Breaking Into Data Science

We often see students in our office interested in careers in data science. We’ve compiled this tip sheet with basic information on the field and ways CUNY GC students can learn more. We invite you to meet with one of our career advisors to talk more about how this information applies uniquely to your own career planning.

 What is a Data Scientist?

According to the US Department of Labor’s Bureau of Labor Statistics, data scientists work to:

  • Determine which data are available and useful
  • Collect, categorize, structure, and analyze data
  • Create, validate, test, and update algorithms (a set of instructions to tell computers what to do) and models
  • Use data visualization software to present findings
  • Make business recommendations to stakeholders based on data analysis
Essentially, data scientists take a business problem, then use statistical and machine learning tools to solve them for business impact.

The DOL reports that employment of data scientists is projected to grow 35% from 2022 to 2032, much faster than the average for all occupations.

Which Skills are Important for Data Scientists/What are Interviewers Looking For?

There are both technical and nontechnical skills that are important for data scientists to have to support in the work of collecting data, optimizing data, analyzing data, and reporting on results. ONET Online offers an excellent overview of on the job skills.

Software engineering, mathemathical skills, and statistical tools often required in job descriptions can include (but are not limited to):

  • Programming, data acquisition, data modeling (Python, PySpark, R, C++, Java, Excel, OOP, SQL, SAS, SQLAlchemy)
  • Machine learning (scikit-learn, Pandas, NumPy)
  • Big data (Hadoop, Spark)
  • Deep learning (Keras)
  • Data visualization (Tableau, Microsoft Power BI, PowerPoint)
  • Statistics and mathematical skills

Additionally, data scientists need to understand their business functions well. According to the DOL, non-technical skills needed are analytical, communication, logical-thinking, and problem-solving. Interpersonal skills/collaboration, project management, an ability to work under deadlines, and business acumen are also important. It is quite helpful to look at current job descriptions to get a sense of industry language/terminology, key words, technical skill sets desired, and other requirements.

How & Where Can I Gain Experience in Data Science?

In addition to taking courses, working on projects, and seeking out internships, there are a number of organizations where you can use your data science skill sets and include these experiences on your resume.

One alum in the field suggests that graduate students to do research projects related to machine learning, deep learning, and incorporate these skills into their existing research work. They can also get involved in machine learning oriented research (as a research assistant), complete class projects (that incorporate data skills), and gain work experience/ internship experience solving real-world problems. In addition to looking for opportunities on Handshake, and direct on organization web sites, this alum recommends looking at roles in the New York City government and its agencies.

Other Sample Opportunities to Build Skills:

Volunteer-based

Competition-based

Virtual Simulation Experiences

Contributing to Open Source Projects on GitHub:

What is Unique about Data Science Resumes?

Data science resumes should follow the principles of any strong resume, with the addition of:

  • Including and detailing course projects or personal projects that demonstrate your data science skills in a Technical or Related Projects section
  • Ensuring your prior experiences highlight transferable skills relevant to the data science field (e.g. both technical and non-technical/soft skills)
  • A link to a portfolio of your projects &/or GitHub presence with READMEs (check out this blog for ideas)
  • Featuring your technical skills clearly, potentially toward the top of your resume

What is Interviewing Like in this Field?

Interviewing for a role in data science will incorporate a combination of behavioral interviewing (which includes answering common questions and sharing specific examples of experiences that connect to the skills for the job) and technical interviewing, which assesses your technical prowess. For information on preparing for technical interviews, read our How to Prepare for a Technical Interview tip sheet.

Hiring managers are looking for candidates who are solid in their fundamentals (statistics, machine learning/ deep learning), quick to learn new skills (like generative AI), willing to communicate and collaborate with with business partners to understand business problems, and then are able to translate these into machine learning problems and solve them on their own or with a team.

Where Can I Learn More?

Follow blogs, newsletters and sites like Toward Data Science, KD Nuggets and Data Science Weekly. Explore joining Meetups (search Data Science or core technical skills and your area), and follow news relevant to the industry you want to work in to stay up to date on trends. Consider joining professional associations like Women in Data Science, Association of Data Scientists, INFORMS, American Statistical Society, and Academic Data Science Alliance, many of which have student membership rates. ONET offers a more expansive list of organizations.

Related Posts:

Alumni Alouds (podcasts featuring GC CUNY Alumni):

Additional Resources:

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