Data Science and Data Engineering for Architects
This course covers the theoretical and practical aspects of applying the principles and methods of Data Science and Data Engineering. Students are introduced to the relevant concepts, terminology, theory, and tools used in the field. This training course is complemented by a variety of hands-on exercises to help the attendees reinforce their theoretical knowledge of the material being studied.
This course is suitable for: Software Developers, Technical IT Managers, Data Engineers, Data Scientists.
Participants should have a working knowledge of Python or have strong programming experience with another language. Familiarity with core statistical concepts such as variance, correlation, etc. is helpful.
Register 21 days before class start date and save 10%! Enter discount code EARLY10 during registration.
Register 21 days before class start date and save $250! Enter discount code EARLY250 during registration.
- What is Data Science?
- What is Data Engineering?
- Distributed Computing Concepts
- Data Processing Phases
- Introduction to NumPy
- Introduction to pandas
- Data Grouping and Aggregation with pandas
- Descriptive Statistics Computing Features in Python
- Repairing and Normalizing Data
- Data Visualization with matplotlib
- Data Science and ML Algorithms
- Parallel Data Processing with PySpark
- Operational Data Analytics with Splunk
- Python as a Cloud Scripting Language
- Amazon SageMaker
- Introduction to AWS Glue
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.