This fast-paced practical course focuses on solving challenges presented by data science in a manner that is simple to conceptualize and easy to implement. This is achieved by leveraging Python libraries that offer abstractions to complicated underlying algorithms. The Jupyter Notebook acts as an add-on tool – a virtual playground – that allows you to create and share live code, equations, visualizations, and text.
What you will Learn
- Identify areas of investigation within a data set
- Develop a plan for doing data science
- Define exploratory analysis
- Prepare data for modeling
- Implement predictive analytics
- Collect data with web scraping
- Explore various data visualization techniques
Who should attend
If you’re a Python programmer stepping out into the hugely popular world of data science, opting for this course is the right-way to get started.
For the best experience in this course, you should have knowledge of programming fundamentals and some experience with Python. In particular, having some familiarity with the Python libraries Pandas, Matplotlib, and scikit-learn is useful.