Data Science is now one of the most desired skill sets in the world. A few days ago I was contacted by a NUST student looking for an attachment. He is studying Informatics with a focus on Data Analytics and Model Development. I had told him he made an excellent choice because his value is highly sought-after globally. Even those who are pursuing other study areas I encourage you to consider studying Data Science or Data Analytics. By the way, Data Science is the umbrella under which several specific focuses e.g. Data Analytics. Here are essential skills needed in Data Science:

Data Wrangling And Reprocessing

This is the most foundational skill set in Data Science that you must acquire. This entails a range of things such as cleaning data or remediating data – also referred to as data transformation. These are processes done on raw data to ready it for usability. For some, it might be to make it ready to be blended with other data sets. Obviously how you do data wrangling and reprocessing will depend on the software or platforms you will be using. Microsoft Excel does not have data transformation features as does Microsoft Power BI and Tableau, for example.

Data Visualization Skills

Data visualization is the representation of data in pictorial or graphic format. This is one of my favourite things to do in Data Science. Data visualization is pivotal because it is the foundation upon which insights can be easily drawn from data. Again, data visualization tools will depend upon the software or platform you will be using. At the most basic level, you need to grasp how to determine which data visualization method to use and why. For example, there are different types of graphs – which one should you use, for which scenarios and why? Understanding those dynamics make up for acquiring data visualization skills.

Mathematics And Statistics

Knowing these might not apply to everyone since some areas do not necessarily need them. Having a basic appreciation of maths and stats is still relevant to what you will be doing. However, when it comes to going in-depth, especially towards the data modelling end, you will need maths and stats big time.

Machine Learning Skills

If you are interested in Data Analytics you might not need to worry much about coding. Though having coding skills will no doubt be an added advantage. When getting into the core of Data Science, coding will be inevitable and foundational. Some of the most common coding proficiencies you will need will be in SQL, Python, and R, amongst others. Machine learning as a whole, encompasses coding, deep learning, data modelling, and so much more. You will need these machine learning skills if Data Science is what you are pursuing.

Lifelong Learning Skills

Lifelong learning is the principle of pursuing education or training initiatives or programmes aimed at personal development. Just like many other areas today, Data Science is ever evolving. You need to keep up and up to date by never stopping to learn. A good example I can cite here is Microsoft Power BI. It is regularly updated monthly with new features or ways of doing things. If you spend say, 6 months without using you will feel as if you are starting. This calls on you to continuously learn so that you do not lag. You cannot expect to have a successful Data Science career without lifelong learning skills.

Soft Skills

Soft skills are universal and are essential to any field of human endeavour. In Data Science there are 3 core soft skills you will need the most. These are Communication Skills, Team Player Skills, and Ethical Skills. In Data Science you will mostly work in or with teams. There will be a heightened need for seamless coordination which calls for communication and being a team player. Data has serious implications, good or bad, on real-world people, events, circumstances, and so on. What you do to or with data, particularly data models has bearings on human lives, directly or indirectly. That is why one needs to be endowed with the necessary ethical skills.

Real World Project Skills

It is not enough to just acquire theoretic knowledge; it is pertinent to apply what you have learnt. You must effectively apply it to real-world projects. By that, I mean real-life scenarios where what you do will have implications on real-life people or frameworks. You might think you have grasped it all but wait till you work on real-world projects. All that you have learned will be put to the test and you will learn even more. You will fine-tune your Data Science proficiency by working on real-world projects.

Once you grasp all these 7 skills you will be unstoppable in the world of Data Science. Data is now considered the new oil or the most valuable asset in the world. Every industry directly relies on data collection and processing. The insights and data models drawn and created are central to strategy formulation. That is why I highly recommend you pursue a career in Data Science.