Data Science Consulting and Services

Gain a competitive advantage with data science consulting

Discover technologically advanced solutions suitable for your business. Invest in data science to turn your data into insights. Use the acquired information to make effective decisions and increase profits.

What is Data Science?

Data science is, as the name suggests, a study of data. It requires knowledge of different algorithms and high-tech tools.

Data science can be described as an interdisciplinary, non-academic field dealing with the process of obtaining, processing, visualizing and inference based on structured and unstructured data, using statistical methods, data mining, machine learning and predictive analysis.

The primary goal of data science processes is to extract valuable insights from structured and unstructured data.

Data-driven companies are 58% more likely to achieve their revenue goals

Why do you need data science consulting?

  • Data science consulting services help companies automatically extract knowledge and insights from various sources of big data.
  • Regardless of the industry or sector, your company operates in, in order to be able to make accurate and effective business decisions, you must base them on reliable observations and predictions.
  • The data science consulting team of experts will help you better understand your customers, personalize their service, automate and improve business processes using advanced data-based technologies.
  • Most companies record growing revenues and cost reductions thanks to the successful implementations of data science solutions.

Who is a data scientist?

Data scientist – a person dealing with data science, in particular big data analysis.

Data scientists s are people with very broad competencies who combine business knowledge with mathematics, statistics and programming.

Their main task is to translate the collected data into real business value. It is extremely important to know statistics and to be fluent in mathematical issues.

Companies lose 20% of income due to data quality problems

How we can help you

Improve customer experience


Data science consulting services will enable your company to:

  • Identify current trends
  • Build customer loyalty
  • Prevent errors before they occur
  • Offer personalized experience on e-commerce

Fraud detection


Fraud detection is one of the main areas of interest of any organization operating in the financial sector. Data science with AI technology offers an effective solution to reduce fraud.

Real-time predictive analytics – this data analytics application enables companies to identify potential fraud transactions and block them when unusual financial activity is detected. Thanks to this solution, companies can prevent numerous financial losses.

Risk management


Risk management is one of the key factors in finance. The potential risk is closely related to changes in policy, customer behavior, market trends and competition strategies.

Thanks to data science applications, financial professionals can more accurately assess the creditworthiness of potential customers by analyzing their data taking into consideration many aspects.

Identify opportunities for improvement


Thanks to data science solutions, proper tools, a technology stack, and an experienced team, your company will gain a complete understanding of what is happening in the company at the moment, as well as accurate predictions of what may happen in the near future.

With these insights, companies can introduce new product developments, implement innovative solutions and find new business development opportunities before other competitors could realize that.

Operations-related data science


The goal of operations-related data science and analysis is to use the tools and data provided by other data analysts, the data warehouse team, and the operational database to identify new opportunities for business improvement and optimization of operations.

The most popular areas of interest are logistics, production, supply chain, finance and human resources.

Cognitive computing development


The main purpose of this solution is obtaining all input data needed to power the model, creating data pipelines, accessing convenient data sources, conducting A/B tests, testing infrastructure.

Once these steps are successfully completed, the focus shifts to building actual algorithms or models. Knowledge of industry-standard tools and statistical techniques is essential.

Cloud & distributed computing


The purpose of implementing cloud and distributed computing is to have the required and distributed infrastructure for data analysis and data science. It allows companies to easily scale big data applications.

Additionally, cloud technologies allow for safe system integration with current applications and microservices architecture.

Data mining & statistical analysis


Data mining and statistical analysis are based on the use of statistics in the form of exploratory data analysis and predictive models. Their goal is to identify trends and patterns in data using existing data sources.

It enables entrepreneurs to take a business problem and turn it into a question for data, create predictive models to answer this question and obtain in-depth information about the results.

Let’s discuss a data science solution for your business

Which industries can benefit from data science consulting & services?

Financial professionals often struggle with huge amounts of data coming from various sources and forms: structured and unstructured data.

By applying data science techniques such as language processing, data mining, text analysis, and more, financial professionals can extract relevant information from raw data and use it to their advantage to make more effective decisions that generate more profit.

Does your company operate in the financial sector? Discover more data science consulting applications for your business.

Drug discovery and subsequent development is a time-consuming process that often costs pharmaceutical companies a fortune. Data science offers a revolutionary solution that significantly reduces the time spent on these processes.

Specially designed data science algorithms can predict the effectiveness of a new drug with high accuracy. Additionally, the drug manufacturing process can be shortened through real-time data analysis.

To learn more, read our article 7 Ways Data Science Is Revolutionizing Healthcare.

The importance of data in today’s world has reached new heights so much so that companies are making business decisions only after a thorough analysis of relevant data.

This has especially found an important place in the e-commerce and retail industry. They can predict the purchases, profits, losses and even nudge customers into buying additional products on the basis of their behavior.

Organizations also use purchase data to create psychological portraits of a customer to market products to them and use it to drive customer loyalty and thereby more revenues.

To learn more, read about our case studies for e-commerce and retail.

Generally speaking, data science in marketing analytics has two major goals:

  • To sell more (primarily by improving their conversion rates and by gaining more visibility in the various online marketing channels, especially Facebook Ads and Google Ads)
  • To lower marketing expenditure (by optimizing not just the campaign itself but also the cost-per-click rates).

Discover data science use cases in marketing.

Every activity of a player leads to predictions of future behavior. It is data which after processing can be of great value for a company. Data-driven algorithms can analyze huge amounts of data (structured and unstructured) and drive conclusions out of it.

We can analyze the current and past activity of a particular player and automatically detect if the player’s behavior has changed over time.

As a result, we can track an odd behavior of a particular player faster, and analyze whether the account has not been hacked.

Interested in mobile gaming in particular? Read our case study – data analytics in mobile gaming.

Data Science Project Flow

1) Data science consulting meeting

The first stage is a meeting with our data science team. We define your product needs, business challenges, we set your goals and project expectations. We also identify your data sources, their quality and how we can use them in a future data science solution.

2) Data identification and preparation

After obtaining the data, our data scientists check your current data infrastructure. The process also includes cleaning up data, removing what is no longer needed, restoring missing ones, and standardizing the format of all collected data. We prepare your data for implementation in the future model.

3) Data modeling

At this stage, our data scientists train multiple models to select the most appropriate one that contributes to the accurate future. The key goal is to find useful hidden patterns and gain insight in a short time, in order to support the decision-making process.

4) Design and implementation

We design, integrate, test your product and monitor the results. Then we visualize and present our results, in line with your business goals and expectations.

Is data science consulting right for your business?

Data science solutions are finally accessible to companies and enterprises of all sizes.

Today, any organization that wants to take advantage of the data it possesses, can now leverage data science solutions to extract hidden insights.

Data science consulting offers a unique opportunity to your team and business – a source of single, accurate, high-quality information.

Data science applications enable business leaders to make informed decisions based on forecasts, predictions and optimizations, so their competitive advantage increases.

Examples of data science applications in big companies


This startup was aware of the possibilities offered by data science from the very beginning.

Airbnb had data analysts on its initial team.

Perhaps this is one of the reasons why it has grown so quickly, leaving competitors far behind.

In five years, Airbnb has seen 43,000% growth.

How has data science affected this?

Data science applications are present in every decision-making process, making the chosen solution the right one.

How is Airbnb doing now?

“We are at a point where our infrastructure is stable, our tools are sophisticated, and our warehouse is clean and reliable. We are ready to take on exciting new problems.”

“Managers had signs outside their offices that said, ‘In God we trust. The rest, bring me data”

“It was common to have heated debates around data. It was like, I don’t trust your subjective opinion. Bring me a data-driven opinion.”

Guru Hariharan

Former engineer, Amazon


This giant has always been aware of the power of data science.

Amazon’s Alexa is one of their data-driven projects that analyze customer data to improve the platform and sales processes.

Amazon uses a data-driven dynamic pricing algorithm that changes the price of a specific item every 10 minutes to make sure they’re competitive.


Spotify is a world leader in music. It’s hard to find someone who has never tried it.

In addition to a wide selection of artists and intuitive software, Spotify offers a personalized playlist and suggestions that are surprisingly well suited to the user’s taste.

This is where data science plays a key role.

Spotify has an entire team of data scientists working towards an increasingly personalized user experience.

They categorize music by energy, volume, danceability, etc. so that the music you explore most likely suits your needs.

Data scientists use the data provided, and the longer you use the Spotify app, the better they will recognize your favorite type of music.

“Big data will spell the death of customer segmentation and force the marketer to understand each customer as an individual within 18 months or risk being left in the dust.”

– Virginia M. (Ginni) Rometty, chairman, president and CEO IBM.

Discover our case studies

Customer Lifetime Value and Churn Prediction in Loyalty & Ecommerce


We helped one of the global startups enrich the platform with self-learning Machine Learning components in order to automatically draw analytic conclusions from data and then to take up the best business actions.

See how we deployed the full-stack Data Science solution for insights extraction and data management.

Our client gained:

  • Churn prediction model
  • Customer lifetime value prediction model
  • Recommendation engine
  • Custom segmentation algorithm
  • Analytical dashboard

Embedded Advanced Analytics and Data Science Application


We helped an international software company attract new customers and increase market share.

We implemented Machine Learning and Business Intelligence modules, which help end-users to increase retention rates and improve operational efficiency with embedded BI.

Our solution included:

  • Customer 360 panel with machine learning models to predict user behavior.
  • ​Embedding of self-service BI into SaaS application
  • Data integration
  • Custom recommendation engines.

Discover the rest of our case studies from various industries. Find out how Addepto solutions can help your business.

Learn More About Data Science Consulting & Services

Our team uses advanced AI-driven algorithms, technology and tools to support enterprises in developing data science-driven solutions and products.

Our team has extensive knowledge and experience in designing, implementing, consulting, and integrating Data Science solutions within the customer’s IT infrastructure. Below you can find some useful information shared on our Blog by our experts.


Data helps you in almost everything you do. From searching on Google, through hiring new workers up to creating financial reports. It really is all about data.

Check our examples of data science applications!


Finance has been utilizing data over the years before finally conceptualizing and defining data science as a significant influence on finance.


This article will provide you with a better understanding of data science applications in healthcare.

If you are one of the health tech startups, this information will help you to enter the market successfully.

Data scientists are able to think of ways to use data to solve problems that otherwise would have been unsolved, or solved using only intuition.

Peter Skomoroch

Data Scientist, LinkedIn

The goal is to turn data into information and information into insight.

Carly Fiorina

Former CEO, Hewlett Packard


We provide companies with reliable Data Science consulting using open source and commercially trusted technologies.

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What is data science?

Data Scientist tries to find an answer/solution to a specific business problem by “extracting” information from the collected data and finding a certain relationship between them (if any).

As a result, an algorithm is designed that reflects these dependencies, and thanks to it we can, for example, predict customer interest in our products.

What does a data scientist do?

A data scientist works closely with business and data engineers. Its role is to comprehensively analyze the issue: from understanding it through preparing and processing data to building a model, visualizing it and creating a recommendation based on the analysis result.

A data scientist must therefore be perfectly at home in the field of statistics. Most machine learning algorithms are based on calculus, linear and nonlinear algebra.

A data scientist is expected to be able to prepare a good data visualization and to be a communicative person. This feature is important because the data scientist must collaborate with people, present their models and discoveries in the company, and encourage colleagues to use them.

What skills are needed to be a data scientist?

10 must-have skills of any professional data scientist:

  1. Statistics
  2. Programming
  3. Advance knowledge of Data Science
  4. Data Visualization
  5. Data Analysis
  6. Big Data
  7. Fundamentals of machine learning
  8. Deployment of models
  9. Good communication Skills
  10. Problem-solving thinking
What is the difference between data scientist and data engineer?

Software engineer builds software, maintains it, tests it, and improves it when needed. Software engineering, therefore, creates a base that every data scientist uses in their work. That’s why it’s entirely justified to say that there is no data science without software engineering.

Data scientists analyze, process, and model data they possess. Then, they interpret the results to create actionable plans and insights for companies and other organizations.

Data science is a multi-role field of expertise that entails an understanding of such disciplines as:

  • Computer science
  • Maths
  • Statistics
  • Business knowledge
  • Software development
  • Machine learning

The list of desirable skills one should possess is even longer, and it comprises i.a.:


  • Programming
  • Data visualization and reporting
  • Statistical analysis
  • Effective communication and soft skills
  • Data mining, storing, analyzing
  • Knowledge of the big data platforms
  • Familiar with the cloud computing tools
  • Knowledge of the data warehousing
What problems can data science solve?

In its essence, data science is all about five fundamental applications:

  1. Identifying trends and patterns in large datasets. Data science is the best and most effective way to analyze large datasets in order to draw useful conclusions from them.
  2. Identifying anomalies. Anomalies are frequent occurrences in various industries and sectors. Especially banking and e-commerce companies should be interested in identifying anomalies. For instance, if a bank detects some unusual activity on their client’s account, they can take necessary steps to prevent, for example, money laundering.
  3. Predicting the likelihood of future events. Predictive analytics can take into account many elements and datasets to present you with an accurate prediction about future events or courses of events.
  4. Showing how things are connected to one another.
  5. Categorizing individual data points. Classification is all about identifying to which of a set of categories a new observation belongs, on the basis of a training dataset containing observations (or instances) whose category membership is known. Classification is essential, for instance, in spam detection.

To sum up, data science helps you work more efficiently and, in many instances, quicker as well. Thanks to data science, your company can understand data and draw useful business-wise conclusions, which, in turn, leads to more accurate and informed decisions.

To learn more, read our article: Data science for business – what problems can be easily solved?

Is data science still valuable?


In fact, data science is one of the fastest-growing fields in recent years. According to the Bureau of Labor Statistics, data science is projected to grow 31% over the next ten years.

What is the future of data science?

The possible uses of data science in business include:

Decision making

As we have previously mentioned, the main purpose of data science is finding patterns and drawing conclusions from the organization’s data. Thanks to data science, answers to important business questions and solutions to problems occurring in the company can be easily found.


The popularity of ML models can be justified by the fact that data scientists adjust them according to the current needs of the company. That is why the majority of organizations create models which process huge amounts of data in real-time and are able to immediately find recurring patterns and detect possible anomalies or errors which would normally go unnoticed by a human eye.

It is especially useful for security purposes. In case of behaviors on users’ accounts that seem to alert, the system can notify the administrator who is able to take immediate action and check whether a danger was real.


Another area that can benefit from data science is sales. Since by analyzing data, you can predict future events, trends, and behavior, it is also possible to forecast sales in upcoming months. Thanks to data science, sellers may avoid overproduction or run out of the most popular supplies.

Naturally, the way in which a company will benefit from data science is dependent on the industry and individual needs of the company. For example, financial institutions are usually most interested in assessing risks of investments and ensuring security, while e-commerce businesses will be focused on controlling stock and marketing purposes.

Read more in our article: Data science vs. Computer science

Why do companies need data science consulting?

Companies need data science consulting to get the most out of their data.

It helps them:

  • Make a deep market analysis,
  • Compare their position with the competition,
  • Make the right business decisions based on accurate information,
  • Improve the quality of customer service,
  • Save money on operating costs,
  • Streamline various processes in the company.

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