Data science consulting services

Our unique approach to data science projects is a comprehensive and iterative process that includes the key stages of data selection, data sourcing, data synthesis, data engineering, data modeling, and operational optimization. Here’s a breakdown of our data science consulting approach:

Data management

Selecting. A data science consultant meticulously identifies and selects relevant data sources based on project requirements, ensuring that the chosen data aligns with project and business objectives and provides valuable insights.

Sourcing. Our data science consulting team acquires data from various sources, including databases, APIs, external datasets, and internal data repositories, employing suitable data collection methods to gather the necessary information.

Synthesizing. A data science consultant integrates and combines different datasets to create a unified and comprehensive view of the data, ensuring its consistency and eliminating redundancies.

Data engineering

Exploring. Our consulting firm dives deep into the data, exploring its characteristics, distributions, and relationships through exploratory data analysis techniques.

Cleaning. We apply data cleaning techniques to handle missing values, outliers, and inconsistencies, ensuring data integrity and improving the quality of the dataset.

Normalizing. Yalantis’ data science consultant normalizes the data by transforming it into a standardized format, facilitating effective analysis and comparison across variables.

Feature engineering. We develop new features or transform existing ones to enhance the predictive power of data. This involves extracting relevant information, creating derived features, and encoding categorical variables, among other techniques.

Scaling. Our data science services also include scaling data appropriately to ensure that all features are on a similar scale, minimizing bias, and improving the performance of machine learning models.

Data modeling

Selecting a model. A data science consulting team carefully selects the most appropriate machine learning or statistical model based on the business need, considering factors such as data characteristics, business requirements, and performance goals.

Training. Our ML engineers train the selected model using the prepared data, optimizing model parameters and hyperparameters to achieve the best possible performance.

Evaluating. A data scientist evaluates the trained model using appropriate metrics to assess its accuracy, precision, recall, or other relevant criteria. This helps our data science consultant gauge the model’s performance and identify areas for improvement.

Tuning. Our data science services also involve fine-tuning the model by iteratively adjusting its parameters and exploring different configurations to optimize its performance and generalization capabilities.

Operational optimization

Registering. We establish a process to register the trained data models, ensuring proper version control, documentation, and accessibility.

Deploying. Yalantis’ data science consulting team deploys the models in the production environment, integrating them with necessary infrastructure, APIs, or applications to enable real-time predictions and data-driven decision-making.

Monitoring. Our data scientists implement monitoring mechanisms to track the model’s performance, detecting anomalies and ensuring that the model continues to deliver accurate and reliable results over time.

Retraining. Within our data science services company, we have an established process for retraining schedules or triggers to periodically update models with new data, maintaining their effectiveness and adapting to evolving patterns or trends.

By following this comprehensive and iterative approach, our data science consultants ensure that data science projects are executed with precision, yielding accurate insights and delivering impactful results to our clients.

Data science methods and machine learning algorithms we use at our data science consulting company

Our team of highly skilled data science consultants combines their expertise in data science and technology to deliver exceptional solutions to our clients. Below are some of the common methods and techniques we employ.

Statistical methods as a value-added service

Regression analysis and hypothesis testing

Regression models allow our data science consultants to identify relationships and dependencies among variables, allowing us to predict and understand the impact of one or more independent variables on a dependent variable.

Hypothesis testing enables our data science consulting team to make inferences about population parameters based on sample data. It helps them determine the statistical significance of relationships or differences observed in data.

Analysis of Variance (ANOVA) and time series analysis

ANOVA is used to compare the means of multiple groups to determine if there are significant differences between them. It is often employed in experimental and survey data analysis.

Time series analysis focuses on analyzing and forecasting data points collected over time. Our data science services related to time series analysis involve techniques such as Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing to capture trends, seasonality, and other patterns in time-dependent data.

Machine learning algorithms we leverage in data science projects

Decision trees and random forests

Decision trees recursively split data based on feature values to create a tree-like structure for classification or regression tasks. They are easy to interpret and can handle both categorical and numerical data.

Random forests combine multiple decision trees and aggregate their predictions to improve accuracy and handle high-dimensional datasets. They mitigate overfitting and provide feature importance rankings.

Support vector machines (SVM) and neural networks

An SVM is a powerful algorithm for both classification and regression tasks in data science consulting projects. It constructs hyperplanes to separate classes or estimate the value of a continuous target variable.

Neural networks, especially deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), excel in tasks involving complex patterns, image recognition, natural language processing, and sequential data analysis.

K-Nearest Neighbors (KNN), Naive Bayes

KNN is a non-parametric algorithm that assigns class labels based on the majority vote of its nearest neighbors in the feature space. It is commonly used for classification tasks.

Naive Bayes is a probabilistic algorithm based on Bayes’ theorem. It assumes the independence of features and calculates the probability of an instance belonging to a particular class. It is often used in text classification and spam filtering.

Gradient boosting and XGboost (extreme gradient boosting)

Gradient boosting combines weak learners (such as decision trees) sequentially, where each subsequent model corrects the errors of the previous. This allows our data science consulting team to achieve high predictive accuracy and is commonly used in competitions like those conducted by Kaggle.

XGboost builds an ensemble of weak decision trees, similar to gradient boosting but with additional features such as weighted quantile sketching to improve speed and memory efficiency. It provides advanced hyperparameter tuning options, allowing practitioners to fine-tune model performance.

These are just a few examples of the statistical methods and machine learning algorithms we can use in data science consulting projects. The specific method or algorithm you select should depend on the issues you face, the nature of your data, and the desired outcomes. Our data scientists work in sync with a data science consultant to use a combination of techniques to extract valuable insights and build robust models for analyzing data and making predictions.