Opening Doors In Your Job Search With Statistics & Data Analysis

Opening Doors In Your Job Search With Statistics & Data Analysis

  9464 reads

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!

Eric Cai

Eric Cai is a former Career Peer Educator at SFU Career Services who graduated in 2011.  He now works as a statistician at the British Columbia Cancer Agency. In his spare time, he shares his passion about statistics and chemistry via his blog, The Chemical Statistician, his Youtube channel, and Twitter @chemstateric. He previously blogged for the Career Services Informer under “Eric’s Corner” when he was a student.  You can read all of Eric's newer posts here.

Posted on June 05, 2013