![Data Project Fundamentals Skills and Techniques for Success]()
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 46 lectures (5h 17m) | Size: 1.34 GBA hands-on guide to data projects from start to finish and beyond! Data profiling, modelling, unit testing and more!
Gain a comprehensive overview of the skills, thought processes and complexities that accompany a data-related task or project
Discover the benefits of data profiling and unit testing
Learn the techniques, challenges and approaches undertaken in each of the logical stages of a data project
Learn the fundamentals of Structured Query Language with easy-to-follow examples and exercises
Learn how applying the techniques in each phase of a data project leads to more robust, successful and maintainable solutions!
Work with the
course data using Jupyter Notebooks to back up the course concepts
Appreciate the fundamentals of data modelling including Dimensional Modelling and Entity Relationship Diagrams
RequirementsAn understanding of 2-dimensional data structures and basic programming constructs, ideally some python, but this isn't essential
A willingness to challenge existing thought processes and ways of working!
DescriptionThis course aims to highlight the key skills that will make you stand out as a rounded Data Professional. You will cover the techniques, challenges and approaches undertaken in each of the logical stages of a data project. You will cover Data Profiling; Data Mapping and Transformation; Development; Data Modelling; Data Solution Design; Software Design; Unit Testing and Troubleshooting. The course aims to follow the natural life-cycle of a project.
These topics will be demonstrated by a mixture of lectures, screencasts, quizzes and exercises. Practical retail data will bring it all to life with Python and Jupyter Notebooks used to illustrate the techniques. Although Jupyter notebooks are used, a running them in the course is that it is not important how certain techniques are done, but that these techniques are understood and why they form a key part of the process. So although the hands-on sections will definitely enhance the course experience they are not a prerequisite, and could also be tackled using alternative software.
Ultimately this course aims to fill the gaps between courses focused on specific programming, data analysis skills or data modelling skills. It should allow you to appreciate but also critically question your current data environment. I believe this course offers something different and will make you stand back and think before heading full-steam into a data-related task. This more complete understanding of Data Projects will make you a better, more rounded Data Professional. Enjoy!
Who this course is forProfessionals in the early stages of a data career as a data engineer, data scientist or data analyst
Business leaders, business analysts and project managers looking for a fuller, more rounded appreciation of data projects
Software Engineers looking to gain an end-to-end overview of the life-cycle of a data project
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