#Avoid these 5 common mistakes if you want to ace data science....
👉Mistake #1: More Learning, Less Application
One of the most common mistakes data science newbies make is to learn a lot of concepts without thinking much about their applications. Simply understanding them isn’t enough. For example, if a data science beginner learns an algorithm, it is crucial to know its real-world applications, limitations and application for solving a particular problem. Theoretical learning is only useful if it is applied practically. 👉Mistake #2: Relying Only On Data
This mistake is made by numerous data science enthusiasts after learning. The focus is only on data rather than the problem it aims to solve. Data itself cannot solely be the solution — it can only be made useful when the data science expertise and knowledge are combined. 👉Mistake #3: Ignoring Maths And Statistics
Since data science requires a comprehensive analysis of data and deals with fact and figures, it is essential that a beginner knows a certain amount of mathematics and statistics. Linear algebra and calculus are fundamental to understand concepts in areas like machine learning and deep learning. In fact, with maths, it is easier to perceive how data science concepts work. 👉Mistake #4: Trying To Learn Everything In Haste
With data science swiftly gaining momentum over the past few years, everyone wants to master the subject hurriedly without giving space between what has been learnt. Basics and advanced topics are learnt at the same pace without being proficient in the former. For example, consider a complex area such as natural language processing or computer vision. Before delving into these areas, the beginner should have a strong command of ML fundamentals. 👉Mistake #5: Inconsistent Learning
Last but not the least is learning data science in an inconsistent manner. Learning should be continuous and at a professional level. Beginners should not get discouraged if certain topics get too complex and leave midway in their learning process.
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