Data science is currently one of the fastest-growing career paths in the world. Data scientists are being recruited in virtually every industry and computers are constantly advancing to be able to analyze increasingly large sets of data.
As time goes on, the demand for well-trained data scientists is only going to intensify. Data science is also one of the most challenging fields a person can enter, partly because of the varied range of skills that a person needs to become a success in this field.
A successful data scientist requires a great deal of education and training, but they also need to be well-versed in what are referred to as “soft skills,” which are qualities that are hard to measure and include things like teamwork, accountability, and communication. Your training as a data scientist is vital, but a person who is strong in hard skills but weak in soft skills will not be a positive addition to any workplace. Below, you can read about examples of some hard skills and soft skills that will help you become a valuable data scientist in any industry you choose.
- Coding: To be a successful data scientist, you need to have a solid understanding of the system that is producing and analyzing the data you’re working with. There are a lot of programming languages to be familiar with, but there are a handful that are the most commonly used in data science. Knowing at least one language before entering the job market is crucial; it will also show that you can learn other languages over time.
- Statistics: Statistics are a critical element of data science, so you have to be able to understand all types of statistical models and be able to explain them to people who typically have less expertise than you. This knowledge will also help you be able to decide which type of application is most appropriate for the data you have and which will lead you to the information you’re trying to find. Each model has its pros and cons.
- Analytical Tools: Pretend you’re trying to find the biggest number in a batch of millions. You’d never be able to look through them individually, but you could place them in smaller groups and have one person sort through each group to find the answer much quicker. This is a simple explanation of what analytical tools can do. In simple terms, they break down data into more manageable pieces that are easier to analyze. These tools help data scientists obtain important insights from large data sets; you can see why this knowledge is a fundamental precursor to a successful data scientist. The three analytical tools that are most commonly used in data science are SQL, Spark, and Hadoop.
- Artificial Intelligence (AI) and Machine Learning (ML): AI/ML are two of the most common and fastest-growing careers that data scientists are entering, so having some foundational knowledge of these concepts is key for any aspiring data scientist. These careers are becoming more essential as demands increase in complexity and are no longer able to efficiently be done by humans. Learning more about how AI and ML interact with each other will help you understand and explain to your teammates how it can be effective for whatever business you’re working for. Being able to appropriately “train” your AI/ML software to show you the correct data will be an invaluable skill, and these engineers are some of the highest-paid in the industry.
- Communication: As the world becomes more comfortable with remote work, communication can be a skill that gets harder to hone. Even though you’re not typically interacting with your clients and teammates in person, this doesn’t mean communication skills aren’t a vital tool for any professional. Data scientists often need to explain their findings to people with relatively limited technical knowledge; they must be able to translate hard data into terms relevant to whatever business they are working in a way an entire team can understand. They also need to be able to explain how they arrived at their conclusions and what the results mean in terms of an action plan.
- Teamwork: Analyzing data might sound like it would be a rather isolating career, but the reality couldn’t be more different. Data scientists commonly work within larger teams to implement positive changes to an organization. As a data scientist, the recommendations you make based on the data you reviewed are just a few pieces of the puzzle. Your recommendations have to be broken down into achievable steps by other professionals whose work will be impacted by these suggested changes. One of the ways you can ensure you’re being a positive team member is to give feedback respectfully and clearly.
- Curiosity: Data scientists are incredibly intelligent people, but those who stay curious for new approaches are the most successful. Data science rarely produces one answer; there is always more to learn beyond the initial results for those with the drive to take a closer look. A good data scientist will be able to answer the initial question that was asked of the data; a great one will be able to answer questions that nobody even thought to ask. To work on developing your curiosity in the workplace, you can try simple things like making time to journal at the end of the day and spending more time with a coworker who has different ideas and approaches than you do.
- Problem Solving: You can’t be a data scientist if you don’t have the desire and the ability to solve complex problems. Being able to solve problems is a multifaceted skill that includes the ability to quickly identify the root cause of a problem and also understand the most efficient way to approach a problem. This skill also contains elements of communication skills, since data scientists often need to work with a larger team to get a job done. The best problem solvers are those who utilize the resources available to them to constantly adapt and update their methods as they examine a problem.
Learn Data Science in NextGen’s Summer Classes
If learning data science as a high school student sounds exciting to you, you may want to check out NextGen Bootcamp’s data science courses for high schoolers. The school offers in-person data science classes at its campus in Manhattan, as well as live online data science classes that can be attended remotely from all over the world. You can also review the full list of coding courses for high school students on NextGen’s website if you’d like to learn more.
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