Avoid These 5 Common Mistakes If You Want To Ace Data Science
Data science has evolved into one of the most lucrative career options over the past few years. It will, therefore, come as no surprise that data scientists are one of the top paid professionals in the industry. In fact, they are hired after a lot of thought and diligence. This sometimes leaves them with very less room for mistakes. On the other hand, this is even applicable for beginners who wish to perfect data science. It is OK to make mistakes when learning data science. However, this should not resonate as one advances their skill set and expertise. In this article, we shall list down five common mistakes that should be avoided by anyone learning and/or working in this trending field. 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. Furthermore, functionalities such as advanced libraries, say, Python’s ggplot2, among many others, do not explicitly tell what goes on in the background when they work. For this reason, it is better to apply what is learnt to experiment with the concepts. This will definitely help beginners keep away from blunders when they begin working on large data science projects. 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. Also, data should fit the business criteria in a given data science project or else it would be redundant all along the project’s entirety. Another factor when being solely dependent on data is collating from many sources without taking ethical or legal factors into account. This can cause trouble, especially if the data is sensitive or confidential. Therefore it is always better to understand data requirements and permissions before using data unnecessarily. 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. Also, knowledge of statistics will help establish relationships between data entities, which can help visualise data efficiently. It is recommended that learners be through with both maths and statistics to fully grasp data science at an intuitive level. 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. For this to happen, a data science enthusiast needs to work with more problems in order to be familiar in ML. All of these take time and it’s okay to learn slowly and digest information rather than skimming and cramming all of the concepts and forgetting them later. 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. They can approach their peers, professionals or even discuss on online forums such as Stack Exchange, Stack Overflow or even GitHub. With time and effort, grasping hard topics becomes very easy. It is therefore suggested that they maintain a schedule every day so that nothing goes amiss in their mastery of the subject. Conclusion: For someone totally new to data science, it can be difficult to get through information and concepts in this field without making errors and mistakes. By avoiding the ones mentioned above, a beginner can gradually be successful in implementing concepts for real-time problems. However, this article is not a comprehensive list of mistakes. In addition, we have not covered technical blunders in the field, which itself is vast. The only aim was to help beginners get over these major ones in their journey to captivate data science.
Data science has evolved into one of the most lucrative career options over the past few years. It will, therefore, come as no surprise that data scientists are one of the top paid professionals in the industry. In fact, they are hired after a lot of thought and diligence. This sometimes leaves them with very […]
It is OK to make mistakes when learning data science. However, this should not resonate as one advances their skill set and expertise.
["applications", "data science learning", "Machine Learning"]
["data science", "machine learning", "AI", "R", "ML", "Machine Learning", "computer vision", "RAG", "Python", "Aim", "deep learning", "data science learning", "applications"]
["AI", "machine learning", "ML", "deep learning", "computer vision", "data science", "Aim", "RAG", "Python", "R"]
I research and cover latest happenings in data science. My fervent interests are in latest technology and humor/comedy (an odd combination!). When I'm not busy reading on these subjects, you'll find me watching movies or playing badminton.