Workers are resigning in record numbers and many are making career changes. That’s hard to do! Early in a career change, you often don’t have sufficient knowledge of your new field or what’s required to excel in it. This gets more challenging if the field you’re breaking into is truly interdisciplinary.
Take data science, for example. Data science is fundamentally a computer science field built upon other knowledge bases that aren’t necessarily a part of computer science. And so, to become a data scientist, you’ll need to grow your knowledge beyond programming and mathematics.
So let’s consider 10 skills that every successful data scientist needs.
10 Key Data Science Skills
- A Curious Mind
- A Passion for Learning
- Strong Collaboration
- Clear Communication
- Compelling Storytelling
- The Ability to Adapt
- Business Acumen
- Structured Thinking
- Data Ethics
Although all these skills are essential, the degree of their importance obviously varies based on your exact role responsibilities and the nature of your current project.
1. A Curious Mind
Okay, this is more of a trait than a skill — but it’s the most important thing on the list! If you’re going to be a data scientist, you need to be curious. Data science is the process of shaping, transforming and telling stories through data sets.
As a data scientist, you’ll always need to question why things happen; you will need to wonder why some events affect that data more than others, ask a lot of questions about your data and its behavior to fully understand it, and be able to utilize it to predict future events (not to mention make optimal decisions!). Intellectual curiosity is the driving force behind all great data scientists.
2. A Passion for Learning…Efficiently
Data science is an ever-evolving field so, as a data scientist, you always need to be in learning mode. A passion for learning goes hand to hand with having a curious mind, and together they make a great contribution to helping you become a better data scientist.
You not only need a passion for learning, but a passion for learning efficiently. The amount of knowledge you need to obtain as a data scientist can be overwhelming, so knowing how to learn new skills and tools faster can make a big difference in your career.
3. Strong Collaboration
Being a great collaborator means being a team player. When you’re a data scientist, you’ll probably be a part of a team and each of you will work on a specific aspect of the project. As a group, you’ll need to get feedback and develop possible solutions together to build a solid project.
Sometimes you’ll need to build on other team members’ work, so you need to know how to work well with others. This, of course, connects to having strong, efficient communication skills.
4. Clear Communication
When a data scientist gets a new project, they’ll need to communicate with a client or manager about the project’s requirements and the project’s end goals. So, to be a great data scientist you need to know how to ask the right questions and clearly formulate your problem statements.
The importance of good communication skills goes beyond the first stages of the project; you will need to present and communicate your findings to your client or manager at the end of the project. Your findings will be used to make decisions for future steps, and the way you communicate these findings will be the deciding factor between making the right or wrong decisions down the road.
5. Strong Storytelling
I’ve written my fair share of articles about using visualizations for highly effective storytelling while communicating your findings cleanly and clearly. Data scientists tell stories with data and how you tell that story can be the difference between making an impact at work and having your presentation fall completely flat.
If you’re a strong storyteller, you can convey your findings in a coherent and easy-to-understand way, which is essential because not everyone you’ll present to will have your technical knowledge. Thus, practicing your storytelling will make you (and your work!) stand out in the data science community.
6. The Ability to Adapt
One of the important skills to develop if you’re in any technical field — and data science in particular— is your ability to adapt to change. As I’ve said, data science is always changing; researchers develop new tools, algorithms and methods regularly. So, as a data scientist, you’ll need to always be ready to try out a new tool or a technique. Moreover, you need to be able to respond to varying trends in the field — and the trends are always varying!
7. Business Acumen
Most of the time, companies use findings from any data science project to make business decisions. As a result, data scientists need to have at least a basic understanding of their company’s business model and what’s important to the business.
This skill is probably my least favorite aspect of data science, but it’s essential. Knowing the company’s model, their perspective and their client base can help you optimize your decisions when analyzing and modeling the data. And let’s face it: Sometimes your managers may not really know what they’re asking you for. But with a little business acumen and your expertise in data science, you’ll be a valuable asset to any team.
8. Critical Thinking
Critical thinking is, of course, important regardless of your particular field. As a data scientist, strong critical thinking skills will allow you to approach your analysis objectively. You’ll also be able to frame strong questions and make the optimal decisions about the algorithms you use and how they’ll benefit the overall project. In short, strong critical thinking skills enable data scientists to look beyond obvious trends and anomalies to take a closer look at the information the data’s trying to convey.
9. Structured Thinking
When you are first given a project, it may look complex and impossible. That’s why data scientists need to know how to take a big problem and divide it into smaller, more manageable parts. To be able to divide a big problem into smaller sub-problems requires structured thinking. This skill will help you complete your project in a timely and efficient manner. So, spending some time working on this skill will save you much time (and effort) in the future. Try downloading a large data set and going through the process of cleaning, organizing and analyzing your data for practice.
10. Solid Data Ethics
One of the biggest concerns you may face when deciding to get into data science is data ethics. As a data scientist, you’ll have access to confidential (and sometimes sensitive) user information. You’ll need to use this information to build and develop your models.
But, just because data can be collected doesn’t mean you should use it — or even collect it. The problem is, sometimes what’s what and what’s wrong isn’t so cut-and-dry. If you’re worried you’re stepping into an ethical gray area, take some time to practice (in the mirror, to a friend) explaining your choices and why you made them. Making sure you’re on ethically solid ground will make it easier for you to prove the value of your work while demonstrating you adhered to ethical standards of data collection and analysis.
When it comes to a technical field like data science, figuring out what technical skills you need to practice isn’t difficult. Even if you’re not yet familiar with the field, you could probably guess you’ll need some sort of programming and math knowledge to land a data science job. The challenge arises when we discuss soft skills. Because data science is an interdisciplinary field, the required soft skills may not be intuitive. These 10 skills will make you a better data scientist, colleague and collaborator.
This article was originally published on Towards Data Science.