Overview
Learn how Python makes it easy to gain valuable insight from your data using a combination of data wrangling and data visualisation.
Learning outcomes
Attendees will be able to:
- import data from various formats, including delimited text and Excel
- overcome a range of common data importing issues
- manipulate data in a DataFrame
- visualise data with different types of chart
What you’ll need
- Laptop and headphones and microphone
- Microsoft Office (Word, PowerPoint, Excel)
- Google Chrome or Microsoft Edge
- We will provide browser-based tools for Programming Python (nothing to install)
Course topics
- Getting Started with JupyterLab
- You should already be familiar with the basics of Python before beginning this course, so we’ll jump right in and create your first Jupyter Notebook, as well as how to annotate it with Markdown and LaTeX
- Overview of prerequisite skills
- We’ll start by giving an overview of the key Python concepts and skills that we’ll be building on, e.g. slicing Lists
- Generators
- Learn why Data Scientists find generators useful and efficient for generating sequences of numbers
- Lambdas
- Lambdas are also known as “anonymous functions”. They are often used by Data Scientists as a shorthand way to create a function
- NumPy
- NumPy, short for Numerical Python, is one of the building blocks of scientific computing in Python. You could easily run a course on NumPy on its own, so this is just a crash course.
- Introduction to Pandas
- Import data into a Pandas DataFrame and begin analysing the data
- Read and write files using Python and Pandas
- Error handling in Python
- Data Wrangling with Python
- Learn how to overcome a range of common problems with incomplete and challenging datasets
- Data Munging / Data Cleansing - range of import/export problems
- Manipulating data with Pandas DataFrames
- Creating simple graphs using Pandas plot
- Time-series Data
- Python is exceptionally good at manipulating and analysing time-series data. We’ll overcome a range of challenges that would be difficult in other languages
- Visualising data with Python
- Look for trends and stories in the data by visualising data using Matplotlib
- Connecting to other data sources
- Learn how to import data from a variety of data sources including SQL and NoSQL databases, and APIs
- Limitations of Pandas
- Consider the limitations of Pandas and Jupyter Notebooks and consider alternatives such as Apache Spark for working with Big Data
- Next steps
- Discussion regarding how to continue developing your skills as a data scientist. We’ll give attendees an overview of what’s possible with other graphing libraries, as well as an overview of Machine Learning with scikit-learn