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If you haven’t heard yet, in the next 3 years, 40% The workforce is expected to improve their skills. This is natural to keep up with the continued growth of technology, specifically generative ai.
However, the IBM report states that executives estimate that 40% of their workforce will need to be retrained due to artificial intelligence and automation. However, he also states that in the next 3 years analytical skills with business acumen and plenty of interpersonal skills will be highly desirable.
In this article, I’ll go over the most sought-after skills in 2023 and how they will benefit your career in the future.
So let’s get into it…
As we can see, many things are changing due to technology and the rise of generative ai. If you’re thinking about starting or improving your career in data science, these are the most sought-after skills for 2023.
Programming language
Let’s start with the basics for those looking to start a new career in data science.
Choose a programming language to learn it and learn it well. Get to know the ins and outs, all the nooks and crannies, everything you can possibly know about it. It is better to be a master at one thing than a jack of all trades.
Many organizations want to know that when they hire someone, they can get more than one benefit from them. For example, this employee is very proficient at manipulating data; however, he is fantastic at creating data visualizations for our board meetings.
If you are not sure which programming language to choose, read 8 Programming Languages to Learn Data Science in 2023.
Data cleansing and manipulation
Now let’s see what tasks you will be assigned as a data scientist. There is a lot of data available and with the rise of Big Data and its use for generative ai, organizations will want to use it. Data cleansing and manipulation involves transforming raw data into a format that can later be used for analysis.
Although some say that data scientists spend up to 80% of their time cleaning data, this is not always true. It is a time-consuming task; however, it doesn’t require even 80% of a data scientist’s time, all the time.
That said, it will still be a highly sought-after skill for data scientists in 2023. Why? Because data is rarely nice and clean. Especially now that organizations are sifting through old data that has gathered dust and trying to find ways to use it. Get out the dustpan and brush, because there’s definitely some cleaning to do.
Analysis capacity
As I mentioned before, executives in the next 3 years will be looking for employees who have strong analytical skills. According to the IBM report, one of the top priorities for executives is to improve employees’ skills in a variety of soft skills such as time management and communication. After this come analytical skills with business acumen.
Analytical skill areas include:
- Statistic analysis
- Data exploration
- Feature selection and engineering
- Machine learning
- Model evaluation
- Data visualization
Take statistical analysis for example, it is known as the foundation of data science and allows you to explore data through descriptive statistics, better understand your data and represent it through visualizations. They work hand-in-hand with elements in the data cleaning and discussion phase, such as missing values and anomaly resolution.
Analytical skills underpin the life of a data scientist, so the same rule applies: know the ins and outs, nooks and crannies, and you will excel as a data scientist.
Machine and deep learning
As we live in times where organizations strive to use data to provide them with insights and use it to automate tasks, having a competent understanding of the elements of machine and deep learning will be paramount.
Deep and machine learning skill areas include:
- Mathematics and statistics.
- Machine learning algorithms
- Deep learning architectures
- Neural networks
- GPU and computing frameworks
- Deployment
Both machine learning and deep learning have been shown to have amazing capabilities in extracting insights from data, allowing data scientists to create models that can learn automatically.
Organizations are competitively seeking ways to create high-performing next-generation models across diverse industries. As a data scientist, you will have the ability to handle complex problems, improve accuracy, create models that increase the organization’s competitiveness, and continually drive innovation.
If you have discovered an area of machine learning or deep learning that you are really good at and enjoy, then go ahead with it. Like I said, it’s better to be a master at something than a jack of all trades.
Soft skills
As part of the IBM report, the most critical skills required of the workforce included:
- Time management
- Ability to prioritize
- Work effectively in team environments.
- Communicate effectively
- Flexible, agile and adaptable to change
My personal opinion is that executives have seen that the shift to remote work has possibly been a limitation in these areas. Or in general, it could be a set 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 can’t accomplish right now. technology can help us automate tasks and we can use data analysis to see what works and what doesn’t.
However, if employees do not use their time wisely and are not able to work in a team environment in an agile and flexible way, all that knowledge goes down the drain. Employees are the drivers of innovation, generative ai systems are tools that will help us.
This article was intended to keep you focused on what’s to come in the coming years and what a study of executives has stated they are looking for. If you are new to data science, you will definitely have a lot of studying and work to do; However, having a good knowledge of all the elements will make you more competitive in the future.
If you are currently a data scientist, I hope this article has given you insight that more organizations are looking for candidates with excellent soft skills that can complement their hard skills.
We all need to be aware of how the world is moving, so adopting reskilling or upskilling with the use of artificial intelligence tools will be very beneficial.
nisha arya is a data scientist and freelance technical writer. She is particularly interested in providing professional data science advice or tutorials and theory-based data science insights. She also wants to explore the different ways in which artificial intelligence can benefit the longevity of human life. A great student looking to expand her technological knowledge and writing skills, while she helps guide others.
