So you want to be a scientist pdf

Create an account. Edit this Article. We use cookies to make wikiHow great. By using our site, you agree to our cookie policy. Cookie Settings. Learn why people trust wikiHow. Download Article Explore this Article parts. Tips and Warnings. Related Articles. Article Summary. Part 1. Take the necessary preparatory classes in high school. Starting in high school, and continuing into your undergraduate years in college, you should take classes that teach you the analytical and critical thinking skills you will need to be a scientist.

This is a must to get a leg up later in life. You'll need to be well-specialized in mathematics. Scientists in the physical sciences use a great deal of mathematics, particularly algebra, calculus, and analytical geometry, while those in the biological sciences use math less often.

All scientists need a working knowledge of statistics, too. You'll do more intensive projects than you do in your regular science classes in school. Start out with the basics in college. While you will specialize in a particular discipline later, you'll need to take basic courses in biology, chemistry, and physics to ground you in the basics of each science, as well as the scientific method of observing, making hypotheses, and experimenting.

You can also select elective courses based on areas of interest or to discover new areas of interest to help you define your specialty. In a year or two, you can commit to a more specific branch of science. Skills in 1 or 2 foreign languages may be helpful as well. This allows you to read older scientific papers that haven't been translated into English. Additionally, being multilingual will enable you to collaborate with other scientists from across the world, as well as help you pursue research opportunities in other countries.

The most helpful languages to learn include French, German and Russian. Declare a major in a field that intrigues you. If you'd like or if your college's lack of options necessitates it, you can wait to declare something more specific later aka grad school. A general major like chemistry is fine, too.

Get an internship in college. It's best to start making connections and doing work as soon as possible. Contact one of your professors about an internship — you may be able to get your name associated with a paper your team publishes, too.

This will get you applied lab experience, which is going to be helpful for going to grad school and looking for jobs once you graduate. It shows you've been taking college seriously and have a grip on what's expected of you. If you want to be a field researcher, pursue an internship with an environmental organization, such as the U.

Fish and Wildlife Service. Hone your writing skills. You'll also need to write well as a scientist, both to obtain grants for your research and to publish your results in scientific journals.

Classes in English in high school and technical writing in college will help you polish your skills. Read scientific journals and keep up with the field. You'll be in those journals yourself, in time. Look to their work for structure and the basics of a good scientific paper. Part 2. Go to graduate school.

While some commercial and industrial positions are available to college graduates with a bachelor's degree, most scientists have at least a master's and more likely a doctorate.

Graduate programs are geared more toward original research and development of new theories, working with a professor or other scientists, and possibly using cutting-edge technology.

Most graduate programs take at least 4 years, and possibly longer, depending on the nature of the research. At this time, you'll have to declare a specialty — something that greatly narrows down the field and allows you to have a concentration.

This will make your work more unique and your field of competition smaller. Land a research internship just about anywhere. In grad school, you'll need to look for a research internship for your specific area of interest. Your professors and your school, in general, will be very helpful tools in finding which internships exist and where. Tap into all the connections you've made to find something that fits you like a glove. Participate in a post-doctoral program.

Post-doctoral programs provide additional training in whatever specialty you've chosen as a scientist. Originally lasting 2 years, these programs now usually last at least 4 years and possibly longer, depending on the field of study and other factors.

It's common for scientists to go through 4 years of undergrad, around 5 years of higher education, and 3 years of research, which means it'll be a solid 12 years of training. After you complete your undergraduate education, you'll likely be given a stipend or paycheck as you work through the remainder of your training.

Keep your knowledge up-to-date. During your decade and more of education and your career , it's wise to keep up-to-date in your field and related others by attending conferences and reading peer-reviewed journals. Science is constantly changing — in the blink of an eye, you could be left behind. In smaller fields and some larger ones , you'll get to know all the names in these journals.

Reading them will let you know who you should ask for research help or favors when the time comes. Continue researching and seek out full-time employment. Scientists are always working on some project or idea. Regardless of how far up the career ladder you are, this is a given.

But after your post-doctoral research, you'll likely need a job. Here are a few of the basic opportunities you'll find: A science teacher. Her studies are focused on proteins and neurodegenerative diseases. This article has been viewed 90, times. Either way, there are several key steps to becoming a good scientist and cultivating your ability to make positive contributions to the scientific community, and potentially, to the world.

To be a good scientist, don't be afraid to experiment and approach problems from a new angle, which is how a lot of scientific discoveries are made! You should also be very detail-oriented since noticing small details can make a big difference in science.

Also, learn to be open to failure, which will happen all the time when you experiment and test out new hypotheses. If you haven't already, start working on your reading and writing skills since scientists are constantly learning new things and putting their thoughts down on paper. To learn how to put your passion for science to use, keep reading!

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Article Summary. Part 1. Love science and scientific exploration. This is perhaps the most important step, as a love for science will motivate you to study, learn, and develop your ideas with passion and curiosity. If you love science and exploration, you are already one big step closer to becoming a good scientist, as it is always better to be yourself and do work within a niche that you enjoy and find fascinating.

Experiment with new ideas. A significant fraction of scientific discovery is the result of hard work and serendipity, or more bluntly, sheer luck. From the discovery of penicillin by Fleming to the discovery of new ionization techniques such as MALDI, luck has frequently played a large role in scientific discovery. You never know when experimentation and luck will collide to create a significant discovery.

National Institutes of Health Go to source Often big discoveries come from noticing an inconsistency or oddity and then troubleshooting to figure out what caused it.

Instead, consider them and pursue them further to see where the unexpected might lead. Be patient and detail oriented. Almost no scientific discovery just happens or occurs, in fact, as a scientist you need to have the patience to go through years of work, performing experiment after experiment, to prove your theory and verify your results. Categorizing and analyzing data is a huge part of being a scientist, so ensure you can do this efficiently and correctly.

Be open-minded but consider all the facts and hypotheses. A good scientist will accept whatever outcome their work has and not try to force the results of an experiment into a predetermined opinion or theory.

It is also essential that to bear in mind the facts and hypotheses from work done by other scientists as a resource to inform the results of your experiments. National Institutes of Health Go to source A good scientist will have good ethics and will not give false results or shade an experiment to fulfill the expected outcome.

They should be open to the solutions made by others in their field, even when they conflict with their own theories.

Be open to failure. Though you may think a scientist should be brilliant, skilled in mathematics, and incredibly precise, one of the most important skills a good scientist should have is a willingness to fail. Time that is seemingly wasted on a theory that goes nowhere may later prove to be time well spent. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The term 'computational biologist' can encompass several roles, including data analyst, data curator, database developer, statistician, mathematical modeler, bioinformatician, software developer, ontologist—and many more. What's clear is that computers are now essential components of modern biological research, and scientists are being asked to adopt new skills in computational biology and master new terminology Box 1.

Whether you're a student, a professor or somewhere in between, if you increasingly find that computational analysis is important to your research, follow the advice below and start along the road towards becoming a computational biologist!

Key to good computational biology is the selection and use of appropriate software. Before you can usefully interpret the output of a piece of software, you must understand what the software is doing. You wouldn't go into the laboratory and perform a polymerase chain reaction without a basic understanding of the method.

Why would you do the same with a computational analysis? Understanding the underlying methods and algorithms gives you the tools to interpret the results. That doesn't mean you need to read through each line of source code, but you should have a grasp of the concepts. Software tools are often implementations of a particular algorithm that may be well-suited for particular types of data; for example, in de novo assembly, an Overlap-Layout-Consensus assembler is optimized for longer sequence reads, whereas de Bruijn graphs were designed with short reads in mind.

Choosing software employing the most appropriate algorithm will save you a lot of time. Laboratory scientists wouldn't dream of running experiments without the necessary positive and negative controls How do you know your script, software or pipeline is working? Computers will happily output results for the most bizarre of input data, and the absence of an error message is not an indication of success.

Create tests, small datasets for which the answer is known, and check that the software or pipeline can reproduce that answer. Try and do that for every 'type' of answer you expect to find. Double-check the results of everything, to see if those results make sense. Laboratory scientists wouldn't dream of running experiments without the necessary positive and negative controls, and these tests are the computational biology equivalent.

The perfect is the enemy of the good. Remember you are a scientist and the quality of your research is what is important, not how pretty your source code looks.

Perfectly written, extensively documented, elegant code that gets the answer wrong is not as useful as a basic script that gets it right. Having said that, once you're sure your core algorithm works, spend time making it elegant and documenting how to use it.

Use your biological knowledge as much as possible—that's what makes you a computational biologist. Versioning will help you track changes to your code, maintain multiple versions and to work collaboratively with others.

Using a standard tool, such as Git or Subversion, you will also be able to publish your code easily. Be nice to your future self. A few well-placed README files explaining the choices you made and why you made them will be a boon in months or years when you return to a project. Document your code and scripts so that you understand what they do.

When you come to publish your work, try publishing the scripts and methods you used to generate your results so that others can reproduce them.

Also consider keeping a digital laboratory notebook to document your analyses as you perform them. Repositories, such as Github, are ideal for this and also help you maintain copies of the repository to serve as off-site backups Table 1. A pipeline is a series of steps, or software tools, run in sequence according to a predefined plan. Pipelines are great for running exactly the same set of steps in a repetitive fashion, and for sharing protocols with others, but they force you into a rigid way of thinking and can decrease creativity.

Warning: don't pipeline too early. Get a method working before you turn it into a pipeline. And even then, does it need to be a pipeline? Have you saved time?

Is your pipeline really of use to others? If those steps are only ever going to be run by you, then a simple script will suffice and any attempts at pipelining will simply waste time. Similarly, if those steps will only ever be run once, just run them once, document the fact you did so and move on.

Yes you can! As a computational biologist, you will need to be creative, from tweaking existing methods to developing entirely new ones. Be adventurous, be prepared to fail, but keep going.

It's amazing what you can achieve by using Google, by asking other people in the field and by teaching yourself how to solve particular problems.

Attending training courses Table 2 can be useful, but these are only really the start of your learning, not the end. Continue by teaching yourself afterwards.

The following experiment is often performed during statistics training. First, a large matrix of random numbers is created and each column is designated as 'case' or 'control'. A statistical test is then applied to each row to test for significant differences between the case data and the control data. You should not be surprised to learn that hundreds of rows come back with P values indicating statistical significance. Biological datasets, such as those generated by genomics experiments are just like this, large and full of noise.