CF

Computer Science Fundamentals

1h 30m 17s
English
Paid

Building a solid foundation in computer science is crucial for advancing in the technology sector, especially if you're aiming to become a Data Engineer. This course is designed to be your first guiding step in this journey.

Course Overview

This course offers an in-depth guide to the essential topics and resources in computer science that are particularly important for Data Engineers. We primarily focus on software development and relational databases.

Software Development

Key Learning Outcomes

  • Understanding how to write code and design program architecture.
  • Learning to model databases using UML, sequence diagrams, and other tools.
  • Mastering the effective use of Git for team collaboration.
  • Exploring the advantages of agile development and the connection between development and operations (DevOps).

Relational Databases

Core Concepts

  • Understanding the differences between OLTP and OLAP databases.
  • Grasping the basics of data modeling and normalization.
  • Mastering key SQL queries essential for every Data Engineer.

Additional Topics

At the end of the course, useful resources and recommendations are available on the following topics:

  • Fundamentals of computer networking.
  • Linux operating system.
  • REST API essentials.

Feeling overwhelmed? Don't worry! Each section is broken down step-by-step with plenty of examples. For each tool mentioned, you'll find links to resources where you can either learn how to use them or deepen your existing knowledge.

About the Author: Andreas Kretz

Andreas Kretz thumbnail

Andreas Kretz is a German data engineer and one of the most widely followed independent voices on data engineering as a career discipline. He runs the Plumbers of Data Science brand and has been publishing tutorial material continuously since the field consolidated around the modern lake-house stack (Spark, Kafka, Snowflake, Databricks, Airflow).

His CourseFlix listing is the largest single-author catalog under this source — over thirty courses spanning data-pipeline construction, streaming architectures, the cloud-native data stack on AWS / Azure / GCP, the Python and Scala tooling that dominates the field, and the soft-skills / career side of breaking into data engineering. Material is paid and aimed at engineers transitioning into data work or already-working data engineers picking up specific tools.

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#1: Introduction
All Course Lessons (16)
#Lesson TitleDurationAccess
1
Introduction Demo
05:00
2
Coding
08:15
3
UML Diagrams
08:14
4
Git
05:46
5
Agile Roles
05:11
6
Agile Process & Tools
05:07
7
Azure DevOps
07:18
8
DevOps
05:38
9
OLTP vs OLAP
08:29
10
Why Relational DBs and SQL is so important
02:36
11
Data Modeling
05:59
12
ER Model
04:16
13
Normalization
07:32
14
Primary & Foreign Keys
02:46
15
Building your DB with dbdiagram.io
03:57
16
Important SQL Queries
04:13
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Books

Read Book Computer Science Fundamentals

#TitleTypeOpen
1Computer Networking Links PDF
2Linux Links PDF
3Software Development Links PDF

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Frequently asked questions

What prerequisites should I have before enrolling in this course?
The course is designed as an introductory guide, so no formal prerequisites are required. However, a basic understanding of programming concepts and familiarity with databases can be beneficial. The course covers foundational topics such as coding, database modeling, and SQL, making it accessible for beginners aiming to build a career as a Data Engineer.
What practical projects will I work on during the course?
Throughout the course, students will engage in practical exercises using tools like UML for database modeling and dbdiagram.io for building databases. Key learning outcomes include writing code, designing program architecture, and learning effective Git collaboration. These projects aim to solidify understanding of both software development and relational databases.
Who is the target audience for this course?
The course is ideal for those interested in pursuing a career as a Data Engineer, specifically individuals who are in the early stages of their career or those looking to establish a solid foundation in computer science. It covers essential topics in software development and relational databases, catering to beginners with a focus on practical applications.
How does this course compare to other computer science courses?
This course distinguishes itself by focusing on computer science fundamentals particularly relevant to Data Engineers. It covers both software development practices and relational database concepts, including key SQL queries and data modeling. Additionally, the course addresses practical tools like Git and agile methodologies, offering a comprehensive approach tailored to the needs of aspiring Data Engineers.
What specific tools and platforms will I learn to use?
Participants will gain hands-on experience with several tools and platforms crucial for Data Engineers. These include Git for version control, dbdiagram.io for database building, and Azure DevOps for understanding the integration of development and operations. The course also provides insights into using UML for database modeling and sequence diagrams.
What topics are not covered in this course?
While the course covers a broad range of computer science fundamentals, it does not delve into advanced topics such as machine learning, artificial intelligence, or non-relational databases like NoSQL. The focus remains on foundational concepts pertinent to software development and relational databases, ensuring a strong base for further specialized learning.
How can this course benefit my career in data engineering?
This course provides essential skills and knowledge for a career in data engineering. By mastering key areas like software development, database modeling, and SQL, students can build a strong foundation. The skills acquired are transferable and applicable to various roles in the technology sector, particularly in data management and analysis, thereby enhancing career prospects in data engineering.