What is the course about?
The analytical workflow is a dialogue between intuition and calculation. Our intuitive understanding of data can be greatly enhanced and developed by visual tools, so this course develops the two skills in tandem.
We will look at methods of exploratory data analysis, calculating simple measures such as correlations and regressions using different views of the data to guide us.
We then form a hypothetical model and test the hypothesis using tools that are more rigorous. When our results are in, we will then be concerned with ways to present our findings to a variety of audiences.
This is a live online course. You will need:
- Internet connection. The classes work best with Chrome.
- A computer with microphone and camera.
We will contact you with joining instructions before your course starts.
What will we cover?
• The data analytic workflow, from initial exploration to a final model
• Regression, correlation and distribution-fitting
• Some basic elements of ANOVA
• Hypothesis testing, cross-validation and p-values
• Considerations when presenting the results of data analysis
• Use of appropriate Python libraries, including scipy, scikit-learn and matplotlib.
What will I achieve?
By the end of this course you should be able to...
• Describe a standard analytic workflow
• Use Python libraries to perform a common range of statistical tests and measures
• Calculate correlation coefficients, regression lines and p-values
• Describe good and bad practice in data analysis and presentation
• Produce various kinds of chart from your data.
What level is the course and do I need any particular skills?
This is an advanced course. You should already be confident with basic Python programming at the level of our Introduction to Python course, and you should have used the basic features of Numpy and Pandas – Introduction to Data Analytics with Python is ideal preparation for this.
You do not need any prior knowledge of statistics or analytical methods.
How will I be taught, and will there be any work outside the class?
There will be some theoretical underpinning to the course, but it is nearly all practical, through demonstrations and problem solving activities.
Are there any other costs? Is there anything I need to bring?
There are no additional costs. A pen and paper to take notes.
When I've finished, what course can I do next?
Advanced Data Analytics: importing and cleaning is designed to accompany this course and rounds out this programme. You might want to explore the Excel courses in data analysis such as: Data analysis with Power BI, Introduction to DAX: data analysis expression for Power BI or you might find beneficial to attend one of our maths courses in Probability and statistics for Data Analysis.