Considering a career change?
Killian McAleese, from data training experts CodeClan, shares
the top 8 skills that employers require for data analyst roles.
1. R Programming
R is a programming language adopted as an industry standard for
data analysis and data science. For managing and analysing data, it
has a steeper learning curve than a regular spreadsheet.
2. SQL
When working with data, you'll need to communicate with a
database, and that's what SQL (or Structured Query Language) is
for. It's widely used and one of the most common data skills
you'll find in job listings.
Understanding SQL will allow you to access and manipulate
databases, extract data using queries, join tables together,
connect databases and loads more.
3. Python
Python has recently become the world's most popular
programming language, thanks to its relative ease of use and
its versatility. Python can be used for tasks as diverse as
building a web application, analysing data and machine
learning.
4. Data Cleaning
Data cleaning is an essential and invaluable skillset.
Examples of 'dirty data' include inconsistent fields, formats
and duplicates which can make input inaccurate. Data cleaning is
the process of fixing all that, so that data can easily be
analysed. Unfortunately, it's not quite as simple as checking
through a spreadsheet, as sometimes datasets have literally
millions of rows.
5. Data Visualisation
Data Visualisation is the point where data gets truly powerful,
with the ability to convey simple, impactful messages and tell
stories with potentially millions of bits of information via graphs
or charts or something more creative. This is where data influences
people and drives decisions.
6. Presentation skills
Visualisation often goes hand-in-hand with presentation skills
when it comes to data, as both involve explanation, making a point,
telling a story or ultimately driving a decision.
As a data scientist or data analyst, you may understand the data
inside-out, but if you're going to use it to influence decision
makers, you'll need to be able to present it convincingly.
7. Probability and statistics
Probability is essentially the mathematics of chance. Statistics
is the mathematically based field in which data is traditionally
studied.
The sort of maths you need for working with these as a data
analyst or data scientist is fairly practical and applicable to the
real world. You may need to dust it off a little but it's not more
difficult, for example, than learning a programming language.
8. Machine learning
Machine learning is basically the way in which we programme
software to make better predictions, based on available data.
It sounds complicated (and sometimes, it is) but it's perfectly
learnable, through topics like correlation, linear regression,
decision trees and clustering.
Interested in learning more? Visit the CodeClan website
where you can read more blogs by professionals from the
data sector and learn more about training courses.
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