Data science consulting services
Collect data from all possible sources and turn it into actual value for your business.
Prepare for integrating AI and ML solutions into your business
Develop an AI and ML implementation roadmap based on your business needs
Validate ideas and hypotheses using existing data and models
Ensure smooth AI/ML solution adoption and integration within your organization
Structure data in a data lake, data warehouse, or data mart
Get on-demand support for your data architecture and software
Data mining and text analytics
Extract valuable insights, patterns, and correlations from structured and unstructured data sources such as corporate software systems, text documents, and customer reviews.
Derive value from statistical analysis, data mining techniques, and ML algorithms to build predictive data models and enhance business decision-making.
Data visualization and exploration
Effectively explore and analyze your data with visually appealing, informative, and interactive data visualizations and dashboards.
Advanced analytics (AI and ML)
Solve complex business issues and gain insights into your workflows with advanced analytical methods such as natural language processing, deep learning, and AI and ML algorithms.
Data science use cases we cover at Yalantis
Data science center of excellence at your service
Improve business efficiency, generate new value, and obtain a competitive advantage with a dedicated team of data science consultants.
Data science tools and technologies at Yalantis
Apache Spark MLlib
Amazon Machine Learning
Azure Machine Learning
Azure Synapse Analytics
Need a technology that isn’t listed above?
Each data science project requires unique solutions, and our well-versed data engineers can help you with tasks of any complexity.
Some of our data and analytics works
Data lake for a manufacturing company
Storage of structured and unstructured data
Data grouped into catalogs for easier search
Simplification of supply chain data analysis
System for supply chain big data analytics
Predictive stock planning
Load and route planning
Strengthened decision-making that boosted CLV
Unified access to data from lots of sources
Platform for real-time election data processing
Anti-fraud mechanisms and a tiered access system
Interactive electoral district maps
Automated ballot chart generation
Optimize your business processes with AI and ML algorithms
Learn seasonal demand for your goods, determine financial risks, and offer more personalized patient treatment with advanced data science mechanisms.
Data science Insights
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BI and advanced analytics solutions for supply chain data analysis
Read a guide on BI and advanced analytics solutions for the supply chain. Learn how to implement each one and read about common tools and techniques for both.
Predicting house price using regression algorithm for machine learning
We have prepared a step-by-step guide of using regression algorithms in machine learning, to help you predict house prices for a given area.
Who is included on a data science team at your company, and does the team include a data science consultant?
At Yalantis, we have a proficient data science services department with the following team roles:
- Data engineers are responsible for facilitating efficient data storage and management. They also build data processing pipelines and help data analysts interpret data trends and patterns.
- Data analysts focus on examining data to extract valuable information from it and support business decision-making. Data analysts build reports, data visualizations, and dashboards to present collected data in an understandable way.
- ML engineers apply machine learning techniques and algorithms to build and deploy data models that are trained on large datasets and are capable of generating additional business insights. In cooperation with software developers, a data ML engineer can also design and develop ML systems.
- Data science consultants help companies drive business growth by developing a data strategy, defining the value and quality of their data, and identifying relevant data sources.
What value can your data science consultants offer my business?
Unlike other data science consulting firms, we have data science experts dedicated to various industries to help you convert siloed data into tangible monetary value. With a domain-specific data strategy, you can ensure transparent cross-department data flows and establish a more productive workflow.
Our data science services team can also help you implement customized AI and ML systems so you can deliver personalized customer services, detect business and operational issues early on, and reduce human error.
Yalantis’ data scientists constantly increase their knowledge and can offer an up-to-date solution for your business that won’t require costly re-engineering in the future.
How do you ensure secure and reliable data science consulting services?
We understand the importance of adhering to legal and regulatory requirements, and our data science consultants are well-versed in data privacy laws, industry regulations, and compliance standards. Our team makes sure that all data processing and analysis activities align with legal guidelines, safeguarding the confidentiality and integrity of our clients’ data.
Ethical considerations and transparency are also critical for us. Our data scientists prioritize informed consent, data anonymization when necessary, and clear communication regarding data usage and outcomes. On top of that, we implement encryption, access controls, and other industry-standard security practices to safeguard data throughout our consultancy.
How does a Yalantis data scientist-consultant determine whether data quality is sufficient for analysis?
First of all, our data science consulting team evaluates the accuracy and completeness of data. We determine whether data has errors and if it contains all attributes necessary for analysis. To do this, we perform data profiling and data integrity checks. Our next step is to determine data consistency by looking for uniformity in data formats and data values across different data sources.
We also aim at discovering how relevant your business data is for the purposes of your current project and for meeting your business objectives. Our team regularly assesses data quality throughout the entire data analytics process to derive accurate insights from your business data.
How Yalantis data science consulting services help businesses gain valuable insights and make data-driven decisions
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:
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.
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.
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.
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.