Data Science Academy

Courses from 14 Eur/h.

Courses for retraining of specialists from different areas and training  new advanced data management specialists.

Full-time, evening and individual courses 3-4 times a week.
The standard time for full-time courses is from 9 a.m. to 4 p.m.
The standard time for evening courses is from 6 p.m. to 9 p.m.
UZT founded full-time courses is from 9 a.m. to 4 p.m.
UZT founded evening courses is from 7:30 p.m. to 9:30 p.m.

The courses take place in Vilnius, Lithuania, at a distance or in mixed groups.

Register

Data Analysis Course

75 h.

The course consists of the following modules as standard:‌
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‌Data queries using SQL language.

Data editing and visualization using Microsoft Power BI.

Advanced data analysis using the Python programming language.

Data Engineering Course

75 h.

The course consists of the following modules as standard:‌
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Developing business applications without code using Power Apps.

Code-free business automation with Power Automate.

Data editing and visualization using Microsoft Power BI.

Creating code-free virtual chat bots using the Power Virtual Agent.

Computer workflow automation using PowerShell.

The courses can also be completed according to individual needs by assembling the desired modules or ordered separately as individual modules. When studying less than 3 modules, a higher hourly rate applies.


Testing before starting courses

In order to increase the efficiency of the courses, the student groups are formed according to the different level of intensity desired by the students.

Each student can take self-assessment tests before the course to determine the initial level of knowledge and skills.

Depending on the test results, students can choose different preferred lecture intensities.

Training programs are adapted to lectures of different intensities.

Test for self-assessment‌

Low intensity lectures

Lectures are conducted according to the slowest information absorbing students.
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‌Advantages:
‌Lectures are given at a slower pace, more attention is paid to the more detailed explanation of topics with more examples. More attention is paid to the practical tasks during the lecture.
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‌Disadvantages:
‌The slower pace (depending on the slowest absorbing students) reduces the amount of information provided. Significantly less focus on advanced features and methods.

Medium intensity lectures

Lectures are conducted according to the average level of information absorption.
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‌Advantages:
Much attention is paid to the practical application of skills and the sharing of experiences. Additional and advanced topics are selected flexibly. More attention is being paid to the debate. The aim is to pass all the intended material.
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‌Disadvantages:
A significant part of the practical tasks is transferred to independent work. Fluctuations in the pace of lectures are possible. Lectures are more demanding for greater involvement and activity.

High intensity lectures

Lectures are based on the students who are best in absorbing the information.
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‌Advantages:
‌Lectures focus on unique solutions and provide a lot of additional related information. Considerable attention is paid to the mathematical basis of analytics. The lectures cover in detail modules such as machine learning or an introduction to artificial intelligence.
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‌Disadvantages:
‌Most of the practical tasks are transferred to independent work. Self-directed learning during lectures has a major impact on the final level of knowledge.


Data science training

Data science training is conducted in accordance with pre-prepared, approved and standardized training material.

All lectures are recorded and the recordings are given to the students after each lecture as well as all the material and slides used during the lectures.

Each lecture module has one or more tests that are assessed by the lecturer. When studying a full course, the final assessment of the training consists of the assessment of the individual modules and the final project.

The final assessment of the course is then used for the certification level and for further Datacademy commitments.‌

Data Analysis Course

This course is balanced for novice data analysts, but the material is full of advanced data analytics information, challenges, and examples. Here you will not only gain a general understanding of what data analysis is, but you will also see real data analysis solutions that use the tools from this course. Get to know and see live the requirements of employers and the real-world tasks facing data analysts in job selection. For those looking for an answer if data science is for You - this is the place!

DASQL

In SQL, this is the beginning of the data analysis journey. With this language, we will extract data from servers and automate data exchange.
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‌During the course we will examine in detail the following and related topics:
SQL Servers, Database Structures, Table Relationship, Table Keys, SQL Queries, Data Filtering, Data Aggregations, Data Grouping, Subqueries, Table Joining, SQL Functions, Data Types, Data Base & Table Creation & Deletion.

DAPBI

Power BI is an indispensable tool for interactive data visualization. In this tool, we will implement both the acquired SQL knowledge and Python programming, combining everything into a single and complex analysis.
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‌During the course we will examine in detail the following and related topics:‌
Power BI Desktop, Power BI Service, Power BI Gateway, Different Data Sources, ODBC Connector, Connection to Server Data, Query Editor, KPI Dashboards, AI Analytics, Forecasting, Tooltips, What-if parameter, Table Relationship, Bookmarks, Drill Actions, DAX, Power Query, SQL Query, Python integration,  Data from WEB, Custom Theme, Templates.

DAPTN

Python is the best that modern programming has to offer data scientists. With the help of this tool, we will not only learn to do what you already know with the help of Excel and similar tools, but we will also touch the wonderful world of artificial intelligence.
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‌During the course we will examine in detail the following and related topics:
‌Basics of Programming Languages, Code Editors, Python Syntax, Basic Python Functions, Python Data Types, Variables, Operators, Basic Mathematics in Python, Data Sets, Methods, Indexing, Slicing, Casting, Unpacking, Loops, List Conprehension, Functions, Lambda, Classes, Built-in Modules, Third party Modules, Advanced Data Analytics with Pandas Module, data exchange with SQL servers by combining SQL code with Python code, Data Visualization with Pandas, Matplotlib and Seaborn Modules, Machine Learning With SKLearn Module.

Data Engineering Course

Big data is one of the biggest puzzles for an efficient modern business. Different systems that processing business data are more than a business can handle. For each new process that does not fit within the framework of existing tools, we look for specialized new tools until the administration of all the tools we use becomes more of a burden than the benefits that they bring. In this course, without programming experience and without the use of code, we will learn how to create the business management programs we need and not only automate the operation of these programs, but also combine the endless different tools used, into one system. 

BDPAP

At B‌IG DATA, we digitize all possible processes in the company. However, developing business applications is often an expensive and complicated process. In this part of the course, we will learn how to digitize a variety of processes using standard Microsoft tools in minutes.
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During the course we will examine in detail the following and related topics:
‌BIG DATA relation in Scalable Architecture Programs, Microsoft Power Platform concept, Datavers, Power Apps connection with SharePoint and SQL Server, Canvas Apps from Template, Canvas Apps from Blank, Functions, Variables, Model Driven Apps, Solutions, Flows, Power Apps Portal.

BDPAU

With the help of the P‌ower Automate tool, we will learn how to automate processes that we still perform inefficiently, such as signing holiday requests.
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‌During the course we will examine in detail the following and related topics:
‌Data Flow Automation with Power Automate, Power Automate Cloud, Integration with Microsoft Outlook, Forms, SharePoint and Teams, Power Automate Cloud from Template and Blank, Approvals, Functions, Variables, Power Automate Desktop, Robotic Process Automation, Legacy Program Automation, Combination of Power Automate Could with Power Automate Desktop, Power Automate Business Processes.

BDPBI

Power BI is an indispensable tool for interactive data visualization. In this tool, we will realize both the acquired knowledge of Power Apps and the flows of Power Automate, combining everything into a single and complex system.
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‌‌During the course we will examine in detail the following and related topics:‌
Power BI Desktop, Power BI Service, Power BI Gateway, Different Data Sources, ODBC Connector, Connection to Server Data, Query Editor, KPI Dashboards, AI Analytics, Forecasting, Tooltips, What-if parameter, Table Relationship, Bookmarks, Drill Actions, DAX, Power Query, SQL Query, Python integration,  Data from WEB, Custom Theme, Templates.

BDPVA

Now 60% of people like to talk to virtual bots, which not only saves us resources, but also eliminates errors and poor customer service. With the help of this tool, we will learn how to create our own virtual bot in a few minutes without code.
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‌During the course we will examine in detail the following and related topics:
Microsoft Power Virtual Agent from Blank, Power Virtual Agent from FAQ, Topics, Entities, Variables, Authoring Canvas, Conditions, Publishing.

BDPSH

In this part of the course, we will take a close look at our computers and the possibilities of automating computer processes using an updated command line.
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‌During the course we will examine in detail the following and related topics:
PowerShell Features, Compartion with Comand Prompt, Power Shell ISE, PowerShell Administrator mode, PowerShell versions, Comandlets, Alies, Arguments, Modules, Pipelines, Variables, Filtering, Sorting, Measuring, PowerShell Code Editing Environments.


Certification upon completion of courses

Each student who completes at least one module is certified by the Datacademy with a “successfully finished” certificate.

Students who have completed at least 3 module programs and scored more than 40 points are certified according to the final assessment of the course: ‌

‌40+ = „Specialist“
80+ = „Professional“
90+ = „Expert“
100 = „Scientist“

Datacademy provides additional free consultations until Professionals, Experts, and Scientists gain international Microsoft certifications and LinkedIn skills assessments.

Datacademy Scientists

- Python - skill assessment
- Machine learning - skills assessment

Datacademy Experts

- Python - skill assessment

Datacademy Professionals


‌Recruitment after training

All graduates who have completed at least 3 modules are invited to the Datacademy Career Center for further advice on employment in the IT sector. After the consultation, interviews with potential employers are coordinated.

Students who complete at least 3 modules with a 40+ final score are automatically added to the Talent Acceleration Program Candidate List.

Students who complete at least 3 modules with the highest final score of 100 receive an invitation to the Talent Acceleration Program with a 3-month paid scholarship.