Our top eight skills for a career in data

A woman sits in from of a desktop computer with her hands on a keyboard. She also has a laptop and papers, as well as a mug. She looks productive.

There is currently a significant shortage of people with the right skills to work with data. But what exactly are these skills? We’ve listed our top eight in this post.

By the way, you can learn ALL of these skills on our Professional Data Analysis course.

1. R Programming

R is a programming language that has been 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. However, with a little learning and practice, you’ll be more than capable of mastering the various functions of R, which will hugely extend what is possible in spreadsheets.

2. SQL

Working with data? Then you’ll need to communicate with a database, and that’s what SQL (or, Structured Query Language) is for. It’s widely used and is 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.

It’s that versatility that means Python can be used for tasks as diverse as building a web application, analysing data and machine learning.

4. Data Cleaning

Eurgh… that dirty data is everywhere! From inconsistent fields, formats and duplicates to incomplete and outdated information and stuff that’s just inaccurate, ALL data professionals need to deal with dirty data at some stage in their careers. Many deal with it every day.

Data cleaning is the process of fixing all this stuff so that your 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.

However, that just means that data cleaning is an essential and invaluable skillset.

5. Data Visualisation

Data visualisation is just what it sounds like – making graphic representations of information. We’re typically talking about graphs and charts, but it can get quite creative.

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. This is where data influences people and drives decisions.

Visualisations can be created with the simplest spreadsheets, but using technologies like R and the package ggplot2, Tableau or Python and Seaborn, allow the visualisation of much larger data sets, in potentially more complex plots.

6. Presentation skills

Visualisation often goes hand-in-hand with presentation skills when it comes to data. After all, both of these activities 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

Don’t panic! You don’t need a white coat and a PhD to understand statistics and probability. Probability is essentially the mathematics of chance, and statistics is the mathematically based field in which data is traditionally studied.

Uh-oh… we just said mathematics twice! 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

We’re moving into the realm of buzz words but with good reason. Machine learning is basically the way in which we program 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. If you don’t know what these are, don’t panic! You just haven’t learned them yet.

It’s a great time to get into data…

From demand, to salary and career flexibility, there are many reasons why it’s a great time to change career or upskill into data. CodeClan’s Professional Data Analysis course will cover all of the skills discussed above. Need something more bitesized? Why not join one of our FREE Coding for Data workshops.

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