Apply now | 2500₾

Data Engineering

  • 5.0 Rate

  • 33 Lecture

  • 99 hours

  • 17 Weeks

Data Engineering is the foundation on which the modern Data ecosystem stands. At its core, the goal of data engineering is to design and develop systems and infrastructure that automatically collect, store, and analyze data. Primarily, data engineering aims to develop methodologies and tools that ensure maximum process perfection, information sustainability, and reliability.

Along with fundamental concepts, students will learn to use key technologies and tools for data engineering such as Apache Airflow, Apache Kafka, temporary and persistent data storage systems (SQL, PostgreSQL, Redis), designing web services for data (FastAPI), and various Python libraries. They will understand how different technologies/tools interact with each other and how theoretical concepts help in perfecting practice.

Outcome

Skills Acquired: Python, FastAPI, Apache Airflow, Apache Kafka, Data Workflow, Data Merting, Data Warehousing, PostgreSQL, Redis, SQL,

  • Use Python for data transformation and manipulation

  • Practically apply Python's data engineering and database libraries

  • Design architectural designs for high-performance and sustainable projects

  • Set up and design high-performance web services (FastAPI)

  • Ensure data sustainability and quality

  • Differentiate and implement ETL and ELT processes in various Data projects

  • Set up, orchestrate, design, and analyze data transformation processes

  • Work with temporary and persistent data storage systems (Redis and Postgres)

  • Practically use Apache Airflow, a data flow orchestration and design system

  • Understand the concepts of DAGs and Schedulers

  • Analyze and design Data Workflow systems

  • Understand the fundamental concepts of Apache Kafka

  • Practically work with Data Streams and Message Brokers

  • Analyze/design dependencies and relationships between data

  • Understand and work with principles of various data storage concepts (Data Warehousing, Data Marting)

Nov 19 2500₾

Tue-Thu 20:00-22:00

Apply now
Apr 15 2500₾

Tue 20:00-23:00, Sat 12:00-15:00

Pre-enrollment
Split your payment
TBC installment
BOG installment

Who is this course for?

Developers

Someone who has already started learning Python and has an analytical, detail-oriented mindset.

Data analysts

For data analysts who know the basics of Python and want to develop their career.

Program includes

Alumni Club

After successfully completing the final exam, graduates will be automatically enrolled in the Alumni Club. This membership grants them access to exclusive events, content, and special offers from our partner companies

Work Based Learning

The course includes practice-based learning, including assignments/exercises and individual projects.

Bilingual Certification

Upon successful completion of the course, students will receive a bilingual certificate.

Graduate feedback

5.0 Rate

Syllabus

What is Data Engineering
Data Engineer's responsibilities
Data-oriented thinking
Perfectionism
Dataclasses
Functional Programming and "Sequential code"
Generators
Lab session / Lecture and practical assignment
Context managers
Error handling
Modules and packages
Higher-order functions
Lab session / Lecture and practical assignment
Overview of pandas library
What is DataFrame
Python and pandas
Simple DataFrame operations (selection, filtering, indexing)
Lab session
Grouping data in Pandas DataFrame
Data aggregation
Data Cleaning and Handling
Merge, Join operations

Pick your suitable time

Lecturers

Levan Alibegashvili

Data Engineering

Levan Alibegashvili

Data Engineering

Levan has many years of experience working as a data engineer and scientist. He currently holds the position of Senior Data Engineer at DataArt, where he works on setting up SnowFlake storage and automating data input. Previously, Levan held the position of Data Lead at EPAM Systems, where his main activities included data migration, development of ETL/ELT processes, and business analytics. Levan has work experience in both large international companies (FedEx Ground Services, Betsson Group) and the public sector (Ministry of Agriculture of Georgia, National Bank of Georgia). His career includes diverse positions - from Big Data Unit Head to Policy Advisor, which gives him a unique perspective in the field of data science. Levan has also actively worked as a freelancer, where he served as an expert in Microsoft Excel and Power BI. He has extensive experience in technologies such as Python, SQL, Snowflake, as well as in developing and implementing machine learning models.

Linkedin

FAQs for this course

A: Data Engineering is increasingly vital in today's digital world. It bridges the gap between raw data and actionable insights, enabling businesses to make informed decisions. Data Engineers build and maintain the infrastructure that processes massive amounts of data, making it accessible and useful for analysis. This field offers excellent career prospects, competitive salaries, and the opportunity to work with cutting-edge technologies while solving complex real-world problems.
A: The course requires basic knowledge of Python, analytical and detail-oriented thinking abilities, as well as English language proficiency at B2 level. A strong interest and motivation to work with data is essential.

Your search Digital Designer did not match any documents

Unable to locate relevant information?

Get Free consultation

You may interest

Beginner
1350₾
[Tech] Recruiting
The demand for tech talent has never been higher, making it increasingly challenging for companies to find and attract the best human resources. In this competitive environment, recruiters need not only a deep understanding of the tech industry but also practical expertise in how to find, onboard, and retain A+ tech professionals. This course is packed with practical work and designed to provide a detailed understanding of current trends, platforms, and qualifications - it's a mini MBA in human resource recruitment for the technology sector. Throughout the course, students will learn the complete 360-degree process of [Tech] recruiting.
14 Lectures
42 hours
Intermediate
1000₾
AI in Graphic Design
The course includes Practice-based learning, which includes assignments/exercises and an individual project. During the program, you will receive advice and consultations on career planning. Upon successful completion, you will have career support from Commschool's partner companies, which includes exclusive job offers and internships. Upon successful completion of the course, the student will receive a bilingual certificate.
8 Lectures
24 hours hours
Beginner
950₾
AI in Practice
Artificial Intelligence has become an integral part of the modern manager's role. Therefore, the better we understand what language to use and how to communicate with artificial intelligence, the easier it becomes to integrate it into our daily lives. Most importantly, artificial intelligence enables efficient time use and helps avoid routine tasks. Throughout the lectures, we will study how to use popular AI systems such as ChatGPT, Claude.ai, and Gemini. Working on practical assignments and the final project will help us gain in-depth knowledge about using AI tools and 'taming' them to our needs.
6 Lectures
18 hours
Intermediate
1600₾
Android Development
In the modern world, no digital product or service is considered complete without a mobile application. Approximately 86% of mobile users worldwide use Android, making it the most popular direction in mobile development.Mobile applications are a type of software, so the initial stage of the course focuses on learning programming languages and approaches. After this, we move on to Android-specific frameworks, tools, and the application creation process.
24 Lectures
72 hours hours
Intermediate
1500₾
Angular
In recent years, Front-End Development has emerged as one of the fastest-growing and most promising industries. The Angular Framework, created by Google in 2016 and still actively improving, has played a significant role in this growth. Using the Angular Framework, you will be able to build TypeScript-Based Responsive Single-Page Applications (SPA). Throughout the course, the main focus will be on deeply learning Angular's core concepts. Considerable time will be devoted to in-depth analysis of TypeScript and RxJS, demonstrating why typing (TS) and the reactive paradigm (RxJS) are valuable.
20 Lectures
60 hours
Beginner
1200₾
Content Marketing
Based on the final project results, the top graduate will be guaranteed an internship at one of our partner creative agencies.Content – /kənˈtɛnt/ – isn't a Georgian word, but it doesn't sound bad. Content is a big part of our daily lives; it can be understood, viewed, and perceived in various ways, which is why content is everything and content is everywhere. With properly planned content, many great goals can be achieved; the key is to hit the target.Content is what pleases the eye and doesn't lack for the ear. This course will be one big content experience, filled with practical work where we'll see, create, change, and then create again with our own hands, across different digital channels, using various means, resources, and ideas.
14 Lectures
42 hours
Beginner
1100₾
Creative Advertising
Advertising has a peculiar characteristic - we often try to avoid it, block it, or "skip" it. Yet, we might eagerly anticipate a new campaign from our favorite brand, enjoying Nike's commercials, McDonald's and Burger King's battles, or Coca-Cola's holiday caravans. When does advertising become entertaining rather than annoying? How do some brands achieve this? What's behind an effective advertising campaign? This course aims to answer these questions.We'll explore the fundamentals of advertising communication, key principles, and tools. You'll learn about the mechanics of creative agencies, their responsibilities, and how to maintain a harmonious relationship with clients. We'll analyze and discuss both global and Georgian case studies.Students will get an inside look at the advertising industry and develop managerial skills in campaign planning, creative evaluation, and working with creative teams.
11 Lectures
33 hours
Beginner
1500₾
Data Analytics
It can be said that today's world stands on data. For most companies, data is their most important asset, but data without analytics is just numbers that cannot bring any added value. The role of a data analyst is precisely to translate large amounts of raw information into insights that push companies to take valuable and significant steps. During the course, we will cover the full cycle of data analysis: formulating a business problem as an analytical problem, finding relevant data, cleaning and processing it using SQL, analyzing and visualizing data using Power BI, communicating the findings, and making decisions based on analytics.
18 Lectures
36 hours
Intermediate
2500₾
Data Engineering
Data Engineering is the foundation on which the modern Data ecosystem stands. At its core, the goal of data engineering is to design and develop systems and infrastructure that automatically collect, store, and analyze data. Primarily, data engineering aims to develop methodologies and tools that ensure maximum process perfection, information sustainability, and reliability. Along with fundamental concepts, students will learn to use key technologies and tools for data engineering such as Apache Airflow, Apache Kafka, temporary and persistent data storage systems (SQL, PostgreSQL, Redis), designing web services for data (FastAPI), and various Python libraries. They will understand how different technologies/tools interact with each other and how theoretical concepts help in perfecting practice.
33 Lectures
99 hours
Intermediate
2500₾
Data Science
Data Science can be defined as a combination of mathematics, business, algorithms, and machine learning techniques that help us find patterns in data and make discoveries that will significantly simplify and make decision-making more effective for organizations.The course will cover all important areas of Data Science. Using Python, the most commonly used programming language for data analysis, we will cover the full cycle of data science - initial data processing, preparation for modeling, building machine and deep learning algorithms, training, application, and turning them into products.Throughout the program, students will work independently on an individual project.
32 Lectures
96 hours