Data Science & Analytics
Data analysts, data scientists, and quantitative analysts combine a variety of programming languages, statistical tools, and specialized technical skills to work with and understand large data sets to develop data-informed solutions for business problems. A data analyst is an entry level role that can be pursued with a Bachelor’s degree. An advanced degree, however, can provide career growth opportunities, and is often required for data scientist and quantitative analyst roles.
When exploring these job functions, consider:
- what would you like to do with data;
- what type of data would you like to work with;
- and in what field you would like to work?
Be aware that job titles and functions vary across fields and organizations. Here are some preliminary definitions:
- data scientists work with structured and unstructured data. Using algorithms, machine learning, and predictive models, they design and construct novel processes for modeling and production to facilitate strategic decision making.
- data analysts use the data primarily for problem solving – applying their technical toolbelts to find patterns, derive insight, and provide strategic answers to specific questions.
- quantitative analysts, or quants, work in financial spaces – brokerage firms, banks, insurance, investment companies, etc. These analysts conduct analyses to make informed decisions about investments.
Skills to develop for success in this field
The depth of expertise required for a role will depend on the level of position and qualifications for the opportunity. When applying for different roles, pay close attention to the skills outlined in the job description.
Typically, data scientists and analysts may need to demonstrate experience in the following areas:
- Analysis
- analytics languages (i.e. Python, R)
- strong statistical and probability theory foundation (i.e. hypothesis testing, statistical significance, linear/logistic regression, Bayesian statistics, Monte Carlo simulations)
- Communication & Collaboration
- experience and comfort with desired tools to help communicate the results of analysis (i.e. PowerBI, Flare, Tableau)
- a track record of effective collaboration and communication with different stakeholders (experts, non-experts, colleagues, external collaborators, etc).
- Data Management
- experience in distributed computing (i.e. Hadoop, Spark)
- experience working with APIs
- a strong foundation in SQL
- Machine Learning/AI
- ML Software tools (i.e. tensorflow, scikit-learn)
Online resources specific to the industry
Vault is a comprehensive resource for information on what it is like to work within an industry, company or profession.
Northwestern Resources and Support
- R & Python Learning Resources for undergraduate students
- Integrated Data-Driven Discovery in Earth and Astrophysical Sciences (IDEAS) IDEAS is a multi-faceted program supported by The Graduate School designed for masters and PhD students. Students can participate at a range of levels.
- TGS Integrated Data Science Certificate
Northwestern’s Graduate School offers certificates to help connect doctoral students across various departments and programs. Earning a certificate not only formally recognizes a student’s focus on and achievement in a particular topic area, but helps form a cohort of students and faculty interested in particular interdisciplinary areas. Certificates typically require five course for completion; the certificate will appear on your transcript.
- Northwestern Institute on Complex Systems (NICO) NICO’s mission is to incubate innovative collaborations that leverage complexity, networks, and data science to address societal challenges. NICO is a collaboration between McCormick School of Engineering and Kellogg School of Management.
Job postings and other career informational sites relevant to the field
Recommendations for internship listings from Vault:
Recommendations for full-time roles from Vault:
Key information or knowledge for this field
Employers are looking for a combination of technical and transferable skills. They want candidates who can demonstrate the following:
- an ability to communicate effectively across stakeholders with varying areas of expertise – verbally, in writing, and through technical tools
- successful partnerships and collaborations with teams in various settings
- an understanding of (and interest in) the domain and sector to which they’re applying
- evidence of leadership (can be formal or informal) and initiative
- interest in continual learning and growth in an ever evolving space
You can communicate sought-after skills across:
- application documents
- Github profiles to showcase work
- the interview process
If your work doesn’t inherently involve some of the more specialized technical skills, seek out opportunities to develop and demonstrate experience through:
- Northwestern coursework
- Massive Open Online Courses (MOOCs) - examples include LinkedIn Learning and Coursera
data science hackathons and competitions
- internships
- side projects/contract work
Relevant student groups and professional organizations
Northwestern Undergraduate Student Organizations:
Northwestern Graduate Student Organizations:
External Professional Organizations: