A statistician is a person who works with theoretical or applied statistics. The profession exists in both the private and public sectors. It is common to combine statistical knowledge with expertise in other subjects, and statisticians may work as employees or as statistical consultants.
Statisticians apply statistical theories and methods to collect, analyze and interpret quantitative data. They may work for companies involved in market research and public opinion, for industries concerned with areas such as quality control and product development, and – frequently – for local, state and federal governments. Hard-core theoretical statisticians usually find themselves in research and academia.
WHAT DO THEY DO?
Depending on their level of experience, statisticians may be asked to:
Tackle data-related challenges assigned by management
Decide upon an appropriate strategy to collect data
Extract data from existing sources or instigate new procedures (e.g. customer surveys, science experiments, opinion polls, etc.)
Analyze and interpret data using statistical tools, algorithms, models and software (e.g. R, SAS, SPSS, etc.)
Design new statistical models and data collection tools if needed
Identify patterns, trends, and relationships within data
Present statistical reports and data visualizations for diverse audiences
Provide strategic recommendations/predictions and highlight any data limitations
Develop and maintain statistical tools, databases and programs
Regularly monitor data quality
Work closely with key team members and subject experts (e.g. computer engineers, scientists, IT support, etc.)
Responsibilities are dictated by job titles. Low-level statistical analysts are usually tasked with standard data analyses and supervised by higher-ups. Experienced applied statisticians may be able to propose projects to management, develop new products and processes, oversee statistical teams and work on their own research.
There are various requirements for a Statistician that they need to fulfil to pursue a career in being a Stitistician. The requirements are classified under three heads
EDUCATIONAL AND GENERAL QUALIFICATIONS:
Many colleges and universities offer undergraduate and graduate degree programs in statistics. A bachelor’s degree in statistics is not needed to enter a graduate program. However, significant coursework in statistics or mathematics is essential. Required subjects for a bachelor’s degree in statistics include differential and integral calculus, statistical methods, mathematical modeling, and probability theory.
Many colleges and universities advise or require students to take courses in a related field, such as computer science, engineering, physics, or mathematics. Candidates with experience in a related discipline are particularly desirable to many employers.
For example, training in engineering or physical science is useful for statisticians working in manufacturing on quality or productivity improvement. A background in biology, chemistry, or health sciences is useful for work testing pharmaceutical or agricultural products.
Because statisticians use and write computer programs for many calculations, a strong background in computer science is also helpful.
Opportunities for promotion are greater for people with master's degrees or Ph.D.s. Statisticians with a master's degree or a Ph.D. usually can design their own work. They may develop new statistical methods or become independent consultants.
Statisticians typically have an interest in the Thinking and Organizing interest areas, according to the Holland Code framework. The Thinking interest area indicates a focus on researching, investigating, and increasing the understanding of natural laws. The Organizing interest area indicates a focus on working with information and processes to keep things arranged in orderly systems.
If you are not sure whether you have a Thinking or Organizing interest which might fit with a career as a statistician, you can take a career test to measure your interests.
Statisticians should also possess the following specific qualities:
Critical-thinking skills. Statisticians use logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions, or approaches to problems.
Math skills. Statisticians use statistics, calculus and linear algebra to develop their models and analyses.
Problem-solving skills. Statisticians must develop techniques to overcome problems in data collection and analysis, such as high nonresponse rates, so that they can draw meaningful conclusions.
Speaking skills. Because statisticians often work in teams, they must be able to present statistical information and ideas so that others will understand.
Writing skills. Good writing skills are important for statisticians because they write reports explaining technical matters to persons without their level of statistical expertise.
Job prospects for statisticians are projected to be very good. An increasing number of jobs over the next decade will require high levels of statistical knowledge. Job opportunities are expected to be favorable for those with very strong quantitative and data analysis skills.
Graduates with a master's degree in statistics and a strong background in a related discipline, such as finance, biology, engineering, or computer science, are projected have the best prospects of finding jobs in their field of study.
Skills in Statistical Analysis, Data Analysis, R and Data Modeling are correlated to pay that is above average. Skills that pay less than market rate include Microsoft Excel.
An entry-level Statistician with less than 1 year experience can expect to earn an average total compensation (includes tips, bonus, and overtime pay) of ₹405,880 based on 26 salaries. An early career Statistician with 1-4 years of experience earns an average total compensation of ₹420,000 based on 41 salaries. A mid-career Statistician with 5-9 years of experience earns an average total compensation of ₹1,000,000 based on 16 salaries. An experienced Statistician with 10-19 years of experience earns an average total compensation of ₹1,100,000 based on 6 salaries.
Employees with Statistician in their job title in Bangalore, Karnataka earn an average of 76.9% more than the national average. These job titles also find higher than average salaries in Hyderabad, Andhra Pradesh (76.9% more) and Mumbai, Maharashtra (44.1% more). The lowest salaries can be found in New Delhi, Delhi (16.4% less) and Kolkata, West Bengal (3.7% less).
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Statisticians held about 27,600 jobs in 2012. About a quarter of statisticians worked for government, mostly at the federal level.
The industries that employed the most statisticians in 2012 were as follows:
Federal government17%Finance and insurance12Educational services; state, local, and private11State and local government, excluding education and hospitals9Health care and social assistance8
Federal statisticians are commonly employed at the Census Bureau, the Bureau of Economic Analysis, the National Agricultural Statistics Service, or the Bureau of Labor Statistics.
Statisticians who work for private businesses often work in teams with other professionals. For example, in pharmaceutical companies, statisticians may work with scientists to test drugs for government approval. In insurance companies, they may work with actuaries to calculate the risks of insuring different events.
Statisticians may travel occasionally to meet with team members, set up surveys and research projects, or oversee the collection of data.
Employment of statisticians is projected to grow 27 percent from 2012 to 2022, much faster than the average for all occupations. Growth is expected to result from more widespread use of statistical analysis to make informed business, healthcare, and policy decisions. In addition, the large increase in available data from the Internet will open up new areas for analysis.
A large amount of data is generated from Internet searching and the use of social media, smartphones, and other mobile devices. Businesses will increasingly need statisticians to organize, analyze, and sort through the data for commercial reasons. Analyses will help companies improve their business processes, design and develop new products, and advertise products to potential customers.
Statisticians will increasingly be needed in the pharmaceutical industry. An aging U.S. population will encourage pharmaceutical companies to develop new treatments and medical technologies. Biostatisticians will be needed to conduct the research and clinical trials necessary for companies to obtain approval for their products from the Food and Drug Administration.
Government agencies will also employ more statisticians to improve the quality of the data available for policy analysis. This occupation will also see growth in research and development in the physical, engineering, and life sciences, where statisticians' skills in designing tests and assessing results are highly useful.
Frequently Asked Questions;
Q: What are the top pros and cons of being a statistician?
A: Pros: As a statistician, you will likely be asked to provide your expertise to teams working in a wide variety of fields and sub-fields, so you will get to become a mini-expert in each problem you’re involved in. You engage the creative, analytical, and social parts of your brain simultaneously on a daily basis. You translate boring numbers into interesting stories. You quantify uncertainty, and when you find patterns and relationships, you are able to say, “This is real, this isn’t just random noise.” Human beings are super good at seeing patterns where there are none, and your role is to guard leaders and decision makers against that.Cons: Nobody knows what exactly you do (not their fault). The most popular response you’ll hear is “ugh, I hated the stats course I had to take in college.” Your peers in computer science are constantly inventing methods that have already been invented and published by your fellow statisticians 50 years ago.
Q: What kind of impact do statisticians have on the Wikimedia Foundation’s overall success?
A: I’m only in my second month there, but already I’ve observed how much data analysis plays into progress and decision making. Every decision that the Foundation makes has an effect on our community of users and volunteers as well as the community at large. When WMF chose to file a lawsuit against the National Security Agency and U.S. Department of Justice, that was and will be a community-impacting decision. So the decisions at WMF can’t be made willy-nilly; we need to have convincing evidence, and that’s where researchers and data analysts come in. Whether it’s A/B testing fundraising banners or new features, or disputing yellow press’ claims (investigation by my friend and colleague Oliver Keyes), statistical thinking and the role of statisticians are highly valued by the Foundation.
Q: Which skills or programming languages do you most frequently use in your work, and why?
A: The primary language/environment I work in is R. When I was working at NRP, where we did all of the neuroimaging analysis in MATLAB, I used R for everything else. We’re also huge fans of RStudio’s Shiny (a web application framework for R) and use it for our dashboards to give the teams easy access to daily metrics and KPIs. My enthusiasm for R is no secret as I am a co-host and producer of the R Talk podcast. A lot of my work at NRP and a good chunk of my work at WMF also requires me to write file/text processing pipelines, so Unix/Linux/Bash…scripting(?) is key to my work.The skills are the same ones I listed in response to a later question, but I want to add one and emphasize another. First, I want to add that data visualization is a tremendously useful skill to learn and maintain. The best plot tells a story and guides the viewer/reader to a conclusion that otherwise takes at least one paragraph to reach with text. Second, I want to emphasize how important it is to be able to interpret results to non-statisticians. Telling a biologist what the hazard ratio is from your survival analysis is useless if you don’t interpret it. Hazard ratios are so ridiculously inaccessible, that in 2014(!), a statistician published an article on reformulations of the hazard ratio to make it more interpretable. Whether people have found a use for those reformulations in practice is another story, but the point is that raw parameter estimates are useless without an accessible narrative (and an accompanying data visualization).
Q: What’s the biggest difference between statisticians and data scientists?
A: I will refer to an op-ed written by former advisor: Data science is statistics (Statistical Thinking: The Bedrock of Data Science by Dr. Joel Greenhouse). Anyone who says otherwise doesn’t know enough about the art and science of statistics, and that’s a person I wouldn’t trust to work with my data.
Q: What kind of person makes the best statistician?
A: From the same op-ed, “Good statistical thinking requires:– a nontrivial understanding of the real-world problem and the population for whom the research question is relevant – judgments such as those about the relevance and representativeness of the data – judgements about whether the underlying model assumptions are valid for the data at hand – judgements about causality and the role of confounding variables as possible alternative explanations for observed results – the ability to interpret and communicate the results of a statistical analysis so non-statisticians can understand the findings”The best statisticians don’t just throw their data into a black box (e.g., neural network) and rely on machine learning algorithms to do their work for them. Instead, they develop an understanding and intuition for the data they’re analyzing. The best statisticians are also great communicators. They can talk to their clients and collaborators through all the stages of an experiment or study. Those people may not be as well-versed in statistics but are usually experts in the subject matter, so it is crucial to work with them in unison.The best statisticians poke and prod at assumptions and aren’t afraid of being wrong. To see what I mean, play around with this interactive puzzle from The New York Times.
Q: How has the role of statistician changed over time? How do you see it evolving moving forward?
A: Statisticians have long been a key component of academia and research, and now we’re seeing that same propensity in the corporate world as everyone is realizing the value of data-driven decision-making and is trying to have at least one data scientist on their team. This had/has a few side-effects:- Statisticians have gained access to new types and amounts of data. Big Data, if you will, on transactions, customer purchasing/browsing histories, and web sessions. Every major web-based member of commerce out there is tracking and logging its users’ behavior and engagement. Gleaning insights from that is a nontrivial task. – Statisticians have become more involved in the data collection process and privacy discussions. You don’t need to know EVERYTHING. You can design your system to collect only the data that will be necessary to aid your decision-making process without sacrificing user/customer privacy and putting them at risk in case of a data breach. It’s large-scale experimental design. – Statisticians should be guides and make recommendations to the decision makers. Data should not be the be-all and end-all decision-maker. We talk a lot about “data-driven decision-making” when we really mean “data-informed decision-making,” because ultimately it should be a person making the decisions that impact other people.This last point is where I think data scientists could benefit tremendously from a formal education in statistical science, because that education (hopefully) involves learning and thinking about ethics. In school, we spent time reading about and discussing case studies with ethical concerns. I suspect there’s an empathy element that is missing when you come into data science from a purely computer science/engineering background, but I hope I’m wrong.
Q: What advice would you offer students preparing for a position as a statistician?
A: Get involved in data analysis competitions. As students, you have likely been only exposed to neat data that is meant to cement what you have learned about specific models or statistical methods. Data analysis competitions, such as those on DrivenData and Kaggle, offer you opportunities to engage with (relatively) real world data in an open-ended (rather than structured) way. For instance, if you’ve never done network analysis before, competitions with social network data are a great opportunity to become acquainted with those models on your own, and ask your professors for help if you have questions. To find more competitions, I also recommend checking out KDnuggets. But the biggest advice I can offer is: Don’t talk only with other statisticians. Engage with students and practitioners in other disciplines. Expose yourself to people, topics and issues in computer science, biology, chemistry, environmental science, psychology, history, performance art, visual art (especially graphic design), interactive art, user experience design, public policy, social and political activism, etc. The best statisticians have a broad perspective and understanding of the world around them. If your role will be to offer statistical expertise to different teams in the organization, you will have to become a mini-expert in whatever the teams are working on. You will never not work with people from other backgrounds in this profession, and communicating is a crucial component of the craft.