Programming for Data Science with Python
Overview
According to the 2020 Emerging Jobs Report Australia by LinkedIn, data scientists and engineers ranked #7 and #8 in the top 15 emerging jobs, indicating that as industries evolve, the adoption of new technologies is opening up a wealth of demand for new talent.
If you’re looking to seize the opportunity and future-proof your career, our Programming for Data Science with Python may be the course for you. Created with collaborative data science company Mode, you’ll cover fundamental programming skills used in many data analysis and data science roles, including Python, SQL, Terminal, and Git.
Our Programming for Data Science with Python course will be delivered to you in partnership with Udacity, meaning you'll have access to both Udacity's learning and career services as RMIT Online's course enablement support through our Learner Success team.
Recognition
After completing an RMIT Future Skills course, you will earn an RMIT certificate and credential which can be validated, recognised and shared on social media platforms.
Content
This course equips you with fundamental tools used by data scientists around the world: R or Python coding, SQL, command line and Git. By the end of this course, you’ll gain an understanding of:
- Python coding
- SQL
- Command Line
- Git
- Version Control
- Numpy
- Pandas
- JOINs
- Aggregations
- Data visualisation
Whether you’re new to data or an experienced analyst, this is your chance to upskill. Fast.
How does it work ?
By the end of this course, you’ll walk away with an RMIT credential which can be validated, recognised, and shared on social media platforms. Throughout this course you’ll also work on three projects:
- Investigating a relational database while working on PostgreSQL. You’ll complete the entire data analysis process, starting by posing a question, running appropriate SQL queries to answer your question, and sharing your findings.
- You will use Python to answer interesting questions through data collected. You’ll write code to collect the data, compute descriptive statistics, and create an interactive experience in the terminal that presents the answers to your questions.
- On GitHub, you’ll post different versions of a Jupyter Notebook capturing your learnings from the course, and add comments to your project Git repository.
