I made the switch to coding later in life. Here’s what I’ve learnt.

It’s never too late to learn to code. The pay offs for learning to code are huge, and the best time to learn is right now. I learnt to code quite late in life. Here are my tips for those ready to take the plunge.

Tip 1: Use an installation free environment to get started

One of the trickiest things in coding is, unfortunately, also one of the first things you have to do for many learning methods. To begin coding, you need a coding environment. Installing R and linking it to your local CRAN host is not always straightforward. I had a colleague who had a PhD in computing, and still ended up spending a whole afternoon trying to resolve incompatibility issues when he installed Anaconda on his laptop.

Our learning environment in Datacamp is installation free and gets round these early barriers. Everything is done in an online simulated environment. Then, when you have a little more confidence, you can install other environments on your laptop yourself.

Tip 2: Getting it wrong IS coding successfully

If you come from a subject like Business Studies, you’ll be used to planning a new project, executing the plan and then moving on to the next project. The nature of coding is fundamentally different. You write something, it fails, you write it again, it fails again …. etc.. From the perspective of a business studies student, the whole thing can feel like one great big experience of failure.

The cyclical nature of coding is coding! It can look like a lot of failures, but as long as the thing parses (goes through successfully) in the end, it’s a success.

The sooner we embrace this different version of learning and concepts of success, the easier it will be.

Tip 3: Rest assured – it will all get reused again!

It can be disheartening when you first start to code because there just seem to be so many different ways of structuring fundamentally similar things. Coding is one of those skills that require time investment at the beginning, but progress speeds up later on. Once you get past a certain tipping point, and you don’t have to go very far in coding lessons to get there, a huge amount of what you already know will be re-used again and again and again.

Have you just learnt the WHERE, ORDERBY and other indexing syntax in R? You’ll find the operators in SQL surprisingly familiar when you get to them. If you’ve just learnt those terms in SQL, then you’re going to have a strong sense of déjà vu when you take a look at graph SQL. Have you just learnt about dictionary structures in Python, after learning all about vectors and matrices and arrays and data frames? You might be feeling a bit fed up with yet another new storage structure, but by learning this one extra form, you’re going to find document data storage very intuitive, as well as JSON objects.

Code creators rarely re-invent the wheel and the fundamentals you learn in the earliest lessons will be reused.

Tip 4: Don’t be afraid to pause videos and work out what you’re looking at.

For example, if someone has just introduced a package or a function and an argument, and you don’t know what those are, pause the video, and take a moment to check. It will save time in the long run.

Packages are collections of functions, like a bag of functions. Functions are a set of commands, which we can access with just one word so we don’t need to write out lines and lines of code each time. They need parameters or brackets after them () to indicate the inputs for the function, like the data set that you would like to run the function over. You have to name the package that you’re using first, then the function, then the arguments e.g.

pd.DataFrame()

This will call Pandas package (pd) of functions and then the DataFrame function within that. We can set which file we want in a dataframe format by putting the file name in the (). Even if there are no arguments, the machine needs empty () to let it know that there are no inputs. Knowing what each part does can really help to make sense of some seemingly illogical processes, like writing empty ().

Tip 5: Check and double check your spelling and the question requirements

Often, the main reason for error is careless spelling or a missed comma here and there. Your first default reaction when you discover your code won’t work should be to check the instructions, and check your spelling for slips and typos. When I was following classes, I was often too keen to have a go, and I misread the instructions. Code that ‘worked perfectly’ was wrong because it didn’t answer the question.

Tip 6: Don’t be intimidated

This last point is an important one – you don’t have to be fluent to be able to ‘get by’. You don’t have to write code that goes on for thousands of lines to be a coder. Many of the images we draw or the analyses that we carry out can be written in a 10 line program. There are plenty of useful things you can do with just 2 or 3 lines.

Focus on what you can do, not what you can’t. It all counts as coding!

And while, without a doubt, the coding community compete fiercely with each other on intelligence and ability, I’ve only ever found the community welcoming and supportive to newcomers to the field!

Don’t forget! You can log on to ALL our courses easily from your mobile. Find our coding lessons here:

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Datacamp - Learning Tracks

All IoA members can use the installation-free Data Camp environments to build, practice and test your skills in Data Camp. We have two custom built tracks to allow you to ensure your training is on course to fulfil your career goals. We’ve recommended two tracks of knowledge and analytics study aligned to all of the 7 first years in the Data Competency Framework.

Which Track is for me?

Business analyst with R: This track will take you through spreadsheet skills and BI tools in the early years, and build up your coding skills to use R environments in the later years with more challenging data projects.

Python analyst: This track goes straight into Python coding and will take you all the way to working with unstructured data and deep learning techniques.
Look for the track name and year when you search for a course.
With our custom tracks, we’ve selected the skills that we know employers are looking for but remember that you can also take any of the 300 courses and assessments and projects any time you want and add that to your CPD records, too. You can find a post discussing the aims and structure of the tracks here.

Datacamp - Learning Tracks

All IoA members can use the installation-free Data Camp environments to build, practice and test your skills in Data Camp. We have two custom built tracks to allow you to ensure your training is on course to fulfil your career goals. We’ve recommended two tracks of knowledge and analytics study aligned to all of the 7 first years in the Data Competency Framework.

Which Track is for me?

Business analyst with R: This track will take you through spreadsheet skills and BI tools in the early years, and build up your coding skills to use R environments in the later years with more challenging data projects.

Python analyst: This track goes straight into Python coding and will take you all the way to working with unstructured data and deep learning techniques.

Look for the track name and year when you search for a course.

With our custom tracks, we’ve selected the skills that we know employers are looking for but remember that you can also take any of the 300 courses and assessments and projects any time you want and add that to your CPD records, too. You can find a post discussing the aims and structure of the tracks here


View Learning Tracks


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