Data science is becoming increasingly popular with more and more industries beginning to value its importance, while recent advancements in open-source software have made the discipline accessible to a wide range of people. Python is a popular choice for most data scientists, owing to its ease of use and versatile nature.
In this course, we show how Jupyter Notebooks can be used with Python for various data-science applications. Aside from being an ideal “virtual playground” for data exploration, Jupyter Notebooks are equally suitable for creating reproducible data processing pipelines, visualizations, and prediction models.
We will look at various data modelling concepts using Jupyter Notebooks, and we will see the full power of Jupyter Notebooks as we work through this course.
- Jupyter Fundamentals
- Data Cleaning and Advanced Modeling
- Web Scraping and Interactive Visualizations
- Machine learning classification strategy
- Exploratory data analysis and investigation
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 will be useful.
Knowledge of programming fundamentals and some experience with Python, including Python libraries, Pandas, Matplotlib, and scikit-learn.