Data science is becoming an increasingly important field today. If you understand its principles, you can extract knowledge and analyze data, which helps you innovate processes and applications and produce unique business solutions. However, the field can be intimidating, especially if you’re a beginner. Here are some actionable tips for newcomers to data science.
Seek Out Experts in the Field
There are various ways you can learn data science—you can watch videos, participate in meet-ups, read articles, and even enroll in online courses. However, one thing you cannot get from these sources is experience. You must go beyond theory and have practical knowledge of data science.
Besides reading about the field, you can listen to practitioners, people who have managed machine learning or deep learning projects. Kenneth Cukier, Bernard Marr, Dean Abbott, and John Elder are some data experts that beginners should follow.
On a related note, you cannot stay purely in the realm of theory if you want to be a data science professional. You need to practice what you learn, which is why you should enroll in courses or participate in hackathons for data science.
Learn How to Tell a Story Using Data
Great stories have a beginning, middle, and end—they give structure to knowledge and enable you to create value judgments. In contrast, data—pure information—is messy. Data is just numbers and letters until a data scientist can extract analyses from it.
If you want to become a data scientist, you have to learn how to tell a story using statistics and programming. Data helps you see patterns and prove theories about your organization, which will help you produce better decisions.
You can start by asking what data is the most important to the company and highlighting those only. As you spend more time working with information, you will learn what matters and what is extraneous.
Learn to Appreciate Coding
As an entry-level data scientist, you will have to do a lot of storytelling—you should know how to code or put messy data in a form that your managers and higher-ups can analyze. You will also need to use different skills with each data set, so learning to love all aspects of coding is a must. It’ll be your primary method of communication at work—when you are proficient at creating and cleaning code, you will develop your capacity for abstraction and creativity.
Learn Statistics and Programming
Data science, essentially, is the intersection of programming and statistics. A common saying among professionals in this field is that a data scientist knows more statistics than a programmer but more programming than a statistician.
If you are a programmer venturing into data science, you need to study statistics. There are plenty of short courses and programs that can help you learn the basics of collecting and interpreting data.
Conversely, if you are a statistician, you need to learn how to use a programming language for data visualization. Newcomers to programming would benefit from learning Python—it is arguable the most popular language in data science. Python has an active community and is easy to learn because it has tons of resources available online.
Start with the basics of Python, then move on to data visualization. Then, you can pursue machine learning—Matplotlib, Pandas, and Scikit-learn are some popular machine learning libraries that use this language.
Data science is a rewarding field to be in, but it is also challenging to study. If you want to become a data scientist, you need to be comfortable working with numbers, uncovering the stories they tell, and presenting these in ways that are intelligible to both practitioners and C-suite executives. Real-world experience, like what you get in hackathons and internships, also helps prepare you for the work you must do in the field.
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