Raisa Fatima

Author: Raisa Fatima

Timeline for Applying to Graduate School

Adapted from ucsd.edu and columbia.edu  

While the application process for graduate school can be overwhelming, it becomes easier to manage if you split the process into smaller steps over the course of several months (or even years). Here is a timeline for the application process to help you prepare over time!

TIMELINE (if applying for admission immediately following graduation from Lawrence)

Junior (Year 3) 

If you plan to apply to graduate school during senior year, it’s a good idea to: 

  • Solidify which area of study you would like to pursue. 
  • Speak with advisers, professors, and career advisors about your interest in graduate school to get advice and suggestions for beginning the program search process. 
  • Create a CV. Here is a sample to help you get started. This resource can give more advice and examples. Our career advisor can help with this as well. 
  • Start research on graduate programs of interest. Things to consider include placement, curriculum, location and size, and more. Pay attention to details about required standardized exams, application processes and deadlines, faculty research, and financial aid/scholarship information. If necessary, contact schools for more information. 
  • Start gathering information about financial aid: scholarships, fellowships, and graduate and teaching assistantships. This list of resources for funding can help you.
  • Start preparing for any necessary graduate admissions tests, such as the GRE 
  • Get involved in a research project if you have not already gained research experience – click here to explore research opportunities and watch our information session on how to apply to the Lawrence University Research Fellowship for the summer 
  •  If possible, attend conferences in your discipline, especially if they include sessions for prospective graduate students or graduate school fairs 
  • If needed, prepare for taking the GRE exam 
  • Look into extramural fellowships in your relevant fields 

Summer before senior year (June to August) 

This is about 6 months away from most application deadlines. While it is important to use the summer to recharge or do other things like research or internships, it is important to have a strong start to your application process. It is important to: 

  • Narrow down the list of programs you intend to apply to (investigate potential faculty mentors, requirements, etc.) and record application requirements and deadlines. This school comparison worksheet can help you do so – you can also use Excel to recreate this worksheet.  
  • Prepare for and/or take the GRE or other required standardized exams 
  • Draft a personal statement or statement of purpose and any other required application essays or materials.  

Early Fall (September to October) 

By early fall, the application process speeds up. It is important to: 

  • Actively seek and apply for application fee waivers 
  • Contact faculty members to seek their advice and ask if they are willing to write you a strong recommendation letter 
  • If your discipline requires you to reach out to prospective faculty for your graduate program, start reaching out to them – you can find their contact information either on their website or on the department website 
  • Solicit feedback on your personal statement and any other essays from professors you know, campus writing centers, and/or your career advisor 
  • Register to take the GRE no later than October (if you haven’t already done so) 

Late Fall (October to November) 

By late fall, you should be nearing completion of your application materials. 

  • Complete application forms 
  • Revise and finalize your statement of purpose, CV, and any other essays 

Application Deadlines (November to December – deadlines vary by program) 

As year’s end approaches, send your applications by the due date 

  • Submit all applications 
  • Order/send transcripts  
  • Ask your letter writers to submit their recommendation letters, providing all the forms, information, and deadlines 
  • Verify that letters of recommendation, test scores, transcripts, and any other supporting documents were received by the graduate programs  

Reach out for help!

The graduate school application process is daunting, but you do not have to go through it alone! Your professors, academic advisors and our career advisors will be happy to support you throughout this process. Feel free to reach out and make an appointment with Jacklyn Fischer, our PHN advisor, for help! 

Careers in Biotechnology

Adapted from northeastern.edu 

Biotechnology is an interdisciplinary field with applications in many industries. Professionals work for a variety of organizations like government agencies, private companies, regulatory bodies, or clinical laboratories. Employers in the field range in size and type from small start-ups to global pharmaceutical leaders to federal organizations such as the Department of Agriculture and National Institutes of Health. Check out some of the most in-demand biotechnology careers that are shaping our future in the list below. 

Biomedical Engineer 

2021 Median Pay: $97,410 

Projected Growth by 2030: 6% 

Biomedical engineers combine engineering and biological expertise to solve problems in biology and medicine. They design biomedical equipment, devices, and medical software, such as artificial organs, prostheses, and diagnostic machines to improve the quality of patient healthcare. Students with a undergraduate degree in the physical or biological sciences often meet the admissions criteria for a master’s degree in biomedical engineering (for example, check out the University of Minnesota Biomedical Engineering Graduate Program prerequisites here). 


2021 Median Pay: $102,270 

Projected Growth by 2030: 5% 

Biochemists study chemical properties of living things and biological processes, like cell development, cell growth, heredity, and disease. They conduct research projects and often isolate, analyze, and synthesize proteins, lipids (fats), DNA and other molecules. They also research the effects of drugs, hormones, and nutrients on tissues and biological processes to develop products and processes that may improve human health. 

Medical Scientist 

2021 Median Pay: $95,310 

Projected Growth by 2030: 17% 

Medical scientists conduct clinical research to improve patient health by investigating diseases and prevention methods. They develop and test medical devices. They prepare and analyze medical samples to investigate the causes and treatments of toxicity, pathogens, and chronic diseases. They may also help standardize drug potency, doses, and methods for the mass manufacturing and distribution of drugs and medicinal compounds. 

Biological/Clinical Technician or Medical Laboratory Scientists 

2021 Median Pay: $48,140 

Projected Growth by 2030: 7% 

Biological technicians collect samples, perform tests, and analyze results of body fluids, tissue, bacteria cultures, and other substances. These technicians use lab instruments, advanced robotics, specialized computer software, and automated equipment to collect, analyze, and model experimental data. 


2021 Median Pay: $79,260 

Projected Growth by 2030: 5% 

Microbiologists study viruses, bacteria and the immune system to produce biomedical and industrial products. These professionals conduct complex research projects and lab experiments to aid in the diagnosis and treatment of infectious illnesses. 

Process Development Scientist 

2021 Median Pay: $94,739 

Process development scientists oversee the manufacturing process in an organization’s lab, looking for ways to increase quality and efficiency. Once a new product has been developed and approved for manufacturing, these scientists develop methods to scale production while adhering to standardized protocols. 

Biomanufacturing Specialists 

Median Pay: $80,629 

Biomanufacturing specialists use tools and methods to guarantee products meet requirements of purity, safety, potency and quality throughout the manufacturing process. It often involves the large-scale production of proteins used to treat or cure human diseases, which requires that these specialists possess a thorough knowledge of federal, state, and industry regulatory standards. 

Business Development Manager 

Median Pay: $123,065 

Business development managers give detailed market analysis to help companies formulate and execute growth and investment strategies. They help with assessing and pursuing expansion, acquisition, and collaborative research and partnering opportunities with other biotechnology institutions to achieve business growth in line with corporate goals. 

Director of Product Strategy/Commercialization 

Median Pay: $129,939 

Biotechnology professionals in these business roles handle the development and execution of the commercialization strategy for new products including launch, market development, marketing and sales, driving growth and profitability while ensuring compliance with regulatory and quality requirements. 

Looking for Tech Internships? The Pitt Computer Science Club can Help!

The University of Pittsburgh Computer Science Club has compiled a list of tech internships on Github for Summer Internships! Go through the links to explore the various openings. They also have a list of new Grad Applications if that interests you instead. All in all, this is an excellent resource for job and internship search.

Career Highlight: Data Analyst

Adapted from northeastern.edu and snhu.edu 

Job Duties: 

Responsibilities of data analysts will vary depending on the type of organization and the extent to which a business has adopted data-driven decision-making practices.  Usually, the responsibilities of a data analyst include: 

  • Designing and maintaining data systems and databases; this involves fixing coding errors and other data-related problems.  
  • Mining data from primary and secondary sources then reorganizing it in a format that humans or machines can easily read. 
  • Using statistical tools to interpret data sets, highlighting trends and patterns important for diagnostic and predictive analytics efforts. 
  • Preparing reports for leadership that effectively communicate trends, patterns, and predictions using relevant data.  
  • Collaborating with programmers, engineers, and organizational leaders to improve processes, recommend system modifications, and develop policies for data governance. 
  • Creating documentation that allows stakeholders to understand the data analysis process and duplicate or replicate the process if necessary. 

Data Analyst vs. Data Scientist vs. Business Analyst 

When reading about data analysts, you might also be wondering about the similarities and differences between other related careers like data scientist and business analyst. The differences between what they do comes down to how the three roles use data: 

  • The data analyst gatekeeps an organization’s data so stakeholders can understand and use data to make strategic business decisions. It is a technical role that requires an undergraduate degree or a master’s degree in analytics, computer modeling, science, or math. 
  • A business analyst serves a strategic role focused on using the data analyst’s information to find problems and propose solutions. These analysts typically earn a degree in a major such as business administration, economics, or finance.    
  • The data scientist takes the data visualizations created by data analysts and sifts through them to find weaknesses, trends, or opportunities to organize the data. This role also requires a background in math or computer science, along with some study or insight into human behavior to help make informed predictions. At startups and other small organizations however, it is not uncommon for a data analyst to take on some of the predictive modeling or decision-making responsibilities that may otherwise be assigned to a data scientist.   

Education and Training: 

A bachelor’s degree is necessary to get started in the field. While many people begin a data analytics career with a degree in math, statistics or economics, Lawrence’s Statistics and Data Science minor would be a nice supplement to any student with a quantitative skillset who is interested in a post-graduate role as a data analyst. 

Within a bachelor’s program, you may wish to explore courses in mathematics while also pursuing classes and research projects focused on data mining, simulation and optimization. You can learn to find and define data challenges across industries, gain hands-on practice collecting and organizing information from many sources and explore how to examine data to find relevant information.  

A master’s in data analytics can further your career, exploring how to use data to make predictions and how data relates to risk management. This also helps you dive deeper into data-driven decision-making, explore project management and develop communication and leadership skills. Finding an internship can give hands-on experience that helps you stand out when applying for data analyst jobs. 

Pay and Job Outlook: 

The average salary ranges from approximately $60,000 to $138,000. Roles at financial and technology firms tend to pay higher than average. The data analyst role can also act as a gateway for more senior data-driven jobs. According to PayScale, data analysts move on to roles such as senior data analyst, data scientist, analytics manager, and business analyst which also come with substantial increases in pay. According to IBM, the annual salary of data scientists will start at nearly $95,000, while analytics managers will make nearly $106,000 per year. Moreover, demand for mathematicians and statisticians is projected to grow by 33% and jobs for database administrators are expected to grow by 8% through 2030 according to the BLS. 

Intro to R

Adapted from simplilearn.com and psychologicalscience.org 

R is an open-source programming language often used as a data analysis and statistical software tool. It’s particularly useful for machine learning operations and data wrangling. If R is widely used, what are some potential advantages and drawbacks and is it really worth learning? 

Here are some advantages: 

  • It’s open source. No fees or licenses are needed, which is why if you’re developing a new program, it’s a low-risk venture. 
  • It’s platform-independent, which means that it runs on all operating systems. This allows developers to only create one program that can work on competing systems. This is also why R is cost-effective. 
  • R is great for statistics – it can do many things from regression equations to frequentist and Bayesian statistics. It can bootstrap, simulate, randomize and resample your data which is why it’s widely used in the data sciences. 
  • It’s well suited for Machine Learning. R is ideal for machine learning operations such as regression and classification.  
  • R lets you perform data wrangling which involves turning unstructured, messy data into a structured format. This involves merging data sets, cleaning data and identifying important rows or columns. R also creates formatted tables, complete with significance stars. For this reason, R is often used in financial tech industries and in academia.  
  • It visualizes data well because there are packages dedicated to making pretty plots.  

Drawbacks include: 

  • It has a steep learning curve. It’s best suited for people who have some previous programming experience. 
  • It’s not as secure. R doesn’t have basic security measures. Consequently, it’s not a good choice for making web-safe applications and it can’t be embedded in web browsers. 
  • It’s slow. R is slower than other programming languages like Python or MATLAB. 
  • It has bad memory management, so it takes up a lot of memory when running code. R’s data must be stored in physical memory however, the increasing use of cloud-based memory may eventually lessen the negative effects of this drawback.  
  • It doesn’t have consistent documentation/package quality. Docs and packages can be patchy and inconsistent, or incomplete because it doesn’t have official support and instead is maintained and added to by the community.  

Some stat classes at Lawrence already teach people how to use R like STAT 255, CMSC 205 and ECON 380. You can also install RStudio yourself since it’s free and use online teaching and coding resources like Datacamp to do so as well.  

Career Spotlight: Computational Scientists

Adapted from pennstateuniversity.edu and energy.gov 

A computational scientist is someone who uses scientific computing in applied disciplines such as physics, chemistry, biology, or the social sciences to analyze, clean up and calibrate large amounts of data and create computer models or simulations to create artificial data to solve problems and inform decisions. Because computational scientists primarily work with data, models, and simulations, they can be scientists, statisticians, applied mathematicians or engineers. 

Job Duties  

Computational Scientists work primarily with research. Their job duties primarily involve 

  • Analyzing and interpreting data  
  • Applying computer science procedures to a variety of situations and recommending potential solutions 
  • Designing experiments and developing algorithms  
  • Identifying relationships and trends or any factors that could affect the results of research 
  • Coordinating with research faculty and other technical team members for needs assessment and to accomplish individual project and/or larger organizational goals 
  • Co-authoring papers, proposals, presentations and reports  
  • Maintaining external research collaborations 

Later into one’s career, computational scientists may take on more managerial and mentorship roles as they become in charge of projects and mentor others like grad students in academic settings or new hires in tech companies.  

Working Conditions 

Computational scientists are typically researchers at academic universities, national labs and tech companies because data analysis, creating computational models and simulations are all skills that can be easily used in multiple disciplines. Often, they will need to work with in academically or professionally diverse teams and communicate clearly with researchers from their own or other institutions or clients and executives with non-technical backgrounds if they want to talk about their results. When working for academia or in national laboratories, it may be necessary to travel to research conferences to present their research.  

Education and Training  

Depending on the work, the education requirements vary from a bachelor’s degree to a PhD in disciplines related to what you are applying for. For example, jobs that focus on modeling Earth Systems might require a PhD in either Earth Sciences, Oceanography, Computer Science or any related field. However, jobs that need computational scientists because they need someone to facilitate deeper understanding or shorter time for research then, at the lower levels, a bachelor’s degree may do. Financial companies may want an Economics or financial background. However, prior experience is strongly recommended, even at entry levels for most jobs. 

Pay and Job Outlook 

According to the Bureau of Labor Statistics, Computer and Information Research Scientist jobs are expected to grow by 22% and their median salaries were $126,830 in May 2020. The lowest 10 percent earned less than $72,210, and the highest 10 percent earned more than $194,430 with the top three industries being software publishers, research and development in the physical, engineering, and life sciences and computer systems design and related services. 

More Information 

Here is our list of sources you can go through, if you would like to know more: 

Department of Energy’s Career Map on Computational Scientists 

Computing in Science and Engineering Article on How to Become One