Data Analytics 101 Part 3 of 5: Analysing the Data

This is the third part of the series (you may refer to the first and second articles if interested) To analyse data meaningfully, it is necessary to understand the different techniques available (and how/when to use them),  and produce data visualisations to communicate the messages and insights.

a) Data manipulation techniques

If you want to know more about any of the topics explored in the module, you can find further resources here:

• The pandas website provides detailed information on the various uses of functions within the pandas package for Python. Make a start by looking at the user guides under this link.
• Information on how pandas can be used to select data from a dataset and the drop command is available under this link..
• DataCamp provides useful cheat sheets including one covering data wrangling techniques examined in the previous unit and the manipulation techniques examined in this module.
• A useful summary of the different way to manipulate rows and columns is available under this link and covers selecting, adding and deleting.
• Access this link to find out more about aggregating data using the groupby function, including a list of the aggregation functions.

b) Summary statistics in exploratory data analysis

If you want to know more about any of the topics explored in the module, you can find further resources here:

• A deeper look at summary statistics within Python can be found in this article from Real Python.
• TowardsDataScience provides a useful article that explains hypothesis testing in further detail under this link.
• Further documentation on the t-test commands in the SciPy package: scipy.stats.ttest_1samp and scipy.stats.ttest_ind.

c) Basic visualisations in exploratory data analysis

If you want to know more about any of the topics explored in the module, you can find further resources here:

• DataCamp provides a useful cheat sheet that summarises the main visualisation commands in the Seaborn Package.
• TowardsDataScience examines the different data visualisations with the corresponding code and a breakdown of what visualisation to use when in the article under this link.
• Access this TowardDataScience article for a walkthrough of performing exploratory data analysis including summary statistics and visualisations.

Any thoughts on data analysis? Leave your insights in the comments below!