In the new world that is shaping around us, a new currency is emerging. No, we are not talking about bitcoins. We are talking about data.
The digitisation that is sweeping the vertical and horizontal market across the world is reconstituting what we had known until this point in time. The data science sector is particularly witnessing this impact generating new skillsets and roles.
What does a data scientist do?
To put it simply, a data scientist goes through a massive amount of structured and unstructured data giving insights and inputs to meet business goals and needs.
But this is not all.
A data scientist is also a data analyst, an IT architect, a test manager, and a data visualizer when the need arises.
Given the scope of a data scientist in the modern world, the career has become a lucrative choice. For instance, according to the Bureau of Labour Statistics, the average salary can be pegged at $111,000 in 2016. It is predicted that more jobs will come up in this field and by 2024, it is likely to grow by 11pc.
According to a Glassdoor report, data scientist is ranked the best across different industries. So, if you are working in the field or are planning to enter it, you are looking at a rewarding career.
Here are six trends that you should know about if you want to begin a career in data science.
All industries are open in the modern world, yet there is not one dominant industry when it comes to data science.
Manufacturing, financial services, and logistics sectors are all emerging markets which require data science; however, in the next few years, you will find that the role of a data scientist will become valuable almost everywhere.
But that does not mean that industries are not looking for specialists. They are looking for a person who can research in a particular sector and that is what you should focus on as well. You need to make sure that your resume stands out as one that is relevant to a specific industry.
For instance, in the financial services sector, a data security specialist is much sought after. The skills that a data security specialist brings to the transaction and accounting sector is of high value given the consequences a potential breach can cause. Therefore, data scientists are required for ensuring compliance, security and fraud detection in the financial services sector.
Most of the data scientist jobs require the candidate to have a PhD in mathematics or in statistics from a recognized university. While academic training is essential and a PhD does command the attention of the employers, it is not a must for all possible data scientist roles.
So if you do not have a high-level academic degree, you are looking at balancing your experience with on-the-job learning. This means you will have to develop some skillsets that will help you meet the industry standards and needs. Professional development courses, boot camps, and online courses are just the beginning. Additionally, you can also consider a a big-data certification to add value to your resume.
Upskilling is important especially when we are talking about a sector such as the data science sector. It is especially required of candidates to familiarise themselves with new technologies and trends.
Therefore, make sure that you are constantly researching the market (your interest area) and are learning about it as much as you can. Attend meetings, trainings and make sure that you balance this with on-the-job learning.
In the data science field, data analysis is also becoming popular. This is so because businesses need clean data upon which they can review their businesses.
This makes quantitative analysis a very important skill to possess. If you can ensure that you improve your ability to analyse, build data strategies, implement machine learning and run an experimental analysis, you will become a valuable asset to the sector.
You should also keep in mind that data science often finds itself working with AI, deep learning and machine learning sector.
So make sure that you research about these disciplines and learn about managing and working with unstructured datasets. Experience will come in handy when you are dealing with crisis situations.
Although data science is often treated as the next evolutionary stage in business intelligence (BI), you should know that those already working in the sector already have some BI skills.
For instance, a critical soft skill which is essential is communication. You need to have communication skills in order to describe the data, explain the insights and analytics that you have extrapolated from it. Communication is also particularly important when you are trying to relay information to people who are non-technical and cannot understand technical information. This requires effective and clear communication in a manner that anyone can understand what you have understood from the data.
An example of a hard skillset is SQL programming. Since its ascending popularity, it has become an important method to work with large amounts of data, manage it, and visualise data. There is no excuse if you cannot work with SQL programming if you want to enter into the data science sector.
Up to Date
If you are trying to build a career in data science, you need to ensure that you do not put all your eggs in one basket.
For instance, some organizations may prefer using NoSQL or MongoDB for managing their data while others may be using Python or SAS as their modelling framework. So you cannot just learn or master one of these trends but rather ensure that you can update your skills as and when needed.
In addition, Teradata, Power BI, and IBM Db2 are industry tools that you should be aware of.
That that data is ever growing means you will be expected to upgrade your skills and experience. If you are ready to do that, you are looking at a fulfilling career in data science.
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