IBM Data Engineering Professional Certificate

IBM Data Engineering Professional Certificate

Prepare for a career as a Data Engineer.

Gain the in-demand skills and hands-on experience to get job-ready in less than 5 months. No prior experience required.

Instructors: Muhammad Yahya

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173,402 already enrolled

12 course series

Earn a career credential that demonstrates your expertise

from 62,102 reviews of courses in this program

Beginner level

Recommended experience

Flexible schedule

5 months at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

12 course series

Earn a career credential that demonstrates your expertise

from 62,102 reviews of courses in this program

Beginner level

Recommended experience

Flexible schedule

5 months at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

What you'll learn

Master the most up-to-date practical skills and knowledge data engineers use in their daily roles
Learn to create, design, & manage relational databases & apply database administration (DBA) concepts to RDBMSs such as MySQL, PostgreSQL, & IBM Db2
Develop working knowledge of NoSQL & Big Data using MongoDB, Cassandra, Cloudant, Hadoop, Apache Spark, Spark SQL, Spark ML, and Spark Streaming
Implement ETL & Data Pipelines with Bash, Airflow & Kafka; architect, populate, deploy Data Warehouses; create BI reports & interactive dashboards

Details to know

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Taught in English

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Prepare for a career in Data Engineering

Receive professional-level training from IBM
Demonstrate your proficiency in portfolio-ready projects
Earn an employer-recognized certificate from IBM
Qualify for in-demand job titles: Database Engineer, Data Engineer, Junior Data Engineer

$132,000+

median U.S. salary for Data Engineering¹

59,000+

U.S. job openings in Data Engineering¹

Professional Certificate - 12 course series

Prepare for a career in the high-growth field of data engineering. In this program, you’ll learn in-demand skills like Python, SQL, and Databases to get job-ready in less than 5 months.

Data engineering is building systems to gather data, process and organize raw data into usable information, and manage data. The work data engineers do provides the foundational information that data scientists and business intelligence (BI) analysts use to make recommendations and decisions.

This program will teach you the foundational data engineering skills employers are seeking for entry level data engineering roles, including Python, one of the most widely used programming languages. You’ll also master SQL, RDBMS, ETL, Data Warehousing, NoSQL, Big Data, and Spark with hands-on labs and projects.

You’ll learn to use Python programming language and Linux/UNIX shell scripts to extract, transform and load (ETL) data. You’ll also work with Relational Databases (RDBMS) and query data using SQL statements and use NoSQL databases as well as unstructured data.

When you complete the full program, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. You’ll also earn an IBM Digital badge and will gain access to career resources to help you in your job search, including mock interviews and resume support.

This program is ACE® recommended—when you complete, you can earn up to 12 college credits.

Applied Learning Project

Throughout this Professional Certificate, you will complete hands-on labs and projects to help you gain practical experience with Python, SQL, relational databases, NoSQL databases, Apache Spark, building data pipelines, managing databases, and working with data warehouses.

Projects:

Design a relational database to help a coffee franchise improve operations.
Use SQL to query census, crime, and school demographic data sets.
Write a Bash shell script on Linux that backups changed files.
Set up, test, and optimize a data platform that contains MySQL, PostgreSQL, and IBM Db2 databases.
Analyze road traffic data to perform ETL and create a pipeline using Airflow and Kafka.
Design and implement a data warehouse for a solid-waste management company.
Move, query, and analyze data in MongoDB, Cassandra, and Cloudant NoSQL databases.
Train a machine learning model by creating an Apache Spark application.
Design, deploy, and manage an end-to-end data engineering platform.

Introduction to Data Engineering

Course 1, 14 hours

What you'll learn

List basic skills required for an entry-level data engineering role.
Discuss various stages and concepts in the data engineering lifecycle.
Describe data engineering technologies such as Relational Databases, NoSQL Data Stores, and Big Data Engines.
Summarize concepts in data security, governance, and compliance.

Python for Data Science, AI & Development

Course 2, 24 hours

What you'll learn

Develop a foundational understanding of Python programming by learning basic syntax, data types, expressions, variables, and string operations.
Apply Python programming logic using data structures, conditions and branching, loops, functions, exception handling, objects, and classes.
Demonstrate proficiency in using Python libraries such as Pandas and Numpy and developing code using Jupyter Notebooks.
Access and extract web-based data by working with REST APIs using requests and performing web scraping with BeautifulSoup.

Python Project for Data Engineering

Course 3, 10 hours

What you'll learn

Demonstrate your skills in Python for working with and manipulating data
Implement webscraping and use APIs to extract data with Python
Play the role of a Data Engineer working on a real project to extract, transform, and load data
Use Jupyter notebooks and IDEs to complete your project

Introduction to Relational Databases (RDBMS)

Course 4, 16 hours

What you'll learn

Describe data, databases, relational databases, and cloud databases.
Describe information and data models, relational databases, and relational model concepts (including schemas and tables).
Explain an Entity Relationship Diagram and design a relational database for a specific use case.
Develop a working knowledge of popular DBMSes including MySQL, PostgreSQL, and IBM DB2

Databases and SQL for Data Science with Python

Course 5, 18 hours

What you'll learn

Analyze data within a database using SQL and Python.
Create a relational database and work with multiple tables using DDL commands.
Construct basic to intermediate level SQL queries using DML commands.
Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.

Hands-on Introduction to Linux Commands and Shell Scripting

Course 6, 17 hours

What you'll learn

Describe the Linux architecture and common Linux distributions and update and install software on a Linux system.
Perform common informational, file, content, navigational, compression, and networking commands in Bash shell.
Develop shell scripts using Linux commands, environment variables, pipes, and filters.
Schedule cron jobs in Linux with crontab and explain the cron syntax.

Relational Database Administration (DBA)

Course 7, 21 hours

What you'll learn

Create, query, and configure databases and access and build system objects such as tables.
Perform basic database management including backing up and restoring databases as well as managing user roles and permissions.
Monitor and optimize important aspects of database performance.
Troubleshoot database issues such as connectivity, login, and configuration and automate functions such as reports, notifications, and alerts.

ETL and Data Pipelines with Shell, Airflow and Kafka

Course 8, 18 hours

What you'll learn

Describe and contrast Extract, Transform, Load (ETL) processes and Extract, Load, Transform (ELT) processes.
Explain batch vs concurrent modes of execution.
Implement ETL workflow through bash and Python functions.
Describe data pipeline components, processes, tools, and technologies.

Data Warehouse Fundamentals

Course 9, 16 hours

What you'll learn

Job-ready data warehousing skills in just 6 weeks, supported by practical experience and an IBM credential.
Design and populate a data warehouse, and model and query data using CUBE, ROLLUP, and materialized views.
Identify popular data analytics and business intelligence tools and vendors and create data visualizations using IBM Cognos Analytics.
How to design and load data into a data warehouse, write aggregation queries, create materialized query tables, and create an analytics dashboard.

Introduction to NoSQL Databases

Course 10, 18 hours

What you'll learn

Differentiate among the four main categories of NoSQL repositories.
Describe the characteristics, features, benefits, limitations, and applications of the more popular Big Data processing tools.
Perform common tasks using MongoDB tasks including create, read, update, and delete (CRUD) operations.
Execute keyspace, table, and CRUD operations in Cassandra.

Introduction to Big Data with Spark and Hadoop

Course 11, 20 hours

What you'll learn

Explain the impact of big data, including use cases, tools, and processing methods.
Describe Apache Hadoop architecture, ecosystem, practices, and user-related applications, including Hive, HDFS, HBase, Spark, and MapReduce.
Apply Spark programming basics, including parallel programming basics for DataFrames, data sets, and Spark SQL.
Use Spark’s RDDs and data sets, optimize Spark SQL using Catalyst and Tungsten, and use Spark’s development and runtime environment options.

Data Engineering Capstone Project

Course 12, 18 hours

What you'll learn

Demonstrate proficiency in skills required for an entry-level data engineering role.
Design and implement various concepts and components in the data engineering lifecycle such as data repositories.
Showcase working knowledge with relational databases, NoSQL data stores, big data engines, data warehouses, and data pipelines.
Apply skills in Linux shell scripting, SQL, and Python programming languages to Data Engineering problems.

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Build toward a degree

When you complete this Professional Certificate, you may be able to have your learning recognized for credit if you are admitted and enroll in one of the following online degree programs.¹

This Professional Certificate has ACE® recommendation. It is eligible for college credit at participating U.S. colleges and universities. Note: The decision to accept specific credit recommendations is up to each institution.

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