Data Science

What Is Data Science?

Data science is a combination of math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning. It is used to reveal the insights hidden in an organization’s data.

Insights that Data Science brings can be used by decision makers as a guide for strategic planning, and thus help them realize their business’ full potential.

Data Science as a Service

Data Science services provide invaluable insights for making business decisions by using statistical methods and machine learning algorithms to further analyze data and create predictive models.

The main goal of the Data Science team is to use those insights to help clients answer some of the burning questions like:

What direction do they want to develop their business?
What is their primary goal?
What are the limitations to achieving that goal?

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Data Science Roles

The core roles of our Data Science team are Data Scientist, Data Engineer, and Data Analyst. These three roles combine technical expertise and domain knowledge with solid understanding of clients' business goals to provide data solutions that will help them succeed.

Wondering what are the key differences between these roles? Read more below:

Data Scientists

To avoid making important business decisions based on "gut feeling", there are Data Scientists. Data scientists use statistical methods, machine learning algorithms and other tools to analyze data and create predictive models. They are the core members of a team who make decisions based on statistical conclusions obtained from historical data.

Data Engineers

Data engineers are in charge of developing, testing and maintaining data pipelines.

Data Analysts

Data Analysts analyze and interpret numeric data, and they build dashboards to help companies make better, data-driven decisions.

Data Science Areas in which we are experts

Big Data
Forming Data Warehouses & Data Lakes
ETL processes
Computer Vision
Deep Learning
Machine Learning
Recommender Systems
Linear Programming
Reinforcement Learning
Time Series Analysis
A/B Testing
Analysis of Graphs and Graph Neural Networks
NLP
Data Visualisations and Dashboard

Why Choose Vega IT for Data Engineering, Data Science and Data Analytics?

When you combine our relentless passion with domain and technical expertise we have been gathering since 2008, you get a team that can utilize previous knowledge to better understand your vision, customers, specific industries' requirements, and the challenges you are facing.

We work at the cutting-edge of digital product development - helping clients turn their vision into reality. Our flexible service model is designed to fit your needs, whether you need a couple of engineers to expand your capacity, or a whole product team to ship the next big thing.

If you need a team of passionate experts to bring your vision to life, you can rely on us to deliver.

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Data Science Technologies we use:

Python
PySpark
Data Scraping Stack: Scrapy, Selenium, BeautifulSoup
Data Analysis Stack: Pandas, Numpy, Matplotlib, Seaborn, NetworkX
Machine Learning Stack: Scikit-Learn, XGBoost, FBProphet, CatBoost
Deep Learning Stack: PyTorch, Keras, Tensorflow
NLP Stack: NLTK, HuggingFace, Stanford CoreNLP, Gensim
Data Analytics Stack: PowerBI, Tableau
Database Stack: SQL, MongoDB, FastAPI, Flask, Docker, OpenCV
Azure Stack: DataBricks, Azure Functions, Azure AI services (e.g. LUIS, Speech to Text), Data Factory, Data Lake, Delta Lake, Cosmos DB, Synapse, Data Explorer, ML Studio
AWS Stack: Sagemaker, Glue, Kinesis, S3, EC2, EMR, AI Services (e.g. Translate, Text to Speech, Rekognition), Redshift, RDS, Athena, Quicksight, Lambda, DocumentDB, Neptune, Elasticsearch, Kibana, MLFlow, Grafana

Data Engineering Technologies we use:

Python
Golang
Java
SQL
AWS Stack: Redshift, Dynamo, RDS, ECS, EC2, Lambda, SimpleQueueService, CodePipeline...
Azure: SQL Database, Cosmos DB, Data Lake, Data Factory, Functions, Databricks...
Apache tools: Airflow, Spark, Kafka, Hadoop...
Terraform
Snowflake
Elasticsearch

Unsere Stärke in Zahlen

16 +

Jahre auf dem Markt

900 +

Experten

1500 +

durchgeführte Projekte

200 +

Kunden, die uns vertrauen

Case Study: Argus Data Insights

The client is a company that processes data collected from the media and finds insights within that data. Our goal was to use Natural Language Processing (NLP) backend services to improve solution performance and accuracy. Find out more.

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What wir tun

Lerne uns kennen

Frontend-Entwicklung

Which business model suits you?

Different budgets, deadlines, challenges, and requirements. There is no one-size-fits-all approach to software development. To match your exact goals and ambitions, we offer two types of business models:

Time & material: Greater control. Flexibility. Participation in candidate selection. With no rigid processes or end dates, this business model is easier to scale up or down as your business needs change.
Fixed price: Fixed scope. Fixed budget. Fixed timeline. Those are the main benefits of the fixed price model. You set the requirements upfront, and we deliver the project within them.

Many clients choose to start with the fixed-price model. However, as their project scope evolves, they typically shift to the time & material model.

We’re here to find fast, elegant solutions to your trickiest problems.

Sasa gründete Vega IT vor 16 Jahren gemeinsam mit seinem ehemaligen Studienkollegen Vladan. Ihr Traum, ein IT-Unternehmen zu gründen, hat sich zu einem der führenden Softwareunternehmen mit mehr als 900 Ingenieuren entwickelt. Wenn Sie es vorziehen, eine E-Mail zu senden, können Sie sich gerne an hello.sasa@vegaitglobal.com wenden.