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Course work on Data Science

Pandas, NumPy, Matplotlib, Seaborn, Plotly. Exploratory Data Analysis (EDA), Feature Engineering, model building in Scikit-learn, Jupyter Notebook and Google Colab.

from UAH 3,500 Term from 7 days

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Directions of coursework in Data Science

From loading and cleaning data to building predictive models. Complete analysis pipeline with interactive visualizations and reasoned conclusions.

Pandas / NumPy

Data processing and transformation: CSV, JSON, Excel loading, gap cleaning, normalization, aggregation and pivot tables. NumPy vectorized computing for performance.

from UAH 3,500

Data visualization

Matplotlib for static graphs, Seaborn for statistical charts, Plotly for interactive dashboards. Heatmap, boxplot, scatter, distribution histograms.

from UAH 3,500

EDA is exploratory analysis

Full-fledged Exploratory Data Analysis: feature distributions, correlation matrix, outlier detection, gap analysis, statistical tests and hypothesis formulation.

from UAH 3,500

Feature Engineering

Generating new features from raw data: coding categorical variables, scaling, polynomial features, temporal features, PCA for dimensionality reduction.

from UAH 4,000

Scikit-learn

Classification (Random Forest, SVM, Logistic Regression), regression (Linear, Ridge, Gradient Boosting), clustering (K-Means, DBSCAN). Selection of hyperparameters and cross-validation.

from UAH 4,000

Jupyter Notebook / Google Colab

Interactive environment with explanations in Markdown, reproducible analysis, clear code with comments. GPU support in Colab for large datasets.

from UAH 3,500

How we work

TK analysis

We study the methodology, choose a dataset, determine the target variable and metrics of the quality of the analysis

EDA and training

We conduct intelligence analysis, clean data, create visualizations of distributions and correlations

Modeling

We build models, compare algorithms, select hyperparameters and evaluate quality

Demonstration

We show a Jupyter Notebook with the results, explain each step, you pay after checking

What is included in the Data Science course

Jupyter Notebook with a complete analysis pipeline
Data cleaning and preparation (Pandas, NumPy)
Exploratory Analysis (EDA) with visualizations
Feature Engineering and feature selection

Building and comparing models (Scikit-learn)
Interactive graphs (Plotly, Seaborn, Matplotlib)
Documentation and explanatory note
Free edits and defense preparation

Reviews of Data Science courses

"I ordered a course on EDA on the Airbnb dataset. Pandas, Seaborn, correlation analysis, anomaly detection. Jupyter Notebook is designed perfectly - the teacher praised the structure and conclusions."

Andriy V.

KNU named after Shevchenko, Kyiv

"I needed a course on data visualization: Plotly dashboard with interactive graphs. Made in 8 days, added a Streamlit application for presentation. Protection went perfectly!"

Marina L.

NaUKMA, Kyiv

"Scikit-learn course: bank customer classification. Random Forest, XGBoost, cross-validation, ROC-AUC. Clean code, clear comments, high quality metrics."

Oleksiy T.

Politechnika Wrocławska, Wrocław

All reviews

Frequently asked questions about Data Science courses

Data Science is a broader discipline that encompasses the entire cycle of working with data: collection, cleaning, transformation, visualization, statistical analysis, and model building. Machine Learning is a subset of Data Science that focuses exclusively on learning algorithms. The Data Science course focuses on EDA, visualization and interpretation of results, while the ML course focuses on building and optimizing predictive models.

We work with open datasets from Kaggle, UCI Machine Learning Repository, Google Dataset Search, as well as with state open data (data.gov.ua). If your TK provides for a specific dataset or topic, we will use it. For each project, we choose a data set that best meets the research question and the requirements of the methodology.

Full set: Jupyter Notebook with reproducible analysis pipeline, data cleaning and preparation (Pandas), exploratory analysis (EDA) with detailed visualizations (Matplotlib, Seaborn, Plotly), Feature Engineering, statistical tests (scipy.stats), model building and comparison (Scikit-learn), quality assessment (accuracy, F1, ROC-AUC), explanatory note and requirements.txt.

Yes, if necessary, we create interactive visualizations with Plotly and Plotly Express: graphs with zoom, hints when hovering, filters. We can also add a Streamlit or Plotly Dash application for presenting the results of the analysis with an interactive interface - this adds points to the defense.

The standard execution time is from 7 days, depending on the volume of the dataset, the number of stages of analysis and the complexity of the models. Larger Feature Engineering and Ensemble Modeling projects may take 10-14 days. Urgent delivery in 3-5 days is possible for an additional fee. The price is from UAH 3,500.