With 2024 upon us and thoughts of new directions and changes upper most in our minds, we thought it would be a good time to think about what skill sets you might need if you want to move into programming in general, data science or AI in 2024.
With 2024 upon us and thoughts of new directions and changes uppermost in our minds, we thought it would be a good time to think about what skill sets you might need if you want to move into programming, data science or AI in 2024. Here's our top 5 new skills round-up and some suggestions for technologies to support your endeavours.
Skill 1: Programming
At the heart of almost all computer science resides programming, whether you want to write a DevOps script to provision a new server, implement a function to perform some mathematical calculation or use an AI algorithm to create a learning system, you are likely to need to write some form of program.
but one of the best in terms of ease of understanding and speed of learning is Python. The fact that this is one of the key languages used for data analytics tasks as well as many other types of application is just a bonus. There are many introductory Python courses aimed at both non-programmers and existing programmers and this skill will serve you well in many areas.
Skill 2: Databases
Data is at the heart of almost every application. Whether that data represents information on sales, accounts, the operation of a factory or the management of a heating system – data is key. In the vast majority of cases that data is stored in a database. It is therefore extremely useful to understand databases, whether you are looking at working in the data analytics arena, or developing data-driven applications.
There are essentially two types of databases in common usage today, they are relational databases and non-relational databases.
If you are considering relational databases, then understanding how the MySQL database works will give a good introduction to and grounding in general relational database technology. MySQL is a free open-source database and thus it is widely used and easy to access. Commercial relational databases include Oracle and Microsoft’s SQL Server.
If you are looking at relational database technologies, then you should also explore relational database modelling, and Structured Query Language (SQL) which is used to Create, Read, Update, and Delete database records (amusingly referred to as CRUD operations).
Non-Relational databases (aka NoSQL databases) represent database technologies that are based on non-tabular database formats (rows and columns); these can include hierarchical databases, document stores, key-value stores, graph databases and columnar (or column oriented) data stores.
The advantage is that an appropriate database format can be selected for the type of application at hand. They are also useful when the data involved in relatively unstructured or is complex with diverse structures present. A very widely used non-relational database is MongoDB and thus understanding this system can provide a useful introduction to non-relational database concepts.
Skill 3: Data Visualization
It is one thing to store and analyse data it is another to present it in a way that it can be consumed, often by non-technical individuals. This is where the area of data visualization comes in. There are essentially two ways in which data might be visualized one is using a library suitable for use by the programming language you are working with. For example, Matplotlib for the Python language is such a library. This library can be used to generate anything from a simple line graph to complex heat maps and 3D plots. It is easy to use for simple displays but powerful enough to be able to support far more complex and detail visualizations.
The second approach is to use a toolkit or application such as Tableau or Power BI. Both of these tools can be accessed from external environments, such as from a Python Program, and are often used to create powerful dashboards that can present complex information in an easily digestible format.
Becoming familiar with both a library such as Matplotlib, and a tool such as Tableau, will be of significant benefit within many business application domains.
Skill 4: Machine Learning and AI
There are two aspects to Machine Learning and AI, the first is the set of concepts that underly this area. The concepts are important as they help any developer understand which algorithm or tool to use in difference situations. They also help the developer to understand the trade-offs being made between one approach and another.
The second aspect is the avail; able libraries that already provide implementations of these concepts and allow a programmer to make use of them in an efficient manner. The libraries tend to be language specific and within the Python world the three libraries most commonly used are PyTorch, TensorFlow
and SciKitLearn. Any one of these libraries is a great starting point, allowing you to explore applying ML and AI to a range of applications. Combine this with the Kaggle repository of datasets and tasks anyone can very quickly get up to speed with the Data Analytics side of ML and AI.
Much software is now run in the cloud including machine learning and AI systems, data analytics and financial systems. Therefore, becoming familiar with cloud computing platforms such as AWS, AZURE and Google Cloud is very useful and should be part of every developer’s toolkit. Of course, you may not need to be familiar with all of the big three cloud computing platforms, but familiarity with at least one of these platforms will set you up in good stead.
If 2023 was anything to go by, then 2024 will be an exciting year for the technology world; these five skill sets will provide a sound basis for anyone wanting to move into or develop their existing skills so that they can take advantage of their future potential.
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