Data Engineering Foundations

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Learning Experience9.2
Content Rating9.2

Learn data engineering prerequisites, gain skills that can be applied directly to a data career, and build the foundation of your data engineering career.

Last updated on March 14, 2021 11:26 pm

About this Course

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 simple 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 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.

Build the Foundation for a Data Engineering Career. Develop hands-on experience with Python, SQL, and Relational Databases and master the Data Engineering ecosystem’s fundamentals.

What will you learn from this course?

  • 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.

Syllabus

There are 5 Courses in this Specialization

Course 1. Introduction to Data Engineering

This course introduces you to the core concepts, processes, and tools you need to learn to get a foundational knowledge of data engineering. You will understand the modern data ecosystem and the role Data Engineers, Data Scientists, and Data Analysts play in this ecosystem.
The Data Engineering Ecosystem includes several different components. It includes disparate data types, formats, and sources of data. Data Pipelines gather data from multiple sources, transform it into analytics-ready data, and make it available to data consumers for analytics and decision-making.

Data repositories, such as relational and non-relational databases, data warehouses, data marts, data lakes, and big data stores process and store this data. Also, Data Integration Platforms combine disparate data into a unified view for the data consumers. You will learn about each of these components in this course. You will also learn about Big Data and the use of some of the Big Data processing tools.

A typical Data Engineering lifecycle includes architecting data platforms, designing data stores, and gathering, importing, wrangling, querying, and analyzing data. It also provides performance monitoring and finetuning to ensure systems are performing at optimal levels. In this course, you will learn about the data engineering lifecycle. You will also learn about security, governance, and compliance.

Data Engineering is recognized as one of the fastest-growing fields today. In this course, we have discussed the career opportunities available in the field and the different paths you can take to enter this field.

The course also includes hands-on labs that guide you through creating your IBM Cloud Lite account, provision a database instance, load data into the database instance, and perform some basic querying operations that help you understand your dataset.

Course 2. Python for Data Science, AI & Development

Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages. There has never been a greater demand for professionals to apply Python fundamentals to drive business solutions across industries.

This course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and create a final project to demonstrate your new skills.
By the end of this course, you’ll feel comfortable creating basic programs, working with data, and solving real-world problems in Python. You’ll gain a strong foundation for more advanced learning in the field and develop skills to help advance your career.

This course can be applied to multiple Specialization or Professional Certificate programs. Completing this course will count towards your learning in any of the following programs:

  • IBM Applied AI Professional Certificate
  • Applied Data Science Specialization
  • IBM Data Science Professional Certificate
  • Upon completing any of the above programs and earning a Specialization completion certificate from Coursera, you’ll also receive a digital Badge from IBM recognizing your expertise in the field.

Course 3.  Python Project for Data Engineering

This mini-course applies foundational Python skills by implementing different techniques to collect and work with data. Assume the role of a Data Engineer and extract data from multiple file formats, transform it into specific data types, and then load it into a single source for analysis. Continue with the course and test your knowledge by implementing web scraping and extracting data with APIs, all with multiple hands-on labs.

After completing this course, you will have acquired the confidence to begin collecting large datasets from multiple sources and transform them into one primary source or begin web scraping to gain valuable business insights, all with the use of Python.

Pre-requisite for the Python Project for Data Engineering:

Python for Data Science, AI and Development course from IBM is a pre-requisite for this project course. Please ensure that before taking this course, you have either completed the Python for Data Science, AI, and Development course from IBM or have equivalent proficiency in working with Python and data.

NOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.

Course 4. Introduction to Relational Databases

Are you ready to dive into the world of data engineering? You’ll need a solid understanding of how data is stored, processed, and accessed. You’ll need to identify the different types of databases appropriate for the data you are working with and what processing the data requires.

In this course, you will learn the essential concepts behind relational databases and Relational Database Management Systems (RDBMS). You’ll study relational data models and discover their creation, and benefits that they bring, and how you can apply them to your data. You’ll be introduced to several industry-standard relational databases, including IBM DB2, MySQL, and PostgreSQL.

This course incorporates hands-on, practical exercises to help you demonstrate your learning. You will work with real databases and explore real-world datasets. You will create database instances and populate them with tables.
No prior knowledge of databases or programming is required.

Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon completing the course.

Course 5. Databases and SQL for Data Science with Python

Much of the world’s data resides in databases. SQL (or Structured Query Language) is a powerful language for communicating and extracting data from databases. Working knowledge of databases and SQL is a must if you want to become a data scientist.

This course aims to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. Also, you will get started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning. As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud.

Through a series of hands-on labs, you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python. No prior knowledge of databases, SQL, Python, or programming is required. Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon completing the course. LIMITED TIME OFFER: Subscription is only USD 39 per month to access graded materials and a certificate.

Project for the IBM Data Engineering Specialization:

All Specialization courses contain multiple hands-on labs and assignments to help you gain practical experience and skills.

The projects range from working with data in multiple formats to transforming and loading that data into a single source to analyze socio-economic data with SQL and work with advanced SQL techniques.

You will work hands-on with multiple real-world databases and tools, including MySQL, PostgresSQL, IBM Db2, PhpMyAdmin, pgAdmin, IBM Cloud, Python, Jupyter notebooks, Watson Studio, etc.

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

$49.00

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  • Coursera
  • IBM
  • Microdegree
  • Self-paced
  • Beginner
  • 3+ Months
  • Paid Course (Paid certificate)
  • English
  • Artificial intelligence Data Engineering Data Science Data Science with 'Python' SQL
Learning Experience
9.2
Content Rating
9.2
PROS: Hands-on experience with Python, SQL & fundamentals of the Data Engineering ecosystem. Very informative and well-structured courses. Good for getting acquainted with programming.
CONS: Needs more practice material on connection to a database and use of SQL magic. Issues with IBM cloud connectivity.

Description

About this Course

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 simple 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 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.

Build the Foundation for a Data Engineering Career. Develop hands-on experience with Python, SQL, and Relational Databases and master the Data Engineering ecosystem’s fundamentals.

What will you learn from this course?

  • 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.

Syllabus

There are 5 Courses in this Specialization

Course 1. Introduction to Data Engineering

This course introduces you to the core concepts, processes, and tools you need to learn to get a foundational knowledge of data engineering. You will understand the modern data ecosystem and the role Data Engineers, Data Scientists, and Data Analysts play in this ecosystem.
The Data Engineering Ecosystem includes several different components. It includes disparate data types, formats, and sources of data. Data Pipelines gather data from multiple sources, transform it into analytics-ready data, and make it available to data consumers for analytics and decision-making.

Data repositories, such as relational and non-relational databases, data warehouses, data marts, data lakes, and big data stores process and store this data. Also, Data Integration Platforms combine disparate data into a unified view for the data consumers. You will learn about each of these components in this course. You will also learn about Big Data and the use of some of the Big Data processing tools.

A typical Data Engineering lifecycle includes architecting data platforms, designing data stores, and gathering, importing, wrangling, querying, and analyzing data. It also provides performance monitoring and finetuning to ensure systems are performing at optimal levels. In this course, you will learn about the data engineering lifecycle. You will also learn about security, governance, and compliance.

Data Engineering is recognized as one of the fastest-growing fields today. In this course, we have discussed the career opportunities available in the field and the different paths you can take to enter this field.

The course also includes hands-on labs that guide you through creating your IBM Cloud Lite account, provision a database instance, load data into the database instance, and perform some basic querying operations that help you understand your dataset.

Course 2. Python for Data Science, AI & Development

Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages. There has never been a greater demand for professionals to apply Python fundamentals to drive business solutions across industries.

This course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and create a final project to demonstrate your new skills.
By the end of this course, you’ll feel comfortable creating basic programs, working with data, and solving real-world problems in Python. You’ll gain a strong foundation for more advanced learning in the field and develop skills to help advance your career.

This course can be applied to multiple Specialization or Professional Certificate programs. Completing this course will count towards your learning in any of the following programs:

  • IBM Applied AI Professional Certificate
  • Applied Data Science Specialization
  • IBM Data Science Professional Certificate
  • Upon completing any of the above programs and earning a Specialization completion certificate from Coursera, you’ll also receive a digital Badge from IBM recognizing your expertise in the field.

Course 3.  Python Project for Data Engineering

This mini-course applies foundational Python skills by implementing different techniques to collect and work with data. Assume the role of a Data Engineer and extract data from multiple file formats, transform it into specific data types, and then load it into a single source for analysis. Continue with the course and test your knowledge by implementing web scraping and extracting data with APIs, all with multiple hands-on labs.

After completing this course, you will have acquired the confidence to begin collecting large datasets from multiple sources and transform them into one primary source or begin web scraping to gain valuable business insights, all with the use of Python.

Pre-requisite for the Python Project for Data Engineering:

Python for Data Science, AI and Development course from IBM is a pre-requisite for this project course. Please ensure that before taking this course, you have either completed the Python for Data Science, AI, and Development course from IBM or have equivalent proficiency in working with Python and data.

NOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.

Course 4. Introduction to Relational Databases

Are you ready to dive into the world of data engineering? You’ll need a solid understanding of how data is stored, processed, and accessed. You’ll need to identify the different types of databases appropriate for the data you are working with and what processing the data requires.

In this course, you will learn the essential concepts behind relational databases and Relational Database Management Systems (RDBMS). You’ll study relational data models and discover their creation, and benefits that they bring, and how you can apply them to your data. You’ll be introduced to several industry-standard relational databases, including IBM DB2, MySQL, and PostgreSQL.

This course incorporates hands-on, practical exercises to help you demonstrate your learning. You will work with real databases and explore real-world datasets. You will create database instances and populate them with tables.
No prior knowledge of databases or programming is required.

Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon completing the course.

Course 5. Databases and SQL for Data Science with Python

Much of the world’s data resides in databases. SQL (or Structured Query Language) is a powerful language for communicating and extracting data from databases. Working knowledge of databases and SQL is a must if you want to become a data scientist.

This course aims to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. Also, you will get started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning. As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud.

Through a series of hands-on labs, you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python. No prior knowledge of databases, SQL, Python, or programming is required. Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon completing the course. LIMITED TIME OFFER: Subscription is only USD 39 per month to access graded materials and a certificate.

Project for the IBM Data Engineering Specialization:

All Specialization courses contain multiple hands-on labs and assignments to help you gain practical experience and skills.

The projects range from working with data in multiple formats to transforming and loading that data into a single source to analyze socio-economic data with SQL and work with advanced SQL techniques.

You will work hands-on with multiple real-world databases and tools, including MySQL, PostgresSQL, IBM Db2, PhpMyAdmin, pgAdmin, IBM Cloud, Python, Jupyter notebooks, Watson Studio, etc.

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • Coursera
  • IBM
  • Microdegree
  • Self-paced
  • Beginner
  • 3+ Months
  • Paid Course (Paid certificate)
  • English
  • Artificial intelligence Data Engineering Data Science Data Science with 'Python' SQL

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Data Engineering Foundations
Data Engineering Foundations

$49.00

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