Article: A warm washcloth or compress gently held against the bruise can stimulate circulation in the skin around your eye. This can encourage the blood that has collected under your eye to be reabsorbed and may lessen it's dark appearance. Repeat this action several times a day for a few days following the injury. When you lie down, make sure your head is raised up higher than the rest of your body. This position encourages drainage and can help reduce swelling. Sleep with your head propped up on two pillows to keep it elevated. Use a mild soap and water to gently clean any small cuts around your eyes. This will help prevent a bacterial infection, which will elevate your black eye from a bruise to a serious medical situation. Once the area is cleaned, pat with a clean towel and try to keep the injury clean and dry. Signs of infection include a fever, redness, or pus-like drainage.
Question: What is a summary of what this article is about?
Apply moist heat once the swelling has stopped. Keep your head elevated. Clean the area.

Problem: Article: Practicing good hair hygiene can help to prevent the development of dandruff and keep your scalp and hair healthy. Try to wash your hair once a day, especially if you have an oily or irritated scalp. Styling products like hair spray, hair gel, mousses, and hair wax can lead to a build-up of oil on your hair and scalp, leading to the development of dandruff. Cut down on your use of styling products, especially if you already have an oily scalp or are starting to develop dandruff. Studies have shown that sunlight can help to prevent dandruff. But you should always put on SPF or sunscreen on your whole body before you go outside to avoid unhealthy sun exposure. . Stress has been shown to trigger dandruff or make your dandruff worse. Focus on reducing any stress or anxiety you are dealing with at home, at school, or at work. A diet high in zinc, B vitamins, and healthy fats can help to prevent the fungi that causes dandruff.
Summary: Wash your hair regularly. Avoid hairspray and hair gel. Spend more time outdoors in the sun. Practice stress management Maintain a diet high in zinc and B vitamins.

INPUT ARTICLE: Article: If there's any way you can delay taking the test, you'll have a much better shot at passing it. Most drugs are no longer traceable through a blood or saliva test after just a few hours, though some will remain in your bloodstream for up to 3 days or longer. Regardless of what type of drugs you took, your chances will be better if you can delay things for a day or more. If you can't delay a saliva test, you might be able to increase your chances of passing. If you are self-administering the cheek swab for a saliva test, which is supposed to be rubbed between your lower cheek and gum, rub the swab against your teeth instead. Then, rather than hold it between your cheek and gums for two minutes as directed, bite it between your molars. This probably won't work, but it's worth a try if you're on the spot. There's really no way to manipulate the sample for a blood test, since the test is not self-administered. Blood is drawn on the spot and immediately taken to be tested. Since your hair is cut on the spot during a hair test (rather than submitted by you), there's nothing you can really do to tamper with a hair test. However, if you don't have any hair to clip, you might be able to ask for a different type of test that will be easier to pass. If the people drug-testing you have never met you in person and you haven't already agreed to submit a hair sample, shave your head and the rest of your body (especially areas with longer hair) and casually inform them that you don't have any hair to submit. Then ask for a different test. Be sure you have a good story ready as to why your head is shaved. You could say your hair is thinning, or you're experimenting with a new style. Avoid inventing a serious medical condition (eg. cancer) to explain away your hairdo: this might create a lot of long-term complications. Since the hair sample only needs to be an inch long, be forewarned that they might ask for a sample from your leg, underarm, etc. This might be a good time to get an all-over wax and pretend to be a swimmer. Since blood, saliva and hair tests are so very difficult to cheat, you might want to push the issue and see if you can get out of taking it. Here are a few ways to do it: Ask for a urine test. If you think you could pass a urine test because your urine is diluted, or you don't want a blood test because it will show exactly how high you are at the time of the test, see if you can get a urine test instead. Say you consider it less invasive than the other methods. Exercise your rights. In some cases, the person administering the test might not have the right to test you. Get to know drug laws in your state and read up on your employer's drug testing policies. See if there's a loophole that can get you out of taking this test, or delay it to a later time.

SUMMARY: Try to delay a blood or saliva test. Shave your head and body before a hair test. Find a way to get out of taking it entirely.

Numbers are what a data analyst works with every day, so you want to make sure you're comfortable with math. Having a firm understanding of college algebra is important; you should know how to do things such as interpret and graph different functions as well as work through real life word problems. Knowing multivariable calculus and linear algebra will help as well. To become a data analyst, you'll need to be able to interpret data, which is where statistics comes in. Start with a foundation of high school- or college-level statistics, and then move on to more challenging information that might be required for the job. Mean, median, and mode, as well as standard deviation, are examples of the kinds of statistics concepts you would learn in high school or college. Having a strong grasp of both descriptive and inferential statistics will be helpful as well. While you don't need to be an expert at coding or programming to start off as a data analyst, you should be comfortable doing it on a small level. Start by learning how to use programs such as Python, R, and Java first, and then work your way up to others. SQL programming is another that is common among data analysts. You can take courses online to learn coding and programming. Once you've analyzed your data, you'll need to be able to talk about it with others. Work on being able to explain complicated information in a way that makes non-data analysts understand your findings, and practice using programs that illustrate the data in a visually-helpful way. You should be able to communicate data visually as well as verbally. Understand how to use tools such as ggplot and matplotlib to illustrate your findings. You'll be organizing data and calculating numbers as a data analyst, so you need to be comfortable using Excel. There are many video tutorials online, as well as free sites, that will help teach you all you need to know about using Excel to its full potential. Teaching a computer to come up with predictions or decisions on its own after it has studied data, or machine learning, is important when dealing with data analysis. Look online to find courses you can take that will teach you all you need to know about machine learning, and some of them are even free. To understand machine learning, you'll need to have a foundation in programming and statistics. There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. An example of supervised learning is your email filtering your inbox and putting spam in its own folder. Supervised learning would be when Netflix suggests television shows or movies that you might like, and an example of reinforcement learning is a self-driving car and its ability to see and then adapt to its surroundings.
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One-sentence summary --
Master college-level algebra. Understand statistics. Work on your coding and programming abilities to be a more appealing candidate. Develop strong communication and presentation skills. Familiarize yourself with Microsoft Excel. Learn about machine learning.