In this mini bootcamp, you will learn how to use Python, which is one of the most powerful languages in the world of Data Science. The bootcamp covers important Python and Data Science libraries like Pandas, NumPy and SciPy, which you need to know to analyze and manipulate data. You will also be working through a Python application project.
You will learn Python for Data Science, Pandas, and NumPy over a 3 weeks period, with a live instructor and an interactive learning cohort.
You Will Learn
Use Python for Data Science including cleaning, preparing and transforming data.
Data visualisation with Matplotlib/ Seaborn
Statistical analysis with Scipy
Extensive use of the NumPy library
Extensive use of the Pandas library
Working with database connectors
Working through an example python application project
3 Reasons to Join the Bootcamp
1. Live Online Instruction
Learn directly from experienced instructors through live online learning sessions.
2. Live Sessions
Learn three evenings per week between 7pm and 9pm, available in multiple time zones.
3. Affordable & Flexible
One of the most affordable instructor-led online bootcamps, with flexible payment options.
1. Application and Enrollment:
Reserve your seat and enroll in the bootcamp by paying the bootcamp fee of only $495. The price includes the exam fee and 18 hours with live instructor.
2. Complete the Bootcamp:
After enrollment, you will be placed in a learning cohort with other students. You will go through the course material together and complete assignments and projects with the help of an experienced instructor.
Throughout the bootcamp, you will receive support from your cohort and the W3Schools team to help you grow your skill set.
3. Certification and Job Application:
Upon completing the bootcamp, you will get a Certificate of Completion for Python for Data Science and an extra acknowledgement that you were a part of the bootcamp. This certificate demonstrates that you completed the bootcamp and mastered the topics.
W3Schools is optimized for learning and training. Examples might be simplified to improve reading and learning.
Tutorials, references, and examples are constantly reviewed to avoid errors, but we cannot warrant full correctness