, hyperparameter selection, evaluation of results (keras)
• Statistical hypothesis testing
• Power of statistical tests
• Languages: Python, R

Reconstructing functions from scanned plots (2022) Many industrial standards and building codes specify caculation procedures to design various structures. Some older (but still valid) documents provide plots (nomograms) for manual determination of various parameters. While formulas can be easily converted into code, manual determination of parameters is inaccurate and time consuming, especially in case of iterative processes. Project purpose: train a neural network to reconstruct function values from plots. Skills: convolutional neural networks.

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Airbnb price prediction (2022)

Kaggle contest for the students of the Data Scientist reskilling course.

Skills:

• Data preparation and analysis with Pandas
• Principal component analysis
• Gradient boosting
• Sklearn pipelines

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Clustering nations by several features (2022)

Study project: optimum clastering of nations by given features.

Skills:

• Principal componet analysis
• Biplots
• K-means clustering
• Selecting the optimum number of clusters

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Checking Zipf's law validity (2022)

According to Zipf's law, the most frequent word in a language or a large body of texts will occur approximately twice as often as the second most frequent word, three times as often as the third most frequent word, etc.

Project purpose: check Zipf's law validity on English and Russian texts as well as the Frequency Dictionary of the Russian language.

Skills:

• Data analysis with Pandas
• Feature transformation to enable linear regression
• Linear regression (Statsmodels)
• Python class definition
• Natural language processing, frequency estimation

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Dictionary conversion (2020)

App for technical translators compiling their own glossaries.

Skills: Python.

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Building foundation design (2020)



Skills:

• MS Excel
• Domain knowledge