Data science is a popular area of expertise in Computer Science and Statistics. It also provides many opportunities for those eager to take it up as a profession in the future.
In this article, we’ll talk about the various courses and subjects college students need to study and have a good grasp of if they want to get into data science.
Mathematics acts as the basic building block for your data science foundation. This subject is integral for anyone pursuing data science academically or professionally. To be more specific, you need to have a very good understanding of basic and advanced Algebra to become a professional data scientist.
Mathematical knowledge helps you form equations that simplify the data in your hand in a more computer-readable form. The equations you form will also help you visualize the graphs your data might form when arranged in specific manners. This is vital as visual knowledge is vital in data science, mostly when working with infographics.
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Those eager to pursue data science must excel in Statistics. STEM students who go on to study engineering or mathematics are almost always asked to master their statistical skills. This is because Statistics, especially the knowledge of probability, is something they must fully understand to solve complex problems related to data science.
Simply knowing statistics won’t do. You must also solve various practice problems from your Statistics course in college. You can check out the Statistics section on Proprep’s STEM practice for college students for better preparation. There’s also a separate section on this platform for learning with probability. You can study the lessons available here to hone your statistical skills.
If you’re looking forward to a career in data science or learning it for fun, you can’t make it happen without learning Python.
While other programming languages work just as fine, Python is comparatively easier to learn. It’s also efficient in the sense that you can do a lot of things with just a few lines of code. Besides, Python has become the unofficial global standard for coding practices in data science.
It’s always a good idea to start learning Python from the basics, even if you have prior knowledge. Once you clear the basic and intermediate stages of Python learning, you can move on to something more specific for data science.
Start by learning about the various Python libraries that can help you solve problems related to data science. TensorFlow, NumPy, SciPy, Pandas, Matplotlib, Keras, and SciKit-Learn are some of the most popular libraries you should work with. You can also look into coding challenges that utilize these libraries or let you solve data science problems in Python.
Remember that the more you code, the better you’ll get at Python. You can also maximize this opportunity and try to get into more theoretical aspects of Python and programming.
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Algorithms and Data Structures
Learning to code will no doubt help you with your pursuit of data science knowledge. However, to truly become a master of it, you must also hone your skills in algorithms and data structures.
Start by identifying yourself with the various data structures used in programming, including arrays, stacks, queues, trees, and linked lists. Learn how they work and when you should be using them. At the same time, you should also learn the various pros and cons that come with each of these data structures.
Then it would be best if you dived into algorithms. Algorithms are programming outlines that showcase how a program works by highlighting to you the main parts. The better the algorithm, the more efficient your results will be. Practice with existing established algorithms that have shown proven results so that you can learn how the algorithms are working to deliver the results.
Understanding databases and database systems will help you work with data directly from various databases. You can select the data and then utilize it with your algorithm to produce various results and graphs.
Learning about the functionalities of databases can help you implement your data science knowledge in the real world. This is the ultimate goal of this area of expertise, as professional data scientists must always work with real-life scenarios and real-world data.
That’s all for this article. Start your journey into data science with the subjects and courses mentioned above. As you further dive into this field, you’ll come across many other advanced topics you might have to learn to keep getting better in data science.