Opening Doors In Your Job Search With Statistics & Data Analysis – Guest Blogging on Simon Fraser University’s Career Services Informer
June 9, 2013 1 Comment
The following post was originally published on the Career Services Informer.
Who are the potential customers that a company needs to target in its marketing campaign for a new service? What factors cause defects in a manufacturer’s production process? What impact does a wage-subsidy program have on alleviating poverty in a low-income neighbourhood? Despite the lack of any suggestion about numbers or data in any of these questions, statistics is increasingly playing a bigger – if not the biggest – role in answering them. These are also problems your next employer may need you to adress. How will you tackle them?
The information economy of the 21st century demands us to adapt to its emphasis on extracting insight from data – and data are exploding in size and complexity in all industries. As you transition from the classroom to the workplace in a tough job market, becoming proficient in basic statistics and data analysis will give you an edge in fields that involve working with information. This applies especially to STEM (science, technology, engineering, and mathematics) and business, but it also applies to health care, governmental affairs, and the social sciences. Even fields like law and the arts are relying on data for making key decisions.
It is also important to learn how to analyze data using common software or programming languages that are used for statistics (most of the concepts can be explained on a chalkboard, but the execution and automation of those concepts to analyze data are almost always done on a computer). Basic data analysis can be done in Microsoft Excel or its free equivalents, like OpenOffice Calc or Google Docs Spreadsheet. Data are still commonly recorded in spreadsheets, so knowing how to use and manipulate them is a valuable skill, even if the statistics will be done in different software. Advanced statistics requires software or programming languages like SAS, JMP or SPSS, which are commercial; and R or Python, which are open-source.
Luckily, there are many ways to learn statistics and data analysis during the current movement toward free and accessible education online. Coursera has many courses at all levels of statistics – from basic data analysis for beginners to machine learning and statistical computing for advanced students and professionals. The Khan Academy has a series of short videos on Youtube that cover the topics in a standard introductory statistics course. The Massachusetts Institute of Technology (MIT) pioneered making its course materials available through its OpenCourseWare, and it has grown and improved steadily over the past decade. I have learned a lot from reading books that teach statistics using languages like R or SAS.
Of course, if you seek jobs that require certified training, you can take post-secondary courses in statistics. There are also certifications that you can pay to earn from commercial vendors like SAS, and these certifications are desired, if not required, by many employers.
To those who may be new to data analysis, statistics, or computer programming for statistics, I can tell you three things from my experience:
- Learning these skills can be very difficult and intimidating, especially at the beginning
- These skills require hard work to learn, especially the more advanced topics
- Acquiring these new skills is very rewarding, both intellectually and professionally
There are also very helpful communities on the internet where people volunteer to answer questions for others struggling with concepts or computer programming; I find such groups on LinkedIn to be the most helpful. However, it is very important to take the time to learn the material and try to answer the questions by yourself first by reading relevant books and instruction manuals or searching relevant key words on Google – this process could (and should) take some time, even a few hours. Eric Steven Raymond, a famous computer programmer and open-source advocate, wrote a good article called “How to Ask Questions the Smart Way” that I’d recommend checking out before seeking others’ advice.
Having studied and worked in an industrious city like Toronto for almost 2 years, I have seen a very high demand and a moderately low supply of statisticians in the job market; this is consistent with the broader global trend that statisticians will be in high demand for the foreseeable future. Even if you don’t plan on studying statistics in depth or becoming a statistician, many jobs across all industries now require applicants to have experience or be knowledgeable about data analysis, so having this qualification will definitely help you to get a good job.
As an extra note of advice to those who do want to become a statistician like myself, I encourage you to stay engaged about trends in this industry. Traditional statistical techniques like linear or logistic regression are still very useful in many situations, but are very limited in dealing with big data sets. Machine learning is already revolutionizing all industries, and will likely become even more important in the future. Even though I am now working happily as a statistician in the private sector, I still learn or re-learn concepts and practice coding regularly on my blog to prepare for future shifts in the qualifications that employers seek from statisticians.
Last but not least, statistics can be a lot of fun. Even at the basic level, you may marvel at the surprising insights or counter-intuitive results that you get from analyzing your data, and it may not always require very sophisticated techniques. Enjoy the process and the results, and enjoy the next great job that you will find by having learned some statistics and data analysis!
I am very pleased to announce my return as a guest blogger for Simon Fraser University’s (SFU’s) Career Services Informer (CSI), which published my first post earlier this week on how knowing statistics and data analysis can open doors in today’s job market. During my undergraduate studies at SFU, I volunteered as a Career Peer Educator at its Career Services Centre for 5 years; I was very fortunate to be a part of a great team of fellow volunteers and staff members who provided career advice for students and alumni on writing cover letters and résumés, preparing for job interviews, and networking to build one’s career and search for jobs. Soon after the Career Services Informer – a career-oriented blog – was created, I began blogging about my work experiences during my undergraduate degree in a series called Eric’s Corner. This fruitful series lasted for 3 years; I took the next few years to focus on finishing my undergraduate degree and moving to Toronto to pursue my graduate studies – all centred on my very rapid and sharp change toward statistics. Now that I have graduated from my Master’s degree in statistics from the University of Toronto and worked at Predictum for over a year, Jo-Anne Nadort, one of my former supervisors in the Career Peer Education program and a Career Advisor in SFU’s Career Services Centre, invited me to blog for the CSI again. It has been a pleasure to work with CSI’s editor, David Lindskoog, on my first post, which focused on the value of being proficient statistics and data analysis in today’s information- and data-driven economy. I will continue to share my advice and experiences about working and succeeding in STEM (science, technology, engineering, and math) in future posts in Eric’s Corner. Stay tuned!