Welcome to Introduction to the Data Engineering Professional Certificate. After watching this video, you will be able to: Define a professional certificate. Describe the IBM Data Engineering Professional Certificate courses, and explain other important information about the courses. While you take the Career Guidance and Interview Preparation course, you may wonder about your skills and if they’re strong enough for a competitive job market. Whether you’re looking to start a new career or change your current one, professional certificates can help you become job ready. They enable you to build specific skills related to your chosen profession, making you more valuable as a prospective employee. Because they’re more focused and less extensive than a traditional college degree, they can be completed more quickly, can be more affordable, and are accessible to many learners. Moreover, you can often learn at your own pace, whenever and wherever it’s most convenient for you. The IBM Data Engineering Professional Certificate program offered on Coursera consists of 13 online courses that will provide you with the latest job-ready tools and skills. You’ll learn data engineering through hands-on practice in the IBM Cloud using real data science tools and real-world data sets. This Professional Certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs in the IBM Cloud that will give you practical skills. After you finish the course, you will earn a professional certificate from Coursera, and you'll also receive a digital badge from IBM recognizing your proficiency in data science. Let’s take a quick look at the courses in the IBM Data Engineering Professional Certification program. The program begins with Introduction to Data Engineering, an overview of the core concepts, processes, and tools you need to know for a foundation in the field. You will gain an understanding of the modern data ecosystem and the role Data Engineers, Data Scientists, and Data Analysts play in this ecosystem. Python for Data Science, AI, and Development is a beginner-friendly introduction to programming, specifically using the Python language. Python is one of the most popular programming languages and an essential tool for modern data scientists. An additional mini-course, Python Project for Data Engineering, will lead you through a project where you can demonstrate and solidify your Python skills. In Introduction to Relational Databases, you’ll learn the essential concepts behind relational databases and relational database management systems. You’ll study relational data models and discover how they are created and their benefits. In Databases and SQL for Data Science with Python, you’ll acquire a working knowledge of database fundamentals and some basics of SQL, a powerful language used for working with databases. Hands-on Introduction to Linux Commands and Shell Scripting provides a practical introduction to Linux and its commonly used commands. You’ll learn the basics of Bash shell scripting to automate various tasks. Relational Database Administration will show you some of the activities, techniques, and best practices for managing a database. You will learn about configuring, maintaining, and upgrading database server software as well as database security and how to optimize databases for performance. After taking ETL and Data Pipelines with Shell, Airflow, and Kafka, you’ll be able to describe different approaches to converting raw data into analytics-ready data that is credible, contextual, and accessible to data users. You’ll learn about extracting the data, merging extracted data, and importing data into data repositories. In Getting Started with Data Warehousing and BI Analytics, you’ll learn why a data warehouse is one of the most fundamental business intelligence tools in use today. You’ll be able to describe different kinds of repositories, including data marts, data lakes, and data reservoirs, and explain their functions and uses. With Introduction to NoSQL Databases, you’ll learn the history and the basics of NoSQL databases and discover their key characteristics and benefits. You’ll learn about the four categories of NoSQL databases and how they differ from each other, as well as exploring the architecture and features of several different implementations of NoSQL databases. Introduction to Big Data with Spark and Hadoop will cover the characteristics of Big Data and its application in Big Data Analytics. You’ll also learn about the features, benefits, limitations, and applications of some of the Big Data processing tools. In Data Engineering and Machine Learning Using Spark, you’ll learn how to work with Apache Spark for data engineering and machine learning applications. You’ll work with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load tasks, as well as regression, classification, and clustering. Finally, you’ll complete the Data Engineering Capstone, in which you’ll step into the role of a junior data engineer. You’ll use the knowledge and skills acquired in the previous courses to demonstrate what you can do when presented with a real-world use case. A few more points about the program: Anyone with a passion for learning can take this professional certificate program — no prior knowledge of computer science or programming languages is required. This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings, and assignments anytime and anywhere via the web or your mobile device. The professional certificate requires the completion of 13 courses. Each course typically contains three to six modules that take an average of two to four hours each to complete. If you work on them at a part-time pace of about one module per week, you can complete the certificate in about a year. If you work full-time at a pace of one module per day, you can complete the certificate in three to four months. If you have completed some of the courses in other programs or individually, any courses you have already completed will count toward finishing this professional certification. You do not have to take those courses again and will be able to finish more quickly. In this video, you learned that: The IBM Data Engineering Professional Certificate program can help you build career-related skills more accessibly and affordably than a degree. The program contains thirteen courses that cover a variety of data engineering topics, And the courses can be taken completely online and completed in a few months, depending on how much time you spend on them. To find out more and sign up for the program on Coursera, use the included link.
