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!

Data Analytics 101 Part 2 of 5: Data Wrangling

This is the next part of the series after the first article. Before diving into analysis, it is often necessary to clean and transform the source data before any meaningful analysis can be done. This can be viewed as six activities: discover, structure, clean, enrich, validate and publish.

a) Importing Data

• DataCamp tutorial and cheat sheets on how to import data using Python and pandas.

b) Data Management

• Towards Data Science article examining the append function in pandas: Access this link.
• DataCamp provides videos examining merges and joins in more detail.
• DataCamp blog post on joining data.
• A breakdown of the different types of joins with further examples.

c) Identifying Data Quality Errors

• Towards Data Science article discussing the how pandas can be used to cleanse data including handling duplicates, missing data and additional information on outliers.
• How a command in Python works and the parameters it takes. Use this link to access information on the duplicated function.

Do you think cleaning data is important? Let me know your thoughts in the comments below.

Data Analytics 101 Part 1 of 5: Business Context

Read a great publication on the concept of Data Governance (meet the author here) and was inspired to share learnings from the ICAEW course on Data Analytics. Before delving into analytics per se, we first need to understand the context within which analytics occurs. There are several useful resources that are available publicly.

a) Data analysis as a concept and process

• TowardsDataScience  article that describes what a data analyst actually does.
• Why analytics is important for accountants: read the ICAEW’s article on the matter.
• DataCamp blog detailing what is data science, why it is important and the skills analysts need.
• News website Medium presents a series of articles that gives a real-world example of an analytics project that demonstrates how to frame a business problem for analytics
  Framing the Business Problem - 1 of 3
  Framing the Business Problem - 2 of 3
  Framing the Business Problem - 3 of 3
• Career website Indeed detailed guide to writing a problem statement.
• Software company SAS on framing a business problem as an analytical solution.
• TowardsDataScience  article on the importance of checking your assumptions in analytical projects.

b) Organisational data and data quality

• DataCamp blog detailing what exactly is a relational database and SQL.

• TowardsDataScience explains everything you need to know about data management.

• Article from news website Medium on why data quality matters.

 

c) Roles and responsibilities around data analytics

• DataCamp has a useful infographic that breaks down the different jobs, roles and skills required in data and the differences between them.
• The blog Everyday Analytics on thinking like an analyst and the questions you need to ask.
• ICAEW article on the skills required for accountants to perform data analysis.
• Information on data governance can be found in this DataCamp blog post.

Hope you found the above resources useful in your continuous learning journey!

If you have more useful resources to share, please add a comment below.