Brief Introduction
An opportunity to apply your foundational Python skills via a project, using various techniques to collect and work with data
Description
Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!
Journey into the realm of becoming a Data Engineer and apply your basic Python knowledge of working with data. You will exercise various techniques in Python to extract data in multiple file formats from different sources, transform it into specific datatypes, and then prepare it for loading it into a database. You will perform these tasks with the help of multiple hands-on labs using Jupyter notebooks and IBM Watson Studio.
On completion of this course, you will have the confidence to employ Python for data engineering tasks such as extracting large data sets from multiple sources through the use of webscraping and APIs, transforming the data and making it ready for gaining valuable business insights.
NOTE: This course is not intended to teach you Python basics and has limited instructional content. Rather, it is intended for you to apply prior Python knowledge.
PRE-REQUISITE: The Python Basics for Data Science course from IBM is a pre-requisite for this project course. Before taking this course, please ensure that you have either completed the Python Basics for Data Science from IBM or have equivalent proficiency in working with Python and data.
Knowledge
- The objective of this course is to give you a solid understanding of what Data Engineering is.
- In this course you will apply your Python skills for:
- Webscraping and data extraction using APIs Transforming data into specific data types ****
- Logging operations and preparing data for loading
- Working with Jupyter notebooks and IBM Watson Studio
Applied Time Series Analysis and Forecasting with R Projects
- 4.4
-
![]()
![]()
![]()
![]()
![]()
Use R to work on real world time series analysis and forecasting examples. Applied data science with R. Welcome to the world of R and Time Series Analysis!...
Bluebeam in 3 Hours
- 4.3
-
![]()
![]()
![]()
![]()
![]()
Seize the PDF mark-up, organization, and collaboration potential of Bluebeam Bluebeam is a software for working with PDF documents in the architecture, engineering, and construction (AEC) industries....
CS50's Introduction to Computer Science
- 0.0
-
![]()
![]()
![]()
![]()
![]()
An introduction to the intellectual enterprises of computer science and the art of programming. An entry-level course taught by David J....
Six Sigma Tools for Improve and Control
- 4.8
-
![]()
![]()
![]()
![]()
![]()
This course is the final course in the Six Sigma Yellow Belt Specialization. This course will provide you with tools and techniques for improvement....
