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.