Finance on Stats and R

10 potential career options with a degree in statistics

Fri, 24 Mar 2023 00:00:00 +0000

This post has been written in collaboration with Daniel Williams.

Introduction

A reader recently contacted me because he was hesitating between starting his studies in statistics or economics. I studied economics and I am now doing a PhD in statistics, so he reached out to me to know what I thought about these two fields, and in particular, what were my feelings about the career opportunities available with these two degrees.

Before considering pursuing a graduate degree, it is important to have a clear understanding of the skills you will develop and the career opportunities that will become available to you.

I leave it to those who have studied economics and work in this field to discuss career opportunities with a degree in economics. In this post, I will focus on the career options that are available for people with a degree in statistics.

Bear in mind that there are many professional opportunities with a degree in statistics. Indeed, statistical expertise is highly sought after in a wide range of computing and data analysis job roles; as soon as there are data, statistical expertise is required. Moreover, there are almost as many different jobs as people. Therefore, the list of jobs below is non-exhaustive and you may get a job that is not mentioned in this list. However, I hope it will still give you an overview of what you can expect after your studies.

If you believe I missed one—if you studied statistics and your job is not mentioned here for example, feel free to leave a comment at the end of the post.

What types of jobs are available?

If you are skilled in statistics and R programming, there are a plethora of job opportunities available to you.

Some of the roles you can pursue include:

Statistician: Collect, analyze and interpret data in a wide range of fields, including economics, finance, marketing, social and medical sciences.
Data scientist, data/business analyst, data engineer or machine learning engineer: Collect, analyze and interpret large and complex data sets using statistical and machine learning techniques to gain insights and inform decision-making to business and organizations. Mastering these techniques is key to the role, and in industry, turning statistical models into production-ready solutions often requires collaboration with a professional machine learning development company.
Actuary or actuarial analyst: Use statistical modeling to assess risk and uncertainty, and to create financial projections for insurance and investment companies.
Financial (risk) analyst, investment analyst, financial trader, financial manager or quantitative analyst: Use statistical and mathematical modeling to analyze financial and market data, identify and evaluate risks for investment and trading decisions.
Business intelligence analyst: Use data analysis and visualization tools to create reports and dashboards that help businesses make informed decisions.
Operational researcher or quality control analyst: Use mathematical and statistical techniques to monitor and optimize the quality of products and processes in manufacturing.
Market or survey researcher: Conduct research and surveys, gather and analyze data to help businesses develop and improve their marketing strategies.
Economist or econometrician: Use statistical methods to analyze (socio)economic data and develop economic models to inform policy and decision-making.
Teacher: Facilitate learning and provide guidance to students in order to help them acquire knowledge, skills, and values that will prepare them for their future.
(Freelance) consultant: Provide statistical consulting services to clients, helping them solve problems using a combination of statistical methods and business expertise.

In the following sections, we delve deeper into the aforementioned job roles and outline the qualifications expected of prospective candidates.

Statistician

As a graduate in statistics, you have the potential to land a coveted statistician’s job in a reputable private company or public agency. These types of roles typically involve analyzing and assessing data, and utilizing various tools and software to manage data effectively.

With data proliferation and more and more companies/organizations that rely heavily on data analysis, your skills will be valuable in a broad range of fields and industries. Being a statistician means that you could work for instance as an:

environmental statistician: analyze data related to environmental issues, such as climate change and pollution, to inform policy and decision-making,
sports statistician: analyze data related to sports performance, such as player statistics and game outcomes, to inform coaching and strategy decisions,
government statistician: collect, analyze and interpret data for government agencies and public departments to guide policymakers in their decisions,
biostatistician: use statistical methods and data analysis techniques to study and interpret medical and (public) health-related data,
etc.

Statistical expertise is required wherever there is data, so feel free to specialize in the field or industry you care about most!

Data scientist, data/business analyst, data engineer or machine learning engineer

As a relatively new job role in various organizations, data scientists and data analysts are experts in manipulating, analyzing and interpreting data. These roles typically require the analysis of large datasets using statistical methods to assist organizations in making more informed business decisions. These professionals dive into unstructured information to uncover valuable insights for businesses, ultimately increasing their revenue.

To pursue this career, one would usually require a graduate degree in science, statistics, or mathematics, as well as training in data mining. It is also expected that you are familiar with one or several programming languages such as R, Python or SAS.¹

Statistics graduates can also pursue their career as machine learning engineers. Job roles in this field typically involve conducting experiments and implementing machine learning algorithms. Consequently, many organizations hire these professionals to create a wide range of AI products. To do so, they must possess strong programming and statistical skills. Data science and software engineering knowledge are also beneficial.

Actuary or actuarial analyst

Actuaries usually apply statistical modeling tools on data related to retirement, financial/insurance products, accidents or mortality. As an actuary, one would be responsible for analyzing financial costs and risks. It means examining uncertainties associated with investments and other financial products and making predictions about the risks involved in a particular venture.

Actuaries are also in charge of writing insurance proposals, determining policy terms, and calculating premiums for different products, among other responsibilities. You may also play a vital role in deciding whether to accept or reject insurance applications by evaluating their risks. You will draw upon your statistical, actuarial, and background information to accomplish these tasks.

Typically, actuaries have a range of financial companies as clients, and they must identify potential risks and recommend compensation strategies accordingly. It means that their work is closely related to insurance products, as they must assess the likelihood of certain events occurring and the resulting financial impact.

Typically, those who pursue this role hold a degree in actuarial science, mathematics or statistics, and are trained in statistical analysis tools and software. Note that each country has its own specific accreditation related to this profession.

Financial (risk) analyst, investment analyst, financial trader, financial manager or quantitative analyst

As a financial analyst, one is required to gather data from various sources, organize and analyze historical results, and make projections and forecasts. Furthermore, in addition to analyzing data, their role often involves reporting financial results to the board of directors/management to help them in setting the overall strategy and direction of the company.

Investment analysts are experts in evaluating different financial assets such as stocks, securities, and bonds. They do not stop there, though. They also conduct research and make crucial decisions about purchasing business financials.

As a financial trader, you will have a keen understanding of financial markets and the ability to execute trades (i.e., buy or sell shares, bonds, and other assets) on behalf of clients. Note that there are also various sub-roles to explore within this job segment.

Financial managers combine their passion for finance with their love of numbers and statistics. Financial managers are experts at creating and interpreting complex financial reports, providing invaluable insights into a company’s financial health. They can also advise management on investment strategies and assist with vital financial decisions. With their keen analytical skills, financial managers keep a watchful eye on daily financial activities and help ensure a company’s long-term success.

These skills are in high demand in financial organizations, such as banks, actuarial firms, insurance companies (commercial insurance, reinsurance, general insurance, life insurance, car insurance, etc.), and other similar establishments.

The educational background of these professionals is diverse. Graduates in finance, mathematics, and statistics all bring unique perspectives to the table.

Business intelligence analyst

A business intelligence (BI) analyst is responsible for collecting, organizing, analyzing, and presenting large amounts of data from various sources such as databases, spreadsheets, and software applications. They use various tools and techniques to identify trends, patterns, and relationships in the data to create reports, dashboards, and visualizations to communicate their findings to stakeholders. They may also be responsible for monitoring business performance metrics and KPIs, identifying areas for improvement, and making recommendations to optimize business operations.

Additionally, BI analysts may also be involved in the development and implementation of data-driven software applications, such as business intelligence platforms and data warehouses, to improve data accessibility, accuracy, and efficiency. Overall, the role of a BI analyst is crucial in filling the gap between business analysts and the IT team.

The most common software tools and platforms used by BI analysts are, at the time of writing this post, R Shiny, Microsoft Power BI, Tableau, QlikView and SAS BI.

Operational researcher or quality control analyst

Operational researchers are responsible for using mathematical and analytical methods to solve complex problems and optimize business operations. They collect and analyze data, develop models and algorithms, test and validate them, and make recommendations for improvements.

On the other hand, quality control analysts are responsible for ensuring that products or services meet the required standards of quality. They monitor and analyze product or service performance, identify areas for improvement, and develop strategies for maintaining or improving quality. They may also work with production teams to develop and implement quality control processes and procedures.

While operational researchers focus on optimizing processes and systems, quality control analysts focus on ensuring that the end product or service meets the required quality standards. Both roles require analytical skills and the ability to work with data, but operational researchers focus more on mathematical modeling and optimization, while quality control analysts focus more on monitoring and improving quality.

Individuals who pursue job roles of this nature typically possess strong mathematical abilities, often having graduated with degrees in statistics or mathematics. They are trained to analyze vast amounts of data and are well-versed in various analytical tools, such as simulation, mathematical modeling, and data science.

Market or survey researcher

Market or survey researchers are professionals who specialize in conducting research to gather information about consumer behavior and preferences, market trends, and competitive landscapes. They use various research methods and techniques to collect and analyze data, and provide insights and recommendations to businesses and organizations.

Market research professionals typically work with marketing agencies on various projects for clients across different sectors. In addition to a degree in statistics, knowledge in marketing and familiarity with the industry is beneficial.

Economist or econometrician

Economists and econometricians study and analyze economic systems, markets, and policies using quantitative methods and models. They collect and analyze data, develop economic models, conduct economic analysis, provide recommendations to policymakers and organizations, and communicate their findings to a variety of audiences. Their role is to inform economic policies and decisions in both public and private sectors.

If you have a background in statistics in addition to knowledge in economics, there is a whole world of vocational opportunities in economics just waiting for you. Indeed, armed with the ability to appropriately analyze data (thanks to your background in statistics) and the ability to understand (socio)economic issues and financial data, you will be a hot commodity as an advisor to governments and businesses on all economic decisions.

(Freelance) consultant

From a general point of view, a consultant (or freelance consultant) is a professional who provides expert advice and guidance to organizations or individuals on a contractual basis. They analyze problems, develop solutions, and provide recommendations to clients in a specific field or industry.

A consultant specializing in statistics provides expert advice and guidance to organizations or individuals on statistical analysis, data management, and modeling. They use statistical tools and techniques to analyze and interpret complex data, and provide insights and recommendations based on their analyses. The role of a statistical consultant may include identifying research questions, designing studies, collecting and managing data, analyzing data using appropriate statistical methods, interpreting results, and presenting findings to clients. They may also assist clients with the implementation of statistical methods and tools, provide training on statistical software and techniques, and develop custom statistical models for specific applications.

As a side note, note that many consultants in statistics provide their service as data visualization consultants. These consultants are responsible for helping clients to effectively communicate complex data through the creation of clear and visually appealing graphics, charts, and other forms of visual representation that can aid in decision-making and enhance understanding.

You can either be a consultant in a consulting firm, or a freelance consultant. If you choose to be a freelance consultant, you can more easily select the projects you want to work on and the industry you want to focus on, but you will also need to take care of all the administrative tasks such as finding and building relationships with clients, do your bookkeeping, etc.

If you are not ready to be a full-time freelance consultant but would like to experience a taste of it, you can always keep your primary job and accept a couple of side projects. This is known as a side hustle. This way, you will gradually build your portfolio and create relationships with clients, all that with the advantage of keeping a safe source of income. This is what I am doing with datanalyze.be, and I recommend it to anyone who is considering the option of being a freelance in the future.

Teacher

If you care about education, you like to transmit your knowledge and explain complex things in a simple manner, you may be interested in being a teacher in high school or a university professor. With a degree in statistics, you may be eligible to teach different subjects such as mathematics, statistics, physics, or science in general. Your ultimate goal is to educate and inspire students to develop an understanding and appreciation of these subjects (which are often not well appreciated by students), as well as to develop critical thinking skills, problem-solving abilities, and scientific literacy.

Like actuaries, note that each country has its own specific accreditation related to this profession.

To know whether this job suits you, you can start by being a private tutor. This way, you will experience what the job is like and you will be more able to tell whether it is the direction you want to take.

To become a university professor, note that a PhD (and even sometimes a postdoc) is usually required. This brings me to the last career option I would like to mention: a PhD.

I do not include a PhD in the list of the 10 potential career options because it is more seen as an additional degree rather than a job, and even for those who see it as a job, it is a temporary one. However, I would still like to mention it because it is worth considering with a degree in statistics (I am of course biased, but my biased opinion may be of interest to some readers).

PhD student

For those who enjoy academic research, pursuing a career as a researcher after taking a statistics degree may be a worthwhile consideration. Typically, pursuing a PhD in statistics involves working in close collaboration with one or two university professors (your supervisors) on a specific topic.

I cannot speak for all PhD students, but I can speak from my experience. It involves conducting original research in statistical theory and methodology, developing new statistical models and methods, and applying statistical techniques to solve real-world problems or to advance knowledge in a specific field. My research focuses on applying biostatistical procedures to cancer patients, so it is a rather applied PhD, but many of my colleagues work on a more theoretical subject.

We also sometimes participate in academic conferences, present our research findings, and collaborate with other researchers in our field of research.

After completing our PhD, we can continue to evolve in academia (and become a university professor for instance), or decide to work in a private or public research organization. Because professionals with a PhD in statistics become experts in statistical theory and methodology, commercial and public organizations often seek such profiles.

The length of a PhD depends on the country and the type of contract. If you are interested to know more, feel free to contact me, I might be able to help you (at least with how it goes in Belgium). Otherwise, the program director at your university will definitely be able to answer the questions you may have.

Conclusion

As you have seen, with a degree in statistics and knowledge in programming, you will be able to pursue positions at various companies or organizations. You can also always enhance your qualifications with additional courses if you feel that you miss some skills that are required for the job you want to apply to.

Statisticians are in demand in government departments across many regions and sectors. Actuarial firms, banks, investment firms, and market research organizations are also great potential employers. The demand for statistical and analytical skills is on the rise across various fields. Job seekers with these skills are particularly needed. However, it is becoming increasingly common for these positions to require specialized education and training. For example, if you have a degree in statistics, pursuing a finance specialization could open doors to opportunities in banking, investment, accountancy, or insurance firms. Last but not least, those passionate about solving complex problems may find roles as researchers (within or outside academia) appealing.

Thanks for reading.

I hope this article helped you to get an overview of the career options you have with a degree in statistics.

If you are looking for a job, I wish you the best of luck in finding your dream job! Remember that you will find a variety of job openings in academic, research-oriented, and analytical fields on online and offline job boards. Moreover, do not underestimate the power of friends, colleagues and family: make sure to network with people in your field to increase the chance of getting a job.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

There are many programming languages for data analysis, and it will certainly continue to evolve in the future. The most common ones at the time of writing this post are: R, Python, SAS, Jamovi, SPSS, JMP, Stata and Matlab. Depending on the role and the organization, more or less programming skills will be required.↩︎

Mortgage calculator in R Shiny

Fri, 14 Aug 2020 00:00:00 +0000

Introduction

I recently moved out and bought my first apartment. Of course, I could not pay it entirely with my own savings, so I had to borrow money from the bank. I visited a couple of banks operating in my country and asked for a mortgage.

If you already bought your house or apartment in the past, you know how it goes: the bank analyzes your financial and personal situation and make an offer based on your propensity to repay the bank. You then either accept the offer if you are satisfied with the rate and conditions, or visit another bank if you believe you could receive a better offer. Mortgages and loans are more complicated than that of course, but let’s keep it simple here.

As I kind of like to control and keep a close eye on my personal finances (sometimes a bit too close I must admit), I knew precisely how much I could spend for my monthly mortgage repayment while still being able to cover my living expenses. However, I had no clue how much I could borrow in total for my new apartment given these housing repayments.

I knew I was not the first person in this case, so I looked online if I could find a R script which would answer my question (and potentially also give me the total cost of the housing loan, including the loan amount and the accumulated interests). I finally found a R script created a while ago by Prof. Thomas Girke.

Mortgage calculator

The function in the script was functional and solved my main issue, but I wanted to be able to play more easily with the different settings such as the amount, the duration and the interest rate of the loan.

For this reason, I created a R Shiny app which is available here:

Mortgage calculator

Mortgage calculator in R Shiny

In the meantime, I received an Excel file from a friend working in a Belgian bank which does precisely the same task. I am not an actuary nor an expert in mortgage loan, so with his file I was able to cross check the results and edit the code accordingly.

The app greatly helped me to know the maximum amount I could borrow from the bank by playing with the three main settings of a mortgage, so it gave me a precise price limit when looking for apartments online.

Note that the app can of course be used for any loan, not only for mortgage.

How to use the mortgage calculator?

First, you can find the mortgage calculator here.

I try to keep all my Shiny apps easy to use for everyone. However, here is how to use it in case it is not intuitive enough:

Enter the amount of the loan (i.e., the amount you would like to borrow, do not include downpayment)
Enter the annual interest rate in %
Enter the duration of the loan in years

On the right panel (or bottom if you use the app on mobile) you will see:

a summary repeating the settings you entered,
the total cost of the loan (principal and interests included), and more importantly
the amount of the monthly payments

A plot representing the percentage attributed to the repayment of the interests and the capital is also displayed. You see that (especially in the first years of the loan), the higher the interest rate and the duration of the loan, the higher the percentage of the monthly repayments is attributed to the repayments of the interests.

Finally, the amortization table showing the remaining balance month by month is displayed after the summary and the plot. You can copy, export (in PDF, CSV or Excel) or print this amortization table for further use.

Code of the app

Here is the entire code (or see the last version on GitHub) in case you would like to enhance it (feel free to send me your app if you happen to improve it!).

Conclusion

Thanks for reading.

I hope this mortgage calculator helped you to play with the different settings of a mortgage, and who knows, helped you to decide which house or apartment to buy.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

Disclosure: Note that this application does not include investment advice or recommendations, nor a financial analysis. This application is intended for information only and you invest at your own risks. I cannot be held liable for any decision made based on the information contained in this application, nor for its use by third parties.

A practical guide on optimal asset allocation

Tue, 07 Jan 2020 00:00:00 +0000

UPDATE: Due to the limitation in terms of maximum number of Shiny apps that can be published on the free shinyapps.io plan, the Shiny app presented below has been unpublished. However, the code can be found on GitHub.

Introduction

In his book A Random Walk down Wall Street, Burton G. Malkiel advises readers of an optimal asset allocation depending on age. As an amateur investor, I thought it would be useful to develop a Shiny app which depicts his advice for other interested investors. Here is the link to the app:

Optimal asset allocation

How to use this app?

Open the app via this link
Set your age under How old are you?
Indicate how much you are willing to invest

On the right panel (or below depending on the size of your screen) you will see a table and a barplot. The table includes:

the different type of assets which represent the different allocations
the most common tickers for each type of asset
the optimal percentage for each asset
the optimal amount for each asset

In the table, the optimal percentage depends on your age (the older you are, the more conservative your portfolio), while the optimal amount depends on your age and the amount you are willing to invest.

The barplot displays the optimal weight (so the optimal percentage of your portfolio) for each type of asset: stocks, bonds, real estate and cash. This is just a visualization of the column “Optimal percentage” displayed in the table above.

It is also possible to compare your portfolio with the optimal portfolio, as recommended by the author. For this comparison, click on the tab “Compare with your portfolio” above How old are you?. In this tab:

Again, indicate your age
Then indicate your portfolio’s value for each asset (if you do not own a specific asset, leave it equal to 0)

On the right panel (or below depending on the size of your screen) you will see your portfolio’s total amount and a barplot comparing your portfolio and the optimal portfolio according to your age. This visualization will instantly give you a clear overview of what type of asset is overrepresented and underrepresented in your current portfolio compared the the optimal one. This will allow you to easily see how to rebalance your assets in accordance to what the author suggests.

Code

Here is the entire code (or see the last version on GitHub) in case you would like to enhance it.

Note that the link may not work if the app has hit the monthly usage limit. Try again later if that is the case.

Disclosure: Note that this practical guide on asset allocation is based on the book A Random Walk down Wall Street by Burton G. Malkiel. This application does not include investment advice or recommendations, nor a financial analysis. This application is intended for information only and you invest at your own risks. I cannot be held liable for any decision made based on the information contained in this application, nor for its use by third parties.

Conclusion

Thanks for reading.

I hope you will find this app useful to quickly replicate the optimal asset allocation suggested by Burton G. Malkiel in his book A Random Walk down Wall Street. If you want to have more information about this investment strategy, I suggest reading his book.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.