Future-Proof Your Data Game: Top Skills Every Data Scientist Needs in 2023
An overview of the most sought-after skills in 2023 based on the rise of generative AI.
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If you haven’t already heard, in the next 3 years, 40% of the workforce is expected to upskill. This is natural to keep up with the continuous growth in technology, specifically generative AI.
However, the IBM report stated that executives estimate that 40% of their workforce will need to reskill due to AI and automation. However, it also states that analytical skills with business acumen and a bunch of soft skills will be highly desirable in the next 3 years.
In this article, I will go through the top sought-after skills in 2023 and how these will benefit your career in the future.
So let’s get into it…
Data Science Skills for 2023
As we can see, a lot of things are changing due to technology and the rise of generative AI. If you’re thinking about starting or upskilling in your data science career, here are the most sought-after skills for 2023.
Let’s start with the foundations for those looking to start a new career in data science.
Choose a programming language to learn and learn it well. Learn the ins and outs, all the nooks and crannies, everything you can know about it. It’s better to be a master in one thing than a jack of all trades.
Many organizations want to know that when they employ somebody, they can reap more than one benefit from them. For example, this employee is very proficient in data wrangling, however, they are amazing at creating data visualizations for our board meetings.
If you are unsure of what programming language to choose, have a read of 8 Programming Languages For Data Science to Learn in 2023.
Data Cleaning & Wrangling
Now let’s get into what tasks you will be assigned as a data scientist. There’s a lot of data out there, and with the rise of BigData and its use for generative AI, organizations are going to want to make use of it. Data cleaning and wrangling consist of transforming raw data into a format that can be later used for analysis.
Whilst some say that data scientists spend up to 80% of their time cleaning data, it’s not always true. It is a time-consuming task, however, it doesn’t take up to 80% of a data scientist's time - all the time.
With that being said, it’s still a sought-after skill for data scientists in 2023. Why’s that? Because data seldom comes nice and clean. Especially now with organizations skimming through old data that has collected dust and are trying to find ways that they can use it. Get your dustpan and brush out, because there’s definitely some cleaning to do.
As I mentioned before, employees who have strong analytical skills are what executives in the next 3 years will be looking out for. According to the IBM report, at the top of executives' list is to upskill employees in a variety of soft skills such as time management, and communication. After this comes analytics skills with business acumen.
Areas of analytical skills include:
- Statistical Analysis
- Data Exploration
- Feature Selection and Engineering
- Machine Learning
- Model Evaluation
- Data Visualization
Let’s take statistical analysis for example, it is known as the bedrock of data science and allows you to explore data through descriptive statistics, understand your data better and represent it through visualizations. They work hand-in-hand with elements in the data cleaning and wrangling phase such as missing values and addressing anomalies.
Analytical skills underpin the life of a data scientist, therefore the same rule applies - know the ins and outs, nooks and crannies, and you will excel as a data scientist.
Machine & Deep Learning
As we’re living in times where organizations are pushing towards using data to provide them insight and using data to automate tasks for them - having proficient knowledge of the elements of machine and deep learning will be paramount.
Areas of machine and deep learning skills include:
- Mathematics and statistics
- Machine learning algorithms
- Deep learning architectures
- Neural networks
- GPUs and computing frameworks
Both machine and deep learning have been shown to have amazing capabilities when extracting insights from data, allowing data scientists to build models that can automatically learn.
Organizations are competitively looking at ways to build state-of-the-art models with great performance in various industries. As a data scientist, you will have the ability to handle complex problems, improve accuracy, build models that increase the organization's competitiveness, and continuously drive innovation.
If you have discovered an area in machine learning or deep learning that you’re really good at and enjoy, then run with that. As I said, it’s better to be a master in one than a jack of all trades.
As part of the IBM report, the most critical skills required of the workforce included:
- Time management
- Ability to prioritize
- Effectively work in team environments
- Communicate effectively
- Flexible, agile, and adaptable to change
My personal opinion is that executives have seen that the shift in remote work has possibly put a constraint on these areas. Or it could generally be a bunch of skills that can effectively turn ideas into realities.
To keep up with generative AI, executives are looking for employees who can do something that generative AI tools aren’t able to achieve right now. Technology can help us automate tasks and we can use data analysis to see what is working, and what isn’t.
However, if employees do not use their time wisely, and be able to work in a team environment in an agile and flexible manner - all those insights go down the drain. The employees are the drivers of the innovation, the generative AI systems are tools that will aid us.
This article aimed to keep you focused on what’s yet to come in the next few years and what a study of executives has stated they are seeking. If you are new to data science, you will definitely have a lot of study and work to do - however having a good knowledge of all the elements will make you more competitive in the future.
If you currently are a data scientist, I hope this article has provided you with insight that more organizations are looking for candidates with great soft skills that can complement their hard skills.
We all need to keep up with how the world is moving, therefore embracing reskilling or upskilling with the use of AI tools will be very beneficial.
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.