Today, data science is one of the most sought-after and fastest-growing career paths, with a high average salary, job security, and an abundance of openings at exciting companies. With all the information and resources available online, almost anyone can become a data scientist with enough time and concerted effort.
But what should you consider when deciding whether data science is the career for you?
Key characteristics of data scientists
Here are some characteristics of people who thrive in data analytics jobs:
- Detail orientation. Data scientists need to be precise, as misreading a number or joining the wrong tables together can lead to entirely different outcomes. Perfectionism helps to ensure error-free code and clean data to manipulate.
- Independence and teamwork. Data science jobs are popular remote work options because data mining can be done from almost anywhere; however, data scientists also need strong communication skills and the ability to collaborate as a team.
- Love of data-driven problem-solving. According to a SAS survey of data scientists, the most dominant trait was strong logic and analytical skills. There are many different approaches to answering business questions. Some people are more intuitive and prefer qualitative research, such as speaking to others about their experience and conducting informational interviews. Data scientists are all about the numbers, running experiments like A/B tests, and supporting hypotheses with facts and figures.
- Quantitative bent. Data scientists need a basic foundation in mathematics and statistics. Before embarking on data science courses, you will want to brush up on linear algebra, multivariate calculus, and probability.
- Curiosity. Data science is fundamentally about asking the question “Why?” over and over again. Data scientists look for the truth and can dig deeper when they come across surprising findings.
Although 41% of data scientists tend to display certain key characteristics (analytical, logical, technical), many successful data scientists show strengths in creativity and more right-brain areas.
Surprisingly, you also may not need as many prerequisites as you think to become a data scientist. These are some myths about the data science career track we can debunk:
- You need a bachelor's degree
- You need an advanced degree, like a master’s degree or a Ph.D.
- You need prior experience
By building a project portfolio and continuously honing your skills, you can develop a competitive profile for many data science jobs, even without formal credentials. Taking on side projects/internships, starting as a data analyst, and networking with seasoned professionals will set you on the right trajectory.
Most importantly, data scientists should love learning, as there are always new tools and skills to pick up, as industries expand their use of unstructured data and machine learning.
To become a successful data scientist, here are some essential skills you need to master.
Top data science technical skills
- SQL. Structured Query Language (SQL) helps to pull, manipulate, and transform data from internal and external datasets. Through SQL, you can clean and wrangle the data, turning it into easy-to-use, interconnected databases (database management).
- Programming languages. There are many different technical languages that will strengthen your toolkit, but the most popular ones include Python, R, and SAS. Computer science skills enable you to efficiently perform statistical modeling, data storage and processing, and predictive analytics. If you choose Python, you will need to learn libraries like Scikit-learn, TensorFlow, PyTorch, Pandas, and Numpy. As you progress, you may also want to familiarize yourself with data frameworks like Hadoop and Spark.
- Data visualization. Analytical tools, such as Tableau, Power BI, Matplotlib, ggplot, and d3.js, help you represent data through visually appealing charts and graphs and derive meaningful insights. As a data scientist, you may be asked to build dashboards that help stakeholders easily recognize patterns in the data and adapt their decision-making in real-time.
- Machine Learning. Although not all data scientist positions require machine learning, you should develop an understanding of neural networks, decision trees, reinforcement learning, and adversarial learning. More advanced knowledge of supervised or unsupervised learning, computer vision, natural language processing, and recommendation engines will only help you to stand out further in building algorithms to process big data.
Top data science soft skills
- Interpersonal skills. Data scientists continuously communicate their findings and often work cross-functionally with business, engineering, and product teams.
- Business acumen. The ultimate purpose of data is to solve business problems and improve growth and profitability, either through cost-cutting measures or new revenue opportunities. Data scientists should understand company Key Performance Indicators (KPIs) and stay abreast of industry news and trends.
- Storytelling and communication skills. In addition to using data visualization techniques to paint a picture of dataset trends, you will also need to craft a compelling story, tied to business outcomes, and explain your process and results to non-technical colleagues. It’s important to be clear and concise during presentations and create a logical flow.
How to become a data scientist
Once you understand the job requirements, talk to current data scientists to learn more about a typical day in their life, join data science communities like KDNuggets, Kaggle, and career-oriented Slack channels, and read about novel data science projects companies are pursuing.
If you’re interested in exploring a data science career, you can develop expertise through online courses and certifications on Coursera, Udacity, or other platforms and through Springboard’s comprehensive bootcamp, which provides mentorship, a job guarantee, and real-world projects that can boost your portfolio.
Is data science the right career for you?
Springboard offers a comprehensive data science bootcamp. You’ll work with a one-on-one mentor to learn about data science, data wrangling, machine learning, and Python—and finish it all off with a portfolio-worthy capstone project.
Check out Springboard’s Data Science Career Track to see if you qualify.
Not quite ready to dive into a data science bootcamp?
Springboard now offers a Data Science Prep Course, where you can learn the foundational coding and statistics skills needed to start your career in data science.