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Leo Porter, Ilkay Altintas, Rav Ahuja, Hima Vasudevan, Joseph Santarcangelo, Sandip Saha Joy, Ramesh Sannareddy, Priya Kapoor, Lin Joyner, Rose Malcolm, and Abhishek Gagneja

Data engineering is one of the fastest-growing tech occupations, where the demand for skilled data engineers far outweighs the supply. The goal of data engineering is to make quality data available for fact-finding and data-driven decision making. This Specialization from IBM will help anyone interested in pursuing a career in data engineering by teaching fundamental skills to get started in this field. No prior data engineering experience is required to succeed in this Specialization.

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Data engineering is one of the fastest-growing tech occupations, where the demand for skilled data engineers far outweighs the supply. The goal of data engineering is to make quality data available for fact-finding and data-driven decision making. This Specialization from IBM will help anyone interested in pursuing a career in data engineering by teaching fundamental skills to get started in this field. No prior data engineering experience is required to succeed in this Specialization.

The Specialization consists of 5 self-paced online courses covering skills required for data engineering, including the data engineering ecosystem and lifecycle, Python, SQL, and Relational Databases. You will learn these data engineering prerequisites through engaging videos and hands-on practice using real tools and real-world databases. You'll develop your understanding of data engineering, gain skills that can be applied directly to a data career, and build the foundation of your data engineering career.

Upon successfully completing these courses, you will have the practical knowledge and experience to delve deeper into data engineering and work on more advanced data engineering projects.

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What's inside

Five courses

Introduction to Data Engineering

Start your journey in one of the fastest growing professions today with this beginner-friendly Data Engineering course! You will be introduced to the core concepts, processes, and tools you need to know in order to get a foundational knowledge of data engineering. You will begin this course by understanding what is data engineering as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in this exciting field.

Python for Data Science, AI & Development

(0 hours)
Kickstart your Python learning with this beginner-friendly course. Python is one of the most popular programming languages, and demand for Python skills is high. This course will teach you Python basics, data types, data structures, logic concepts, and Python libraries. You'll also use Python for data collection and web scraping. By the end of this course, you'll be able to create basic Python programs, work with data, and automate tasks.

Python Project for Data Engineering

Showcase your Python skills in this hands-on Data Engineering Project! You will extract data from multiple sources, convert it into specific formats, and prepare it for loading into a database. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE and Python Programming.

Introduction to Relational Databases (RDBMS)

(0 hours)
Are you ready to dive into the world of data engineering? This beginner level course will give you a solid understanding of how data is stored, processed, and accessed in relational databases (RDBMSes). You will work with different types of databases that are appropriate for various data processing requirements.

Databases and SQL for Data Science with Python

(0 hours)
Working knowledge of SQL is a must for data professionals. Much of the world's data resides in databases. SQL is a powerful language used for communicating with and extracting data from databases. In this course, you will learn SQL inside out—from the very basics of Select statements to advanced concepts like JOINs.

Learning objectives

  • Working knowledge of data engineering ecosystem and lifecycle. viewpoints and tips from data professionals on starting a career in this domain.
  • Python programming basics including data structures, logic, working with files, invoking apis, using libraries such as pandas and numpy, doing etl.
  • Relational database fundamentals including database design, creating schemas, tables, constraints, and working with mysql, postgresql & ibm db2.
  • Sql query language, select, insert, update, delete statements, database functions, stored procs, working with multiple tables, joins, & transactions.

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