Welcome to the exciting world of data science! If you’re drawn to a career that blends analytical prowess with the thrill of uncovering insights, you’ve landed in the right place. The realm of data science has grown significantly, and its impact is felt in sectors from technology and manufacturing to financial services and healthcare. That’s because organizations of all types need to turn numbers into actions and strategies.
One number that’s bound to interest you is the one at the bottom of your pay slip. The 2024 Robert Half Salary Guide reveals that one-third of managers will boost salaries to attract professionals with data science skills. This move clearly indicates the pivotal role data science plays in steering business decisions, sparking innovation and providing companies with a competitive edge.
Unlocking the value of big data
As the world becomes increasingly data-driven, data becomes more valuable — provided it can be put to practical business use. Enter the data scientist. Businesses need people with statistics and data modeling knowledge to unlock the value of complex, unprocessed data from various sources — machine log data, digital media and documents, databases, the web, social media channels and Internet of Things (IoT) sensors.
The business intelligence, or actionable insights, that companies can glean from the data they gather can be used to inform decisions about everything from new product development to marketing campaigns to supply chain design. Organizations are also relying more on these insights to help them improve cybersecurity, customer experience and engagement, employee retention, recruitment and productivity, and much more.
Because companies can use data-driven business intelligence in many ways, they want to hire data scientists with a head for business. Communication and other soft skills are also essential. One reason these skills are necessary is that data scientists are often required to explain quickly and concisely to nontechnical people the risks, trends and opportunities the business should monitor or act on.
Core technical skills in data science
While fluent communication is a cornerstone of data science, high-level technical skills remain the foundation. Data scientists need to bring a range of analytical and mathematical know-how to their roles — not just any old math, but areas like multivariable calculus and linear algebra.
What about programming languages? Python continues to be the flexible and user-friendly programming language of choice for a wide range of data science tasks. Its libraries and frameworks are indispensable for data manipulation, analysis and machine learning. R, with its strong roots in statistical analysis and visualization, remains a staple for those looking to delve deep into data exploration and predictive modeling.
Visualization tools have become more advanced, with Tableau and Power BI leading the charge for creating interactive and insightful visual representations of data. The ability to work with cloud-based storage and computing services like AWS and Azure is now a must as more organizations move their data infrastructure to the cloud.
Natural Language Processing (NLP) and text analytics have surged in importance due to the exponential increase in unstructured text data. Data scientists are expected to extract insights from textual data sources, making skills in NLP highly sought after.
Lastly, the field has seen a burgeoning emphasis on AutoML tools, making machine-learning techniques accessible to a broader range of professionals. These tools automate many aspects of building and tuning machine learning models, allowing data scientists to focus on more strategic problems.
Education level for advanced data scientist roles
While it is true that many organizations have traditionally preferred candidates with a Ph.D., the data science industry is also recognizing the value of diverse educational backgrounds and practical experience. The rise of tools like AutoML has lowered the barrier to entry, allowing individuals with strong foundational knowledge in programming, math and statistics to enter the field and make significant contributions.
That said, a Ph.D. still holds substantial value, particularly for roles that involve advanced research, developing new algorithms or tackling complex scientific questions. A doctorate may provide a competitive edge for those looking to reach the pinnacle of their data science career.
Data scientist salary: what to expect
Now that you have a better sense of the soft skills, technical abilities and education requirements needed for a data science career, what type of data science salary might you expect? You can find the latest national and local salary ranges for data scientists in the 2024 Robert Half Salary Guide.
Note that salary rates differ by experience and location. To localize your insights for a data scientist salary for your city, enter your city’s name in the Salary Calculator.
Laying the groundwork for a data science career
If you’re a college student or recent graduate wondering how to become a data scientist, must-have job requirements will depend largely on the employer, the company’s technology tools for managing its data, and whether the business has the time and resources to invest in developing entry-level data scientists.
Here are some ways to gain relevant knowledge and skills and increase your chances of successfully launching a data science career.
- Stay current with online resources. Look for e-books, online courses and video tutorials that dig deeper into data analysis, statistics, data coding and related topics that interest you. (Some examples of resources offering online learning options for data science include Coursera, DataCamp, edX, Kaggle, and Udacity.)
- Learn relevant programming skills. Obvious advice, perhaps, but you’ll want to do this before you start applying for data scientist jobs. Becoming proficient with fundamental languages like Python, R and SQL will likely be essential.
- Check out data scientist job descriptions. Research data scientist job descriptions from the organizations you’d like to target for employment. Are they asking for experience with big data tools like Apache Spark or Hadoop? Do they value familiarity with cloud computing services? What about proficiency in newer languages or frameworks that may be gaining traction in the industry?
- Get to know the data science community. Networking is essential. Consider joining specialized LinkedIn groups dedicated to data science, where you can interact with both seasoned professionals and those aspiring to enter the field.
- Request informational interviews. When connecting with experienced data scientists, seize the opportunity to request informational interviews. Such conversations can offer a deeper understanding of others’ career paths and the industry at large. Remember to also tap into the knowledge and networks of your existing contacts, including peers, mentors and professional associates. They may offer guidance on breaking into the data science field and introduce you to potential collaborators or employers.
- Start your own data science projects. Taking the initiative to build your own data science projects demonstrates a passion for learning, a quality that can give you an edge in the hiring process. It indicates to employers that you are committed not only to learning new skills but also to applying them in creative and innovative ways just because you love it. GitHub serves as a repository for open-source projects that offer real-world coding experience. Stack Overflow provides a community-driven platform for troubleshooting and learning from peers in the field.
Ready to kickstart your data science career? Everything discussed here can set you on the right path. Don’t forget the power of specialized tech recruiters while on this journey. They’re your bridge to local organizations and employers seeking fresh talent for entry-level positions. Plus, they offer insider job search tips to help you navigate the market.
Want to start working with a recruiter? Get the ball rolling and submit your resume to Robert Half today!
