#TD

Tag: #TD

AI – Efficiency Tools to improve your chances of getting hired

A big part of job searching and applying for jobs is being able to use the resources you have available to your advantage. Over the last few years, I’ve learned about and used many different types of software to try to make this process easier. Artificial intelligence (AI) tools have grown in popularity in recent years due to their ability to provide valuable assistance in a wide range of tasks. Here are four free AI tools that I have previously used to help you stand out from the crowd and improve your chances of landing your dream job:

  1. Quillbot: This AI tool allows users to rewrite sentences in a more fluent, efficient way that sounds more professional. This can be especially helpful when writing a resume or cover letter, as it can help you present yourself in a more polished and sophisticated manner. Visit Quillbot’s webpage.
  2. Canva: Canva is an excellent tool for creating visually appealing marketing materials, such as LinkedIn header images or social media posts. By using Canva to create a professional-looking header image, you can make a strong first impression on potential employers and showcase your personal brand. Visit Canva’s webpage.
  3. VMock: This software is specifically designed for Lawrence students looking to create a resume from scratch or improve their current resume. VMock provides a score on how your resume compares to other Lawrence students’ resumes and offers feedback on areas for improvement. Visit VMock’s webpage (sign in with your Lawrence Email)
  4. ChatGPT: ChatGPT is a strong artificial intelligence platform that allows users to interact with it as if it were a person. By submitting your CV to ChatGPT, you can request a summary of your previous experiences or suggestions on how to improve your wording. ChatGPT may also assist you on ways to better highlight your skills and accomplishments, as well as how to tailor your CV to a certain position or industry. Visit ChatGPT’s webpage.

Whether I needed to rewrite my resume in a more professional manner, generate a visually appealing LinkedIn header image, or obtain feedback on my resume, I discovered how much these AI tools had helped me, and my hope is that after you read this article you will be able to benefit from them as well.

Don’t hesitate to reach out or schedule an appointment with me if you have any questions about any of these powerful resources, or if you need help with your process of looking for a job or an internship.

Oliver De Croock ’24, Student-Athlete at Lawrence University majoring in Economics and Career Peer Educator. Connect with me on LinkedIn.

Start building your résumé using VMock!

Lawrence University has partnered with VMock to help you create a powerful résumé and accelerate your career journey. VMock SMART Résumé platform leverages technologies like data science, machine learning, and natural language processing to provide instant personalized feedback on your résumé and interview based on criteria gathered from employers and global best practices – from anywhere, at any time of the day. 

Simply login into VMock dashboard, upload your résumé, and VMock will: 

  • Give you an aggregate résumé score to assess the strength of your résumé benchmarked against your own community peer group 
  • Provide you with résumé guidelines based on Career Center standards to ensure that you do not miss fine details and establish a great first impression 
  • Assess how well you have marketed your core competencies to showcase the right skill set reflected in academics, experience, service, achievements, etc. 
  • Give you line-by-line suggestions to improve your résumé content in view of your academics and level of experience 

VMock is designed to work with résumé from many different fields across various industries. Once you receive the detailed feedback, make relevant changes to the résumé and re-upload it to see the increased score and associated improvements.  

Don’t have a résumé, yet? No problem. Log into VMock and start creating a résumé from scratch.

Check out the VMock Video to learn more.

Oliver De Croock ’24, Student-Athlete at Lawrence University majoring in Economics and Career Peer Educator. Connect with me on LinkedIn.

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.  

What Can I Do With a Computer Science Degree? (Part 2)

In Part 1 of our series “What Can I Do With a Computer Science Degree?”, we started looking at the kinds of jobs you can do with a Comp Sci degree and what are the main differences between these options. Because of how broad computer science’s applications are, there are many careers. Here are some more options for you to explore! 

5) Data Scientist:  

Data scientists create mathematical models to address real-world problems to help companies make decisions on anything ranging from how to reduce workplace accidents to how they should market their products.   

Programming languages many data scientists use include Python, R and Java as they’re good for analyzing and visualizing data and SQL which is used for database management. Other important tools they need to know how to use include Hadoop (an open-source software used to work with big data), SAS (suite of software products used to do data management and analysis for business insights), data mining and warehouse where data mining is the process of looking through big datasets and data warehouse is a system created for data analytics. And they need to be familiar with machine learning which examines models and algorithms to analyze large datasets.   

Soft skills include analytics and good problem-solving skills because data scientists need to understand and analyze their data well to see how they can use that information to solve problems. Clear writing and public speaking skills are also necessary because data scientists will need to explain their findings and interpretations to clients, employers and other team members. Being business-focused is also useful as many employers seek data scientists to help them improve their business strategies. 

6) Web Designer:  

Website designers plan and create engaging websites that look aesthetically pleasing and help site users find what they need. Once they finish their creations, designers pass their ideas to web developers who bring the plans to life. However, some designers double as developers and can create websites after designing them. 

Web designers typically need to know how to use JavaScript and HTML as a lot of design software relies on them. Knowing how to use software like Adobe Photoshop and Adobe Dreamweaver is also useful because they are the industry-standard programs for many web designers and allows them to work with other professionals, like developers and project managers, to complete their websites. User interface design is necessary as it allows designers to see their creations through the eyes of an end user with no design experience or helps them make the website accessible.  

Other necessary skills include using software like Adobe Illustrator and Adobe InDesign. CSS is a style sheet language that is used alongside HTML to change aspects like fonts, layers and colors. Excellent graphic design skills can help web designers stand out from everyone else so honing these skills in addition to technical ones is important.  

7) Security Analyst:  

Information security analysts focus on data and network protection to protect their companies’ or organizations’ digital assets. To do so, they need to stay informed about changes in such a fast-evolving field. Information security analysts work with executives, IT teams, and colleagues across their organizations and sometimes train employees about best practices. They establish company security protocols, conduct tests to search for system weaknesses and develop response plans in case security breaches happen. Aside from the challenge of staying up to date with current technology, information security analysts may sometimes deal with stressful situations if a cyberattack occurs. 

Top employers include computer systems design and related services, finance and insurance, and information. Information security analysts usually need a computer science-related bachelor’s degree. With some companies looking for an MBA in information systems. Industry-standard certifications can boost employment prospects for professionals in the field. A security analyst’s job revolves around data and network protection.  

Important hard skills an analyst would need to have include knowing about industry-standard programs like Blackboard, Apache Ant, Symantec, and Django. They also need to know about various databases and software for development, programming, network monitoring, and virus protection. They must also write code to prevent and respond to cyberattacks and need a strong knowledge of how networks function to solve security problems. Key soft skills include strong problem solving, analysis, being attentive to detail and communication skills since they need to analyze and solve security problems effectively then communicate that information clearly to team members, executives and clients throughout the organization. 

8) Software Engineer:   

Computer software engineers apply engineering principles and systematic methods to develop programs and operating data for computers. They work with system programmers, analysts, and other engineers to obtain and apply important information for designing systems, projecting capabilities, and determining performance interfaces. They also analyze user needs, offer advice about designing elements, and help with software installation. Designing software systems requires professionals to consider mathematical models and scientific analysis to project outcomes.  

Programming languages a software engineer might need to be familiar with include Java, SQL, Python, JavaScript, C++ and C#. Other options include Ruby, Rust, PHP and Swift. If you refer to Part 1 of this series, I mentioned in the Software Developer part that different jobs require different languages and will specify their requirements, so learning 3-4 languages very well instead of 6 languages badly would make it easier for you to perform well in the coding interviews and during the job.  

Important soft skills include good communication and organization skills. Software engineers will often need to split attention across different parts of the same project or switch between projects when working on a deadline or to meet the team’s needs. Being attentive to detail is necessary too as they must troubleshoot coding issues and bugs as they happen and track details of many ongoing projects. 

9) Computer and Information Systems Manager/Systems Manager:  

Computer and information systems managers generally oversee the information technology departments within businesses and organizations. A systems manager’s duties depend on organization size and how much technology they use daily. In smaller settings, systems managers may offer support on an as-needed basis, while larger organizations may need larger IT departments with more hands-on systems manager roles. 

Important hard skills systems managers would need to know include network and IT management, which involve overseeing wireless networks, cloud storage, and other systems of data storage and communication and managing daily IT operations or providing support when needed. Project management is a necessary skill because you would be overseeing many IT-based projects like implementing a new computer system, teaching employees how to use a new piece of software or creating new data storage or recordkeeping systems. Knowing how to use MS Office well is necessary as Microsoft creates and manufactures most of the software used by businesses and organizations.  

Soft skills to develop include strong analytical and organizational skills. Leadership skills are important because computer and information systems managers guide the collective efforts of systems analysts, information security professionals, and software developers. As team leaders, they need to delegate, accept responsibilities and always be trustworthy and reliable. System managers need to write reports, instruction manuals and relay information to people with varying technical backgrounds in clear and understandable ways.  

Generally, systems managers hold at least a bachelor’s degree. Graduate education can increase earning potential and may open doors to paths to more advanced careers, but they aren’t necessary. 

If you want to have a deeper look at more specific aspects like salaries and other education requirements, you can check the careers page on computerscience.org. To get help on getting started with a job or internship search, resumes, or interviews or anything else feel free to make an appointment at the Career Center!