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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.
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.