Data Analytics

Data Analysis

Develop data pipelines, Machine Learning, and Artificial Intelligence models, visualize and integrate data, and analyze them – together with Amazinum.

Data Analytics

What is Data Analysis

The global big data analytics market is worth $307.52 billion


91.9% of organizations achieved measurable value from data and analytics investments in 2023.


56% of data leaders plan on increasing their budgets this year.


There could be up to 1.4 million new jobs created in data science and data analytics between 2023 and 2027.

Types of Data Analytics

Data Analytics Types

We are Experienced in Such Expertise

Descriptive Analysis

Descriptive analytics looks at what happened in the past. Today, this method of data analysis is the most common for businesses. It answers the question “what happened” or “what is happening” and thus summarizes historical data.

Descriptive analytics uses the techniques of computing descriptive statistics, data visualization, data aggregation, and data mining.

Types of Descriptive Analysis

Measures of Frequency

Measures Central Tendency

Measures of Dispersion

Measures of position

Contindency tables

Scatter plots

Descriptive Statistics

Summary statistics allow you to calculate average values and measures of variation such as range and standard deviation. It is a method of summarizing and describing basic data sets such as central tendency, variability, and distribution. Using this method, you can see an overview of the data, and based on it, determine patterns and relationships.

Descriptive statistics may include graphical methods, histograms, charts, and scatter plots for visual data representation.

Why do you need descriptive statistics?

It will give you the ability to effectively summarize and describe the main characteristics of a data set. Thanks to this, you can determine connections and regularities in them.

It can help identify outliers, provide information on choosing appropriate statistical methods for further analysis, or summarize other characteristics.

It will help you to understand the central tendency, variance, and distribution shape of a data set.

Descriptive statistics are widely used in various industrial domains such as business, economics, social sciences, and health care.

Data Visualization

Data visualization allows you to graphically represent information or data. Through the use of visual elements such as charts, graphs, and maps, this technology helps you see and understand trends, patterns, deviations, or outliers in your data. In addition, data visualization helps to analyze a large volume of information while presenting it in a convenient and accessible form.

Why Do You Need Data Visualization?

Data visualization technologies will help to streamline any large information.

Thanks to its accessible and understandable form, you can quickly make decisions and react to circumstances. Do not forget that timely actions will save your business from unnecessary costs and loss of authority.

You can quickly and efficiently identify differences in trends or indicators that are vital to the business. It is important to understand what causes costs and what brings profits.

Get information in real-time and improve the management of your business. Data visualization helps you improve operational efficiency and increase productivity.

Data visualization helps you explore business insights and achieve your goals. Analyze critical metrics and correlate data from visuals or graphs.

Save time and resources on customizing reports or predicting trends by leveraging data visualization and extracting data with custom-built reports.

Choose the Right Visualization to Tell Your Story


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Data Mining

Data mining includes the search and analysis of a large part of raw data in order to identify patterns and extract useful information. It is based on data collection, further storage, and processing.

Intelligent data analysis will allow you to develop effective marketing strategies, increase sales, and reduce costs.

Data Mining

Data Sets







Why Do You Need Data Mining?

Data mining includes the search and analysis of a large part of raw data in order to identify patterns and extract useful information. It is based on data collection, further storage, and processing.

Intelligent data analysis will allow you to develop effective marketing strategies, increase sales, and reduce costs.

In sales and marketing:

Inclination to more efficient and smarter use of capital in order to increase profit.

Optimizing marketing campaigns based on demographic indicators of consumers and their behavior on the Internet.

Improving segmentation, cross-selling offers and customer loyalty programs, advertising offers, and thus – increasing investment in your business, or increasing profits.


By looking for patterns, trends, and correlations that connect the data, we help you identify outliers or correlations that shouldn’t exist.

Detection of anomalies in data that resemble fraud.

Eliminate fake accounts.


Gathering data to understand the environment for students that supports their learning and success

Monitoring and evaluation of student performance.

Tracking of time spent and other indicators and online educational platforms.


Analysis of costs for raw materials and materials

Analysis of the use of resources and materials

Information about the company’s strengths and weaknesses

Customer service:

Collection of information about the client’s interaction with your service.

Analysis of the weak and strong points of your service.

All this is just a small part of what the potential of Data Mining can open for you. Contact us and Amazinum can provide you with a solution that will improve your business.

Data Aggregation

Data aggregation is widely used in various industries. It helps to collect important information on the basis of which decisions will be made.

Data Aggregation

Data agregation

Data Aggregation

Data aggregation is becoming a must-have in digital marketing. It will help you understand user behavior, bounce rate, time spent, and a bunch of other metrics that influence your ad campaign and are responsible for its success.


Based on data aggregation, medical experts can suggest and develop more effective treatments and gather information about different cases and abnormalities.

Electronic commerce

With Amazinum Data Aggregation, you can analyze peak and off-peak traffic, visitor demographics, number of transactions, and whether customers made purchases based on recommendations. Collect data about competitors, their sales volume, and prices to make your services unique and customer-oriented.

Data aggregation can unlock the potential of your business or service. And Amazinum сan help you with that.

Diagnostic Analysis

Diagnostic analysis works on the basis of information obtained from descriptive analytics while studying in detail the reasons for these results. It answers the question “Why did it happen?” This method of Data Analysis is used by analysts because it creates more connections between data and can identify possible behaviors.

The main purpose of diagnostic analytics is to track anomalies in data and respond to them. In addition to identifying and solving problems, diagnostic analytics are used to analyze the causes of positive results.

The important steps of diagnostic analytics are:

  • Formation of hypotheses about the causes of a certain phenomenon;
  • Obtaining data on the causes;
  • Carrying out descriptive analytics on subsets of data;
  • Use of statistical models to study the relationship between causes and consequences.

Amaiznum can offer you the methods that best suit your business needs – probability theory, regression analysis, filtering and time series analysis.

Why Do You Need Diagnostic Analytics?

Diagnostic analytics is useful for any industry, as it can shed light on developing predictive models or creating successful business solutions.


Establish cause-and-effect relationships and determine the popularity of certain products. This will give you the opportunity to manage resources wisely, attract investments, and better understand your customers.


Optimize medical documentation thanks to diagnostic analytics – calculate statistics and important information about patients, identify events or hospital resources, or even simulate the speed of disease diagnosis. By comparing this data to historical trends, you can easily build cause-and-effect relationships and identify anomalies.


Diagnose hardware with diagnostic analytics, and calculate software updates or potential failures and their causes. This will help you avoid unnecessary costs or dangerous incidents in the workplace.

Human resources:

Diagnostic analytics have the potential to tell you about the working conditions of your employees, review pay scales, or take other steps to attract or retain employees.

Predictive Analysis

Predictive analytics is aimed at predicting future trends. She answers the question “What can happen in the future?”

Predictive analytics is based on historical data with statistical modeling, data analysis methods and Machine Learning. For the business side of the business, such a decision will be useful first of all for finding patterns in the data in order to identify risks and opportunities. However, it is important to understand that forecasts depend on the quality and details of the data.

Predictive models can use relationships between sets of variables to make predictions. For example, the correlation between seasonality and sales figures can predict your sales. In addition to forecasting, predictive analytics is used in classification. The main element of classification is logistic regression, which works on the basis of classifying an element to target groups based on its characteristics.

Predictive analytics is very useful for business and can take strategy, sales, or customer communication to a new level. Business applications of predictive analytics include:

  • Sales forecasting;
  • Risk assessment;
  • Customer segmentation;
  • Predictive analytics in customer success teams.

Predictive Analysis

Statistical Model

Consumer Behavior

Machine Learning

Predictive Behavior


Decision Making




Why Do You Need Predictive Analysis?

The Amazinum team will help you implement predictive analytics for your business. Our specialists know how to work with different domains and understand how Machine Learning and Data Analysis technologies will help you take your business or service to a new level.


Predictive analytics and Machine Learning are used to predict credit risk and detect fraud. It will help you maintain dynamic changes in the market in real-time. Amazinum will help you set up personal listening for customers and minimize risks. In addition, predictive analytics is widely used in credit scoring. With the help of data analysis technology, the bank receives a prediction about the performance of the granted loan based on the credit history of the applicant and the credit history of borrowers with similar characteristics.


Prognostic analytics can be used to treat and identify chronically ill patients, as well as track specific infections. In addition, predictive analytics helps analyze medical records and predict possible diseases based on them.

Human resources (HR):

HR departments are perfecting various processes, such as forecasting future personnel needs, and requirements for a specific employee, or analyzing employee data to identify factors that contribute to high employee turnover. Predictive analytics help analyze an employee’s performance, learning, moods, and preferences to improve performance, meet professional needs, and plan career development. Such an analysis will help companies reduce staffing costs, satisfy their employees, and manage their resources.

Marketing and sales:

Marketing departments often come into contact with business intelligence, because it helps them understand the performance of sales in past periods, understand whether the interaction with their customers is correct, and investigate their activity. For marketers, predictive data analysis is useful for forming and analyzing cross-selling strategies and other necessary information to understand whether the right business decisions have been made. Moving averages, ranges, and control points are based on historical data and are used to predict future prices.

Supply chain:

Managing inventory and required supplies becomes much easier with predictive analytics. It helps companies assess the cost and profitability of their products in the long term. In this case, predictive analytics uses historical data and statistical models to predict future supply chain performance, demand, and potential disruptions. This will help companies identify and eliminate risks in advance, optimize processes and resources, and make successful and effective decisions.

Prescriptive Analytics

Prescriptive analytics considers all possible factors, scenarios, and predictions and suggests conclusions based on them. It answers the question “What should we do next?”

Recommended analytics is the most demanded and at the same time the most complex type of analysis. This is an advanced level that combines the information of all previous analyses to precisely guide the course of action to solve a problem or find a specific solution. It includes statistical methods and computational modeling procedures. Algorithms use “if” and “else” statements as data analysis rules.

Artificial intelligence is often used in the prescriptive method to analyze large amounts of data and provide the right recommendations. AI systems absorb vast amounts of data, constantly learn, and use their knowledge to make informed decisions. Along with mathematical equations, AI is the main component in learning algorithms. Artificial intelligence makes it possible to optimize business processes without human intervention.

Forward-looking analytics show how best to use the predicted results, such as “What are the next steps to avoid problems and make the right decisions?” or “What should be the actions to profit from the new trend?”

Such combinations provide the ability to see what decisions and how can affect the future, based on all possible scenarios and potential outcomes. This will allow the business to decide on a strategy, “route” and further action plan. That is why prescriptive analytics plays a big role in business and is so in demand.

Companies that already use Data Analytics


Data analysis is used by Microsoft for business intelligence, customer support, and product development. One well-liked tool for data visualization and analysis is Microsoft’s Power BI.


Twitter uses data analysis to optimize content, increase user engagement, and measure the effectiveness of advertising. To improve its services, the platform examines user interactions and tweets.


Tesla uses data analysis to develop autonomous driving, enhance vehicle performance, and perform predictive maintenance. The business gathers and examines data from its automobile fleet.


Walmart employs data analysis for customer insights, supply chain optimization, and inventory management. The business uses data to increase the effectiveness of its operations.


NASA uses data analysis for satellite missions, climate research, and space exploration. Interpreting the enormous amounts of data gathered from space missions requires data analysis.


PayPal uses data analysis to track transactions, identify fraud, and examine consumer behavior. The business uses data to make sure that financial transactions are safe and easy.

General Electric (GE)

GE uses data analysis for energy management, manufacturing process optimization, and predictive maintenance of industrial equipment. 
Data is used by the business to improve the performance of its wide range of products.


Adobe tracks user engagement, optimizes products, and uses data analysis for marketing analytics. Businesses frequently use Adobe Analytics to examine consumer behavior and the efficacy of digital marketing.


HubSpot uses data analysis to score leads, automate marketing campaigns, and manage customer relationships. The platform aids companies in the analysis and improvement of their marketing initiatives.


Fitbit is a company in the fitness tracking sector that tracks activity and health and uses data analysis to provide user behavior insights, personalized recommendations, and health information. Data is used to create customized exercise regimens.


Coca-Cola uses data analysis to forecast demand, optimize its supply chain, and improve the efficacy of its marketing campaigns. To inform its product strategies, the company examines market trends and consumer preferences.


Ford uses data analysis for predictive maintenance, vehicle diagnostics, and performance enhancement. Data is being used by the automotive industry more and more to increase vehicle efficiency and safety.


Amazinum Portfolio

Check out a few of our recent projects
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E-commerce monitoring & intelligence platform drives 2x growth helping Amazon businesses on various markets

Our Industry Focus

Our industry knowledge and background give our clients and partners confidence that we understand their business. Here we highlighted a few top industries we are good at, penetrating to the smallest details and nuances of a certain branch.

SEO & Advertising icon

SEO & Marketing


Safety & Security

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E-commerce & Retail icon

E-commerce & Retail

Gambling and Casino icon

Gambling and Casino

Manufacture icon


Ecology icon



Energy icon


Localisation icon


IoT icon


Partners & Clients

We deeply appreciate our partners for cooperation. Every member of the Amazinum Team does their best to provide the highest quality of services and solutions. We satisfy all your needs and requirements.

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sleepnumber logo
softserve logo
aid genomics logo
Vimmi logo
PVH logo
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AstonUniversity logo
YellowMedia logo
byteant logo
sleepnumber logo
softserve logo
aid genomics logo
Vimmi logo
PVH logo
sloboda studio logo
AstonUniversity logo
YellowMedia logo


Learn about technology stack we use to implement data science:

Programming languages:

Python logo

for data analysis and processing:

OpenCV logo
NumPy logo
SciPy logo
Pandas logo

for creating solutions:

spaCy logo
Spark logo
PyTorch logo
TensorFlow logo
Keras logo
Scikit-Learn logo

for API development:

RabbitMQ logo
Flask logo
FastAPI logo
Django logo

for visualization:

ploty logo
matplotlib logo

Deploying solutions:

Docker logo
GitLab logo
TensorFlow logo
DVC logo logo
KubeFlow logo



PostgreSQL logo
ClickHouse logo
BigQuery logo
MySQL logo


Bigtable logo
Elastic logo
Cassandra logo
mongo DB logo

Cloud Solutions:

Google Cloud logo
AWS logo

Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

Vitaliy Fedorovych contact us photo

Hello there!

Amazinum Team assists you through all data science development processes:
from data collection to valuable insights generation.
Get in touch with our CEO and Data Scientist to figure out the next move together

Contact Us

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Bazarnitska Alina

Client Partner

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Ihor Khreptyk

Client Partner

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Stopnyk Zoriana

Client Partner

Vitaliy Fedorovych contact us photo

Vitaliy Fedorovych

CEO, Data Scientist

Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

Vitaliy Fedorovych contact us photo

Hello there!

Amazinum Team assists you through all data science development processes:
from data collection to valuable insights generation.
Get in touch with our CEO and Data Scientist to figure out the next move together

Contact Us

Click or drag a file to this area to upload.

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