Data Engineer – promising prospects in Big Data Management

Who can apply for our Data Engineer Bootcamp?
The task of a Data Engineer is to collect, process and review data as an essential basis for big data, data warehouse and analysis projects. Data Engineers are essential employees in modern, data-driven companies. It is an important area to make the best use of so-called big data.

They play an essential role in the advancement of the corporate objectives and the success of the company. Likewise, a Data Engineer constantly improves the existing databases and the algorithmus used, thus constantly developing the analysis of the data.

Prerequisites for the profession are a technical interest, basic knowledge of programminginsight into data processing, and an understanding of data processes.

With increasing digitalisation, the need for employees familiar with IT, data, data security and the technical processing of data and its constant optimisation is also growing. In this regard, the career opportunities for a Data Engineer are very high.

Your tasks in the company upon successful completion

Data Engineer Bootcamp

A Data Engineer creates the link between hardware and data processing by monitoring data sources and preparing the existing data, not only to manage it but to prepare it for analysis and interpretation.

An empathetic personality and social skills also play a role in the job profile. This is because a data engineer has contact with employees from other departments within the company, to whom facts about data and its analysis and conclusions, which are often perceived as complex by laypersons, have to be explained.

Data Engineer

Why are Data Engineers in high demand?

The training opportunities are lagging the great demand. The typical Data Engineer is usually a job or career changer. This is exactly why we offer this exciting bootcamp.

The job market for Data Engineers is excellent. The demand is so high that specialists generally have a background in other fields such as computer science, business informatics, statistics, or other computer-related activities.

Many have self-taught know-how through “learning by doing”. This is changing now with our bootcamp Data Engineer.

Working environment

The digitalization of businesses is rapidly progressing, and with it, the need to administer, manage, improve, and ultimately process the generated and collected data for use by the company. Professionals with the relevant knowledge are in great demand.The great thing is that the job can also be done remotely from anywhere in the world. Employers focus primarily on the result and not necessarily on the number of hours worked.Visualize being in an Airbnb in Italy, working on your new project for a German company. Your morning starts with a walk in the mountain. During the day, you use the time for team meetings and client communication – and you finish your day with a glass of wine on your terrace!

Just a dream? Your future starts here if you choose the bootcamp Data Engineer.

Become a Data Engineer AT A GLANCE

DURATION, CONTENT, TEMPORAL, CONCEPTION

Duration of the Bootcamp: 12 weeks
Lesson times of the course: Monday – Friday | 9 am to 6 am (GMT)
Total: 540 learning units.

*1 LU = 45 min

Full Data Engineer Course (Week 1-12)
Learning  Units (LU)
General technological understanding
27
Python
144
Databases, models and visualisation
112
Machin Learning models
72
Advanced skills
39
Green IT
27
Exercises
45
Final project
74
Sum of learning  units
540

START NOW!

Next starting date: 24.07.2023 (GMT)

More about the course

Data Engineer Greenbootcamps

In this course, participants receive practical training and user-related skills in the field of the Data Engineer. Data Engineers develop and optimise the systems that enable Data Scientists and Data Analysts to perform their tasks and work. The course presents a variety of content and competencies needed to meet the professional requirements of a Data Engineer.

This course is aimed in particular at qualified professionals who do not yet have any experience with programming or the Python programming language.

The following modules form the basis of the knowledge transfer:

  • Exercises accompanying the lessons (independent)
  • Team-oriented group work
  • Practical examples and hands-on exercises
  • Learning success checks

Hybrid training (On-site classroom training or virtual face-to-face teaching via Teams or similar VC (Virtual Classroom).

  • Bootcap prepayment: If you pay your boot camp in full in advance, you will receive an 8% discount on the seminar fee.
  • Bootcamp installments: To reduce your liquidity burden, we offer you to pay the boot camp in three monthly installments.
  • Bootcamp schoolarship: We work with a network of business companies. They are interested in hiring newly qualified graduates as trainees or junior consultants and – assuming the interview is successful – they will contribute part or all of the seminar fees.
  • Education Voucher (Bildungsgutschein): If you are unemployed or threatened by unemployment, you can – if you have a German residence permit – get up to 100% of the costs subsidized through an education voucher. (Bildungsgutschein).

The training is accompanied by job application support measures. Greenbootcamps has a network of companies that are looking for employees with the Data Engineer qualification.

These cooperation partners are involved in the application process from the beginning and accompany the participants during the bootcamps. Upon successful completion, they are guaranteed a permanent position.

Data Engineer Bootcamp

Upon successful completion, the participant will receive an internationally valid certificate from Greenbootcamps as well as an evaluation of their performance. Furthermore, you will receive a certificate as ITIL® 4 Specialist: Sustainability In Digital & IT.

You should possess basic IT skills – such as the use of Office products and the internet. Furthermore, participants should be proficient in the language used in the course (English).

Participants work with digital learning materials that they can download via the learning management system. The learning management system is online and is available to all participants during the training.

MS Office, Internet access, advanced open-source applications (such as MS Power BI Desktop, Hadoop, Anaconda), learning management system, online communication system.

MS Office, internet access, additional open source applications (such as MS Power BI Desktop, Hadoop, Anaconda), learning management system, online communication system.

START NOW!

Next starting date: 24.07.2023 (GMT)

Teaching content

540 Learning Unit in total, 1 LU = 45 min 

Data Scientist fullstack bootcamp
Learning Unit (LU)
General understanding of technology ​​
27
General understanding of technology ​
  • Setting up Python Environment
  • Unix
  • Git, Github
  • Intro on DA, DS, DE
27
Python training session
144
- Python Programming (Basics)
  • Intro to Git
  • Intro to Jupiter lab
  • Coding best practices

Basics:

  • Numeric variable types
  • Strings
  • If statement (if, elif, else)
  • Loops (while, for)

Data Structures in Python:

  • Lists
  • Sets
  • Mutability
  • Dictionaries
  • Comprehension
72
Python Programming (Advanced)
  • Functions
    • Introduction to functions
    • Function definitions
    • Calling functions
    • Lambda functions
    • Function challenges
  • Introduction to pandas
    • Pandas functionalities
    • Pandas visualization
    • Exercise on functionalities and visualizations
    • More exercise using data
    • Data frames and combining data frames.
  • Introduction to Numpy
    • Numpy functionalities
    • Exercise on Numpy functionalities
    • Dealing with Data and Time data
72
Databases, Models und Visualizations
112
SQL, NoSQL
36
Data Warehousing
18
Big data processing using Hadoop, Spark
31
Data Visualization
  • Matplotlib
  • Seaborn
  • Plotly
18
Data security
9
Machine Learning models
72
EDA and modelling
  • Concepts of different regression and classification models
  • Evaluation matrices for regression and classification models
  • Time Series model
  • Natural Language Processing (NLP)
  • Implement the above ML models vs use Python packages
72
Advanced skills
39
AWS/Azure cloud
9
Apache Airflow
9
Docker, Kubernetest
9
APIs (Flask, Fast APIs)
9
How to use ChatGPT
3
Green IT
27
Practice days spread over the course
45
Final project
74
Sum of learning units
540

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Once you click apply, our admissions team will contact you to arrange a call to answer your questions and explain our application process. if you have any questions feel free to contact us.