Here is the list of several data engineering courses to help professionals adopt the best skills

The role of professionals specializing in data is essential in both tech and non-tech organizations. Since they are professionals with knowledge in data science, mathematics, statistics, and computer science, every industry is utilizing their area of expertise to boost business growth and increase their revenue. Check out the article listing the top data engineering courses that will help you adopt the best skills. 

Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data-driven decisions has never been greater. The role of professionals specializing in data is essential in both tech and non-tech organizations. Since they are professionals with knowledge in data science, mathematics, statistics, and computer science, every industry is utilizing their area of expertise to boost business growth and increase their revenue. This article lists the top data engineering courses in Linkedin learning. 

Data Engineering Foundations

In this course, Harshit Tyagi explains the fundamentals of data engineering. He covers key topics like data wrangling, database schema, and developing ETL pipelines. He also details several data engineering tools like Hive, Hadoop, Spark, and Airflow. By the end of this course, it should be abundantly clear why the data engineer is one of the most valuable people in a data-driven organization.

Apache Spark Essential Training: Big Data Engineering

This course focuses on building full-fledged solutions that combine Apache Spark with other Big Data tools to create end-to-end data pipelines. Instructor Kumaran Ponnambalam begins by defining data engineering, its functions, and its concepts. Next, Kumaran goes over how Spark capabilities such as parallel processing, execution plans, state management options, and machine learning work with extract, transform, load (ETL). He introduces you to batch processing use cases and processes, as well as real-time processing pipelines. After walking you through several useful best practices, Kumaran concludes with an end-to-end exercise project.

GitHub for Data Scientists

In this course, learn how to get the most out of GitHub, not just as a code repository, but also as a resource for finding software and connecting with an engaged community. Review foundational GitHub concepts, from how GitHub works, to key terminology, to how GitHub facilitates collaboration for data science projects. Learn how to effectively use repositories in GitHub, including how to create and clone a repository and resolve common merge issues. Plus, learn how to create a strong data science portfolio with GitHub, contribute to open-source repositories, and more.

Data Cleaning in Python Essential Training

In this course, instructor Miki Tebeka explains why clean data is so important, what can cause errors, and how to detect, prevent, and fix errors to keep your data clean. Miki explains the types of errors that can occur in data, as well as missing values or bad values in the data. He goes over how human errors, machine-introduced errors, and design errors can find their way into your data, then shows you how to detect these errors. Miki dives into error prevention, with techniques like digital signatures, data pipelines and automation, and transactions. He concludes with ways you can fix errors, including renaming fields, fixing types, joining and splitting data, and more.

More Python Tips, Tricks, and Techniques for Data Science

In this course, instructor Harshit Tyagi shares practical tips and techniques that can help you enhance your Python data science workflow. Harshit covers how to work with IPython notebooks, including how to debug errors. He shows how to use NumPy to manipulate arrays, as well as how to work with pandas, the data manipulation and analysis tool. He provides tips for visualizing your data with Matplotlib, explaining how to add text to plots and annotate elements on a chart. Plus, get best practices for working with scikit-learn, as well as other machine learning tips.

R Data Science Code Challenges

In this Code Challenges course he presents short, bite-sized challenges you can use to practice R programming. Each video is less than four minutes and self-contained, so you can skip around and watch the videos in any order. Mark shares his solutions for every problem, most of which contain fewer than 10 lines of code. Whether you’re a new programmer looking to practice, or an experienced developer who wants to work on some challenges, this short course will give you a chance to sharpen your skills.

Data Science Foundations: Data Assessment for Predictive Modeling

This course introduces a systematic approach to the data understanding phase for predictive modeling. Instructor Keith McCormick teaches principles, guidelines, and tools, such as KNIME and R, to properly assess a data set for its suitability for machine learning. Discover how to collect data, describe data, explore data by running bivariate visualizations, and verify your data quality, as well as make the transition to the data preparation phase. The course includes case studies and best practices, as well as challenges and solution sets for enhanced knowledge retention. By the end, you should have the skills you need to pay proper attention to this vital phase of all successful data science projects.