5 Common Mistakes to Avoid to Excel in Data Science

If you are on the lookout for the interesting job of the century or are just looking for a high paying job that will not be replaced by robots anytime in the near future, a career in data science is an excellent option. Data science has been termed as the best job in America for the last four years. Data scientists are skilled in machine learning, coding languages, algorithms, big data analysis, and problem assessment in exchange for which they enjoy high job demand and lucrative salaries.


Big data experts and data scientists are among the most in-demand tech professionals, but to be successful in this field and make their mark in the data science domain, they would need to avoid certain common pitfalls. We will discuss that in this article.


Understanding Data Science


Data science refers to a blend of various algorithms, tools, and machine learning principles that operate with the goal of discovering hidden patterns from raw data. It is used to make decisions and predictions by using prescriptive analysis, predictive causal analysis, and machine learning. It is used to scope out the right questions from the dataset. It is a multidisciplinary field that works at the raw level of data (structured, unstructured, or both) to make predictions, identify patterns and trends, build data models, and create more efficient machine learning algorithms. Data science experts work in the realm of the unknown. Some of the data science techniques are regression analysis, classification analysis, clustering analysis, association analysis, and anomaly detection.


5 Common Mistakes Newbies and Experienced Professionals Must Avoid


Let us now analyze the mistakes to be avoided by a data science professional.

1. Do Not Complicate Your Resume


You may have learned plenty of tools and skills. But be sure to keep your resume short and simple. Listing out all your skills in bulk will not help. Do not add additional technical and complex terms in your resume if it does not reflect your actual skills at large. Instead, describing a situation and explaining how you solved it using your problem-solving skills will be a better approach to follow. Companies today are more interested in knowing the hows of the problems you have faced till now rather than the whats. Companies always prefer to learn about your knowledge and achievements in a neat and precise manner. Instead of just listing out the tools and libraries you have worked with, provide a detailed description of how you used them.

2. Miscalculating the Time Involved


Becoming a data science expert involves putting in a lot of time and hard work. The amount of hard work and time differs from one individual to the other. For example, if you hold several degrees in Mathematics or many years of experience in software development and information technology, you will be able to speed up the learning process. But in reality, regardless of relevant experience, you will have to spend several hours of your day job to understand the ins and outs of data science. This can be a daunting task.

To adequately measure the work or time commitment, you will need a strategic view of data science and the constituent components constituting data science such as machine learning, big data, visualizations, Excel/R/Python, etc. Be sure to do your research on the time it takes to master each area of data science. This way, you can understand how big of a time commitment data science is and reach your goal of becoming a big data expert.

3. Limiting Experience to Auditing Online Courses


This is a major mistake unless you verify and validate that you actually took an online course and absorbed the content. At some point, you will have to prove that you made this commitment. Basically, there is nothing wrong with auditing online courses. You will be able to learn quite a lot from that experience. At a time when you need to prove that you possess the right skillsets and knowledge, it will be highly difficult to convince people that you have learned the material and done the work. A certification eliminates this potential roadblock by conveying to the employers that you are highly familiar with the concepts and language and that you are ready for a commitment.


So, make sure that the certificate course you choose covers the concepts such as programming, basic statistics, introductory machine learning, exploratory data analysis, visualizations, R or Python, etc. A course covering these basics will help you learn data science in detail. This way, you can plan out a strategy and timeline.


At this stage, you will know more information to decide if you are really interested in committing to this process. You will have the chance to plan and come up with a strategy to achieve the hard-fought goal of becoming a data scientist. If you have the passion to become a data science expert, it is certainly worth it.

4. Underestimating the Commitment Needed


Anyone who has become an expert understands the commitment needed to achieve almost anything. Data science also falls under the same category. It requires attention to detail, topical knowledge, and commitment. You will need to be aware of data visualization, inferential statistics, descriptive statistics, and handling data sets. You will need analytical thinking and a lot of patience. You will need to understand Excel, machine learning, relational databases, SQL, etc. To gain realistic expectations for the work involved, you will have to extract resources from everywhere. This can be videos, blogs, books, podcasts, and data science courses. You will have to spend some time to determine the total outlay of time and effort to become a data scientist.


5. Lacking a Plan


Total lack of planning is one of the common mistakes made by aspiring data scientists. The best way to evaluate if you are going in the right direction and making progress is to measure yourself against a strategy. You can use any preferred method to lay out your goals, such as a piece of paper, a Google Doc, or a spreadsheet. You can also add dates and milestones to this.


Let us now consider a simple example of an annual plan for two months.


a. January- This can include brushing up on statistics, installing and starting Python or R, watching videos on Python or R, learning the packages, libraries, data structures, and data types of Python or R, and making a commitment to use Python or R every day.

b. February– This may include reading a data science book and listening to data science podcasts, learning statistics at a holistic level (descriptive, predictive, inferential statistics), learning various statistical tests, and continuing to use Python or R every day.


This way, you can compose for several months and carry out the tasks and objectives outlined in the plan. Revisit the plan bi-weekly to analyze how well you are staying on target. Though you may sometimes fall behind or deviate from your plan, realign and recompose if necessary and continue to work towards your goals.




At some point in time, we all would have heard the phrase, ‘a penny saved is a penny earned.’ Going by the same logic, avoiding a mistake is a step in the right direction. There are two ways to improve any skill. One is learning what to do, and the other one is learning what not to do. If you wish to get really good at a skill, it is said that you may probably want to do both. To learn more about data science certifications and become a big data expert, check out Global Tech Council.